Our research on pervasive sensing focuses around the concept of Computational Ethnography which borrows the in-depth understanding of activity and people’s behavior from ethnography and anthropology, and combine it with the potential of capturing large sets of rich multimodal data from environmental and body-worn sensors and technology. Through the use of a variety of tracking technology (body-tracking, eye-tracking, social media, etc.) we study human behavior in context and the interaction of individuals with technology.

To support pervasive sensing we developed Lab-in-a-Box — a novel multimodal data collection infrastructure based on eye, body, gesture, voice and activity tracking — and we enhanced our analysis and visualization tools to better understand large amount of collected data. This includes the extension and integration of ChronoViz, our analysis tool for multimodal time-synchronized data, as well as novel web-based visualization and data analysis tools.

Our tools and techniques are used for in-depth study of activity in the wild, for instance we characterize interaction with Electronic Medical Records and understand patient-physician communication, to study sign language, to understand the ergonomics of laparoscopic and robotic surgery, to uncover multimodal cues of neurological disorders such as stroke, and to exploit social interactions online as a support for real-world intervention in the context of HIV/AIDS.

Ubiscope

Ubiscope, the Ubiquitous Computing Microscope is a new kind of microscope for understanding people through the unobtrusive and pervasive sensing of physiological and behavioral data. As the microscope enabled uncovering new understanding and supported key scientific advances, UbiScope acts as a similar proxy for understanding and supporting human activity.

  • UbiStroke: Multimodal Computational Assessment of Stroke
  • ErgoKinect: Detection of Ergonomically Incorrect Posture during Laparoscopic Surgery
  • Effect of Packaging on Smoking Perception and Behavior: a Randomized Control Trial
Funding

Stroke-Kinect: A Sensor-Based Approach Towards Creating a Multimodal Stroke Signature
(FISP 3-G3133/2017,  UCSD Frontiers of Innovation Scholars Program)

Effect of Packaging on Smoking Perception and Behavior: a Randomized Control Trial
(NIH R01, National Cancer Institute)

SL-CN: Learning to Move and Moving to Learn
(National Science Foundation, Office Of Multidisciplinary Activities, Sept 2016 – Aug 2018)

Publications

  • S. Rick, V. Ramesh, D. Gasques Rodrigues, and N. Weibel, “Pervasive Sensing in Healthcare: From Observing and Collecting to Seeing and Understanding,” in In Proc. of WISH, Workshop on Interactive System for Healthcare, CHI 2017, 2017.
    [Abstract] [Bibtex]

    From analyzing complex socio-technical systems, to evaluating novel interactions, increasingly pervasive sensing technologies provide researchers with new ways to observe the world. This paradigm shift is enabling capture of richer and more diverse data, combining elements from in-depth study of activity and behavior with modern sensors, and providing the means to accelerate sense-making of complex behavioral data. At the same time novel multimodal signal processing and machine learning techniques are equipping us with ‘super powers’ that enable understanding of these data in real-time, opening up new opportunities for embracing the concept of ‘Data Science in the Wild’. In this paper we present what this transition means in the context of Health and Healthcare, focusing on how it leads to the ‘UbiScope’, a ubiquitous computing microscope for detecting particular health conditions in real-time, promoting reflection on care, and guiding medical practices. Just as the microscope supported key scientific advances, the UbiScope will act as a proxy for understanding and supporting human activity and inform specific interventions in the years ahead.

    @inproceedings{wish2017,
     abstract = {From analyzing complex socio-technical systems, to evaluating novel interactions, increasingly pervasive sensing technologies provide researchers with new ways to observe the world. This paradigm shift is enabling capture of richer and more diverse data, combining elements from in-depth study of activity and behavior with modern sensors, and providing the means to accelerate sense-making of complex behavioral data. At the same time novel multimodal signal processing and machine learning techniques are equipping us with 'super powers' that enable understanding of these data in real-time, opening up new opportunities for embracing the concept of 'Data Science in the Wild'. In this paper we present what this transition means in the context of Health and Healthcare, focusing on how it leads to the 'UbiScope', a ubiquitous computing microscope for detecting particular health conditions in real-time, promoting reflection on care, and guiding medical practices. Just as the microscope supported key scientific advances, the UbiScope will act as a proxy for understanding and supporting human activity and inform specific interventions in the years ahead.},
     area = {pervasive_sensing},
     author = {Rick, Steven and Ramesh, Vish and Gasques Rodrigues, Danilo and Weibel, Nadir},
     booktitle = {In Proc. of WISH, Workshop on Interactive System for Healthcare, CHI 2017},
     interhash = {14a61e317efb0273cf9b9387910c3c2a},
     intrahash = {ac36ea34e7460fb52fac2f8004823299},
     projects = {ubiscope},
     title = {Pervasive Sensing in Healthcare: From Observing and Collecting to Seeing and Understanding},
     year = 2017 
    }
  • V. Ramesh, S. Rick, B. Meyer, G. Cauwenberghs, and N. Weibel, “A Neurobehavioral Evaluation System Using 3D Depth Tracking & Computer Vision: The Case of Stroke-Kinect.,” in Proceedings of Neuroscience 2016, Annual Meeting of the Society for Neuroscience (Poster presentation), San Diego, CA, USA, 2016.
    [Abstract] [Bibtex]

    Due to the subtlety of their symptoms – slight tremors, blurred vision, and loss of mobility, for example – many neurological diseases are challenging to diagnose. As such, a computational tool that can identify and analyze these symptoms accurately will be of immense use to neurologists. We aim to characterize human motor and cognitive abilities through a multimodal approach that will lead to signatures for neurological disorders, based on patterns in relevant identifiers. We focus here on stroke. Stroke is the 4th leading cause of death and the leading cause of disability in the United States. But Recombinant Tissue Plasminogen Activator (rt-PA), the only FDA-approved treatment currently available, is administered in less than 5% of acute stroke cases. The decision to prescribe rt-PA is based on the National Institute of Health Stroke Scale (NIHSS), a combination of multiple tests conducted by a neurologist to assess visual fields and motor and sensory impairments. Stroke evaluation with the NIHSS is inherently subjective. An inexperienced evaluator may miss key or almost imperceptible tells, misdiagnose the severity of a stroke, forego rt-PA prescriptions, and crudely predict long term outcomes. If this gap in objective and reliable stroke diagnosis is not addressed, stroke survivors will endure an arduous rehabilitation process. We are therefore developing Stroke-Kinect, a new system for automatic eye motion and body motion analysis to assist in the diagnosis of stroke. We obtain high-definition images and the spatial and temporal positions of 25 body joints in stroke and healthy control patients with the Microsoft Kinect v2. We employ machine learning classification algorithms and computer vision techniques to replicate the subjective NIHSS test computationally. Furthermore, we develop new tests for identifiers not captured by the NIHSS that are difficult to detect by the human eye: joint angles and thus body posture, velocity of gestures, twitches and jerks, and center of mass. Our analysis of depth data collected from stroke patients indicates accurate testing for the synchronicity of movements and reliable eye gaze tracking. The data also identifies posture as a key indicator of left side versus right side weakness. These results suggest that larger data sets will permit identification of only the vital indicators in stroke diagnosis, to simplify the NIHSS and mitigate the risk of false negatives and erroneous prescriptions of rt-PA. Stroke-Kinect also paves the way for the computational diagnosis of other neurological disorders, furthering the health sciences and ultimately aiding patients in their recovery.

    @inproceedings{sfn2016_ramesh,
     abstract = {Due to the subtlety of their symptoms - slight tremors, blurred vision, and loss of mobility, for example - many neurological diseases are challenging to diagnose. As such, a computational tool that can identify and analyze these symptoms accurately will be of immense use to neurologists. We aim to characterize human motor and cognitive abilities through a multimodal approach that will lead to signatures for neurological disorders, based on patterns in relevant identifiers. We focus here on stroke. Stroke is the 4th leading cause of death and the leading cause of disability in the United States. But Recombinant Tissue Plasminogen Activator (rt-PA), the only FDA-approved treatment currently available, is administered in less than 5% of acute stroke cases. The decision to prescribe rt-PA is based on the National Institute of Health Stroke Scale (NIHSS), a combination of multiple tests conducted by a neurologist to assess visual fields and motor and sensory impairments. Stroke evaluation with the NIHSS is inherently subjective. An inexperienced evaluator may miss key or almost imperceptible tells, misdiagnose the severity of a stroke, forego rt-PA prescriptions, and crudely predict long term outcomes. If this gap in objective and reliable stroke diagnosis is not addressed, stroke survivors will endure an arduous rehabilitation process. We are therefore developing Stroke-Kinect, a new system for automatic eye motion and body motion analysis to assist in the diagnosis of stroke. We obtain high-definition images and the spatial and temporal positions of 25 body joints in stroke and healthy control patients with the Microsoft Kinect v2. We employ machine learning classification algorithms and computer vision techniques to replicate the subjective NIHSS test computationally. Furthermore, we develop new tests for identifiers not captured by the NIHSS that are difficult to detect by the human eye: joint angles and thus body posture, velocity of gestures, twitches and jerks, and center of mass. Our analysis of depth data collected from stroke patients indicates accurate testing for the synchronicity of movements and reliable eye gaze tracking. The data also identifies posture as a key indicator of left side versus right side weakness. These results suggest that larger data sets will permit identification of only the vital indicators in stroke diagnosis, to simplify the NIHSS and mitigate the risk of false negatives and erroneous prescriptions of rt-PA. Stroke-Kinect also paves the way for the computational diagnosis of other neurological disorders, furthering the health sciences and ultimately aiding patients in their recovery.},
     address = {San Diego, CA, USA},
     area = {pervasive_sensing},
     author = {Ramesh, Vish and Rick, Steven and Meyer, Brett and Cauwenberghs, Gert and Weibel, Nadir},
     booktitle = {Proceedings of Neuroscience 2016, Annual Meeting of the Society for Neuroscience (Poster presentation)},
     interhash = {ae008fd247b1d2f695a9ced3b4c3bc47},
     intrahash = {d363538113a55afe4c39d0a77ef605e5},
     month = nov, projects = {ubiscope},
     title = {{A Neurobehavioral Evaluation System Using 3D Depth Tracking & Computer Vision: The Case of Stroke-Kinect.}},
     year = 2016 
    }

Computational Ethnography

The goal of this project is to accelerate observational analysis by employing multi-modal pattern recognition capabilities to pre-segment and tag records and increase analysis power by collecting multimodal activity and  enable the investigation of macro-micro relationships of people’s interaction sin the wild, among themselves and with technology.

  • QUICK: Quantifying Electronic Medical Record Usability to Improve Clinical Workflow
  • Hands That Speak: Studying Complex Human Communicative Body Movements
Funding

A Multiscale Framework for Analyzing Activity Dynamics (US NSF IIS-0729013).
October 2009 – August 2011
Flight Crew Performance Data Collection and Analysis Tool (UCSD – Boeing Project Agreement 2011-012 / Boeing Project Agreement 2012)
January 2011 – September 2012
QUICK – Quantifying Electronic Medical Record Usability to Improve Clinical Workflow. (AHRQ R01 – HS 021290)
September 2012 – August 2016.

Publications

  • [PDF] N. Weibel, S. Hwang, S. Rick, E. Sayyari, D. Lenzen, and J. Hollan, “Hands that Speak: An Integrated Approach to Studying Complex Human Communicative Body Movements,” in Proceedings of HICSS-49, Hawaii International Conference on System Sciences, Kauai, HI, USA, 2016.
    [Bibtex]
    @inproceedings{weibel2016hands,
     address = {Kauai, HI, USA},
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Hwang, So-One and Rick, Steven and Sayyari, Erfan and Lenzen, Dan and Hollan, Jim},
     booktitle = {Proceedings of {HICSS}-49, {Hawaii} {International} {Conference} on {System} {Sciences}},
     interhash = {60967491ae65a5a15830ddd630b9bf15},
     intrahash = {57ffb6523faacfb33f10d70862f517fe},
     month = jan, note = {In Press},
     projects = {gestures, sign-language,computational_ethnography},
     title = {Hands that {Speak}: {An} {Integrated} {Approach} to {Studying} {Complex} {Human} {Communicative} {Body} {Movements}},
     year = 2016 
    }
  • [PDF] S. Rick, A. Calvitti, Z. Agha, and N. Weibel, “Eyes on the Clinic: Accelerating Meaningful Interface Analysis through Unobtrusive Eye Tracking,” in Proceedings of PervasiveHealth 2015, International Conference on Pervasive Computing Technologies for Healthcare, Istanbul, Turkey, 2015.
    [Bibtex]
    @inproceedings{rick2015clinic,
     address = {Istanbul, Turkey},
     area = {pervasive_sensing},
     author = {Rick, Steven and Calvitti, Alan and Agha, Zia and Weibel, Nadir},
     booktitle = {Proceedings of {PervasiveHealth} 2015, {International} {Conference} on {Pervasive} {Computing} {Technologies} for {Healthcare}},
     interhash = {62adea43383b70c3b9ae5dc2bb995bab},
     intrahash = {8f3a3a660d7a9e74258c7178faac8d62},
     month = may, note = {In Press},
     projects = {quick, medical_informatics, computational_ethnography},
     title = {Eyes on the {Clinic}: {Accelerating} {Meaningful} {Interface} {Analysis} through {Unobtrusive} {Eye} {Tracking}},
     year = 2015 
    }
  • [PDF] K. Zheng, D. Hanauer, Z. Agha, and N. Weibel, “Computational Ethnography: Automated and Unobtrusive Means for Collecting Data in situ for Human-Computer Interaction Studies,” in Cognitive Informatics in Health and Biomedicine: Human Computer Interaction in Healthcare, V. L. Patel, T. G. Kannampallil, and D. Kaufman, Eds., Springer, 2015.
    [Bibtex]
    @incollection{zheng2015computational,
     area = {pervasive_sensing},
     author = {Zheng, Kai and Hanauer, David and Agha, Zia and Weibel, Nadir},
     booktitle = {Cognitive {Informatics} in {Health} and {Biomedicine}: {Human} {Computer} {Interaction} in {Healthcare}},
     editor = {Patel, Vimla L. and Kannampallil, Thomas G. and Kaufman, David},
     interhash = {e3273a755240be319b05d3d052dcab02},
     intrahash = {8949ae5ded1b43502b1a6b0a8432e15a},
     month = jan, note = {ISBN 978-3-319-17271-2},
     projects = {quick, patient-physician-communication,computational_ethnography},
     publisher = {Springer},
     title = {Computational {Ethnography}: {Automated} and {Unobtrusive} {Means} for {Collecting} {Data} in situ for {Human}-{Computer} {Interaction} {Studies}},
     year = 2015 
    }
  • [PDF] N. Weibel, S. Rick, C. Emmenegger, S. Ashfaq, A. Calvitti, and Z. Agha, “LAB-IN-A-BOX: Semi-Automatic Tracking of Activity in the Medical Office,” Pers Ubiquit Comput – Health, 2014.
    [Bibtex]
    @article{weibel2014labinabox,
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Rick, Steven and Emmenegger, Colleen and Ashfaq, Shazia and Calvitti, Alan and Agha, Zia},
     interhash = {dcecbcd424ffc9f5e032165284e4f13a},
     intrahash = {5da8ceba93930c415bdf7775db44bbff},
     journal = {Pers Ubiquit Comput - Health},
     month = sep, projects = {quick, medical_informatics, stroke-kinect, ergokinect, gestures, kinect, computational_ethnography},
     title = {{LAB}-{IN}-{A}-{BOX}: {Semi}-{Automatic} {Tracking} of {Activity} in the {Medical} {Office}},
     year = 2014 
    }
  • [URL] A. Calvitti, N. Weibel, H. Hochheiser, L. Liu, K. Zheng, C. Weir, S. Ashfaq, S. Rick, Z. Agha, and B. Gray, “Can eye tracking and EHR mouse activity tell us when clinicians are overloaded?,” Human Factors Quarterly, Veteran Health Administration, 2014.
    [Bibtex]
    @article{calvitti2014tracking,
     area = {pervasive_sensing},
     author = {Calvitti, Alan and Weibel, Nadir and Hochheiser, Harry and Liu, Lin and Zheng, Kai and Weir, Charlene and Ashfaq, Shazia and Rick, Steven and Agha, Zia and Gray, Barbara},
     interhash = {63723b1b64d631168cf02d4c337bb8c0},
     intrahash = {4395dd0ad3ea36df93a1bcfc83ea5413},
     journal = {Human Factors Quarterly, Veteran Health Administration},
     month = sep, projects = {quick, medical_informatics,computational_ethnography},
     title = {Can eye tracking and {EHR} mouse activity tell us when clinicians are overloaded?},
     url = {https://content.govdelivery.com/accounts/USVHA/bulletins/cfd5d2#article4},
     year = 2014 
    }
  • [PDF] N. Weibel, S. Ashfaq, A. Calvitti, J. D. Hollan, and Z. Agha, “Multimodal Data Analysis and Visualization to Study Usability of Electronic Health Records,” in Proceedings of PervasiveHealth 2013, International Conference on Pervasive Computing Technologies for Healthcare (Poster Track), Venice, Italy, 2013, pp. 282-283.
    [Bibtex]
    @inproceedings{weibel2013multimodal,
     address = {Venice, Italy},
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Ashfaq, Shazia and Calvitti, Alan and Hollan, James D. and Agha, Zia},
     booktitle = {Proceedings of {PervasiveHealth} 2013, {International} {Conference} on {Pervasive} {Computing} {Technologies} for {Healthcare} ({Poster} {Track})},
     interhash = {ed3d21b7605e575861886eb709ac8b45},
     intrahash = {450e0fa1576bbef68ac5c693b74338e1},
     month = may, pages = {282--283},
     projects = {quick, medical_informatics,computational_ethnography},
     title = {Multimodal {Data} {Analysis} and {Visualization} to {Study} {Usability} of {Electronic} {Health} {Records}},
     year = 2013 
    }

Connected and Open Research Ethics

The employment of pervasive sensing technology opened up the way for a critical consideration of research ethics. This is especially important with regards to the collection of behavioral, social, and personal data in clinical or health-related settings. To study these important ethical dilemmas, we created the CORE (Connected and Open Research Ethics) initiative and are actively investigating how to redefine research ethics.

Funding

RWJF CORE – Designing, building, and testing a Web-based prototype to foster the ethical design and review of health research
(RWJF Pilot Pioneer, November 2015 – October 2017)

Publications

  • N. Weibel, P. Desai, L. Saul, A. Gupta, and S. Little, “HIV Risk on Twitter: the Ethical Dimension of Social Media Evidence-based Prevention for Vulnerable Populations,” in Proceedings of HICSS-50, Hawaii International Conference on System Sciences, Big Island, HI, USA, 2017.
    [Abstract] [Bibtex]

    As of 2016 the HIV/AIDS epidemics is still a key public health problem. Recent reports showed that alarmingly high numbers of people in vulnerable populations are not reached by preventative efforts. Despite technology improvement, we are not yet able to identify populations that are most susceptible to HIV infections. In order to enable evidence-based prevention, we are studying new methods to identify HIV at-risk populations exploiting Twitter posts as indicators of HIV risk. Our research on social network analysis and machine learning outlined the feasibility of using tweets as monitoring tool for HIV-related risk at the demographic, geographical, and social network level. However, this approach highlights ethical dilemmas in three different areas: data collection and analysis, risk inference through imperfect probabilistic approaches, and data-driven prevention. We contribute a description, analysis and discussion of ethics based on our 2-year experience with clinicians, IRBs, and local HIV communities in San Diego, California.

    @inproceedings{weibel2017twitter,
     abstract = {As of 2016 the HIV/AIDS epidemics is still a key public health problem. Recent reports showed that alarmingly high numbers of people in vulnerable populations are not reached by preventative efforts. Despite technology improvement, we are not yet able to identify populations that are most susceptible to HIV infections. In order to enable evidence-based prevention, we are studying new methods to identify HIV at-risk populations exploiting Twitter posts as indicators of HIV risk. Our research on social network analysis and machine learning outlined the feasibility of using tweets as monitoring tool for HIV-related risk at the demographic, geographical, and social network level. However, this approach highlights ethical dilemmas in three different areas: data collection and analysis, risk inference through imperfect probabilistic approaches, and data-driven prevention. We contribute a description, analysis and discussion of ethics based on our 2-year experience with clinicians, IRBs, and local HIV communities in San Diego, California.},
     address = {Big Island, HI, USA},
     area = {data_analysis, pervasive_sensing},
     author = {Weibel, Nadir and Desai, Purvi and Saul, Lawrence and Gupta, Amarnath and Little, Susan},
     booktitle = {Proceedings of {HICSS}-50, {Hawaii} {International} {Conference} on {System} {Sciences}},
     interhash = {186ce1d8dba35a1f1286736a41eab680},
     intrahash = {e1a470aa4f364b7ed5a3ffb7572f6741},
     month = jan, note = {In Press},
     projects = {pircnet, core},
     title = {{HIV} {Risk} on {Twitter}: the {Ethical} {Dimension} of {Social} {Media} {Evidence}-based {Prevention} for {Vulnerable} {Populations}},
     year = 2017 
    }
  • C. Bloss, C. Nebeker, M. Bietz, D. Bae, B. Bigby, M. Devereaux, J. Fowler, A. Waldo, N. Weibel, K. Patrick, and others, “Reimagining Human Research Protections for 21st Century Science,” Journal of Medical Internet Research (JMIR), vol. 18, iss. 12, p. e329, 2017.
    [Bibtex]
    @article{bloss2016reimagining,
     area = {pervasive_sensing},
     author = {Bloss, Cinnamon and Nebeker, Camille and Bietz, Matthew and Bae, Deborah and Bigby, Barbara and Devereaux, Mary and Fowler, James and Waldo, Ann and Weibel, Nadir and Patrick, Kevin and others},
     interhash = {4aa43c331dfae18e3b724c783e0ca60c},
     intrahash = {f763583b48011e729cc5b7de4fb25091},
     journal = {Journal of Medical Internet Research (JMIR)},
     number = 12, pages = {e329},
     projects = {core},
     publisher = {JMIR Publications Inc., Toronto, Canada},
     title = {Reimagining Human Research Protections for 21st Century Science},
     volume = 18, year = 2017 
    }
  • S. Dunseath, N. Weibel, C. Bloss, and C. Nebeker, “NIH support of Mobile, Imaging, pervasive Sensing, Social media and location Tracking (MISST) research: Laying the foundation to examine research ethics in the digital age,” Nature: Digital Medicine, 2017.
    [Bibtex]
    @article{dunseath2017,
     area = {pervasive_sensing},
     author = {Dunseath, Sarah and Weibel, Nadir and Bloss, Cinnamon and Nebeker, Camille},
     interhash = {4bf0b7eed1ad0e380eb7d4bd88afd75c},
     intrahash = {46fb255f71f000385574cda590c8a69b},
     journal = {{Nature: Digital Medicine}},
     projects = {core},
     title = {{NIH} support of {Mobile, Imaging, pervasive Sensing, Social media and location Tracking (MISST)} research: Laying the foundation to examine research ethics in the digital age},
     year = 2017 
    }

Social Media as a Sensor

We live in an era where much of our lives are represented online through a variety of social media such as Facebook, Twitter, Instagram, etc. The intense communication that happens across friends and acquaintances and the networks that are created around those exchanges of textual and multimedia messages create incredible opportunities to better understand a variety of behavioral clue that might help resolve health and healthcare related problems. We are using a mixed approach based on natural language processing and network analysis to capitalize on these issues to help drive intervention, specifically in the setting of HIV and Physical Activity.

  • PIRC-Net: Characterizing HIV at-risk
  • SMART: Using Social Media and Mobile Technologies to Promote Improved Health Behaviors
Funding

PIRC-Net: PIRC-NET: Analyzing Social Media to characterize HIV at-risk populations among MSM in San Diego.
(CFAR Developmental Grant, January 2015 – March 2017)
Detecting HIV at-risk MSM in San Diego through Social Networks
(FISP 2014,  UCSD Frontiers of Innovation Scholars Program, January 2015 – October 2016)
Characterizing the Relationship of METH Use and Neurocognitive Impairment in HIV At-risk Online Networks
(TMARC Pilot PST7TP2/2016,  Translational Methamphetamine AIDS Research Center, March 2016 – February 2018)

Publications

  • N. Weibel, P. Desai, L. Saul, A. Gupta, and S. Little, “HIV Risk on Twitter: the Ethical Dimension of Social Media Evidence-based Prevention for Vulnerable Populations,” in Proceedings of HICSS-50, Hawaii International Conference on System Sciences, Big Island, HI, USA, 2017.
    [Abstract] [Bibtex]

    As of 2016 the HIV/AIDS epidemics is still a key public health problem. Recent reports showed that alarmingly high numbers of people in vulnerable populations are not reached by preventative efforts. Despite technology improvement, we are not yet able to identify populations that are most susceptible to HIV infections. In order to enable evidence-based prevention, we are studying new methods to identify HIV at-risk populations exploiting Twitter posts as indicators of HIV risk. Our research on social network analysis and machine learning outlined the feasibility of using tweets as monitoring tool for HIV-related risk at the demographic, geographical, and social network level. However, this approach highlights ethical dilemmas in three different areas: data collection and analysis, risk inference through imperfect probabilistic approaches, and data-driven prevention. We contribute a description, analysis and discussion of ethics based on our 2-year experience with clinicians, IRBs, and local HIV communities in San Diego, California.

    @inproceedings{weibel2017twitter,
     abstract = {As of 2016 the HIV/AIDS epidemics is still a key public health problem. Recent reports showed that alarmingly high numbers of people in vulnerable populations are not reached by preventative efforts. Despite technology improvement, we are not yet able to identify populations that are most susceptible to HIV infections. In order to enable evidence-based prevention, we are studying new methods to identify HIV at-risk populations exploiting Twitter posts as indicators of HIV risk. Our research on social network analysis and machine learning outlined the feasibility of using tweets as monitoring tool for HIV-related risk at the demographic, geographical, and social network level. However, this approach highlights ethical dilemmas in three different areas: data collection and analysis, risk inference through imperfect probabilistic approaches, and data-driven prevention. We contribute a description, analysis and discussion of ethics based on our 2-year experience with clinicians, IRBs, and local HIV communities in San Diego, California.},
     address = {Big Island, HI, USA},
     area = {data_analysis, pervasive_sensing},
     author = {Weibel, Nadir and Desai, Purvi and Saul, Lawrence and Gupta, Amarnath and Little, Susan},
     booktitle = {Proceedings of {HICSS}-50, {Hawaii} {International} {Conference} on {System} {Sciences}},
     interhash = {186ce1d8dba35a1f1286736a41eab680},
     intrahash = {e1a470aa4f364b7ed5a3ffb7572f6741},
     month = jan, note = {In Press},
     projects = {pircnet, core},
     title = {{HIV} {Risk} on {Twitter}: the {Ethical} {Dimension} of {Social} {Media} {Evidence}-based {Prevention} for {Vulnerable} {Populations}},
     year = 2017 
    }
  • [PDF] N. Thangarajan, N. Green, A. Gupta, S. Little, and N. Weibel, “Analyzing Social Media to Characterize Local HIV At-risk Populations,” in Proceedings of Wireless Health 2015, International Conference on Wireless, Connected and Mobile Health Research, Bethesda, USA, 2015.
    [Bibtex]
    @inproceedings{thangarajan2015analyzing,
     address = {Bethesda, USA},
     area = {pervasive_sensing, data_analysis},
     author = {Thangarajan, Narendran and Green, Nella and Gupta, Amaranth and Little, Susan and Weibel, Nadir},
     booktitle = {Proceedings of {Wireless} {Health} 2015, {International} {Conference} on {Wireless},
     {Connected} and {Mobile} {Health} {Research}},
     interhash = {49f82be02b37218ba544b332fb29cba3},
     intrahash = {04a357ef6b8bfce179474bbc022881eb},
     month = oct, note = {In Press},
     projects = {pircnet},
     title = {Analyzing {Social} {Media} to {Characterize} {Local} {HIV} {At}-risk {Populations}},
     year = 2015 
    }
  • G. Merchant, N. Weibel, L. Pina, W. G. Griswold, J. H. Fowler, G. X. Ayala, L. C. Gallo, J. Hollan, and K. Patrick, “Face-to-Face and Online Networks: College Students’ Experiences in a Weight-Loss Trial,” Journal of Health Communication, pp. 1-9, 2017.
    [Abstract] [Bibtex]

    This study aimed to understand how college students participating in a 2-year randomized controlled trial (Project SMART: Social and Mobile Approach to Reduce Weight; N = 404) engaged their social networks and used social and mobile technologies to try and lose weight. Participants in the present study (n = 20 treatment, n = 18 control) were approached after a measurement visit and administered semi-structured interviews. Interviews were analyzed using principles from grounded theory. Treatment group participants appreciated the timely support provided by the study and the integration of content across multiple technologies. Participants in both groups reported using non-study-designed apps to help them lose weight, and many participants knew one another outside of the study. Individuals talked about weight-loss goals with their friends face to face and felt accountable to follow through with their intentions. Although seeing others’ success online motivated many, there was a range of perceived acceptability in talking about personal health-related information on social media. The findings from this qualitative study can inform intervention trials using social and mobile technologies to promote weight loss. For example, weight-loss trials should measure participants’ use of direct-to-consumer technologies and interconnectivity so that treatment effects can be isolated and cross-contamination accounted for.

    @article{merchant2017face,
     abstract = {This study aimed to understand how college students participating in a 2-year randomized controlled trial (Project SMART: Social and Mobile Approach to Reduce Weight; N = 404) engaged their social networks and used social and mobile technologies to try and lose weight. Participants in the present study (n = 20 treatment, n = 18 control) were approached after a measurement visit and administered semi-structured interviews. Interviews were analyzed using principles from grounded theory. Treatment group participants appreciated the timely support provided by the study and the integration of content across multiple technologies. Participants in both groups reported using non-study-designed apps to help them lose weight, and many participants knew one another outside of the study. Individuals talked about weight-loss goals with their friends face to face and felt accountable to follow through with their intentions. Although seeing others’ success online motivated many, there was a range of perceived acceptability in talking about personal health-related information on social media. The findings from this qualitative study can inform intervention trials using social and mobile technologies to promote weight loss. For example, weight-loss trials should measure participants’ use of direct-to-consumer technologies and interconnectivity so that treatment effects can be isolated and cross-contamination accounted for.},
     area = {pervasive_sensing},
     author = {Merchant, Gina and Weibel, Nadir and Pina, Laura and Griswold, William G and Fowler, James H and Ayala, Guadalupe X and Gallo, Linda C and Hollan, James and Patrick, Kevin},
     interhash = {42f0a4bbcf0cd3633b57bd3a2f05b6b1},
     intrahash = {595bbb1b7ff92c334fc872116999b62a},
     journal = {Journal of Health Communication},
     month = {January},
     pages = {1--9},
     projects = {smart},
     publisher = {Taylor & Francis},
     title = {Face-to-Face and Online Networks: College Students’ Experiences in a Weight-Loss Trial},
     year = 2017 
    }
  • [PDF] G. Merchant, N. Weibel, K. Patrick, J. H. Fowler, G. J. Norman, A. Gupta, C. Servetas, K. Calfas, K. Raste, L. Pina, M. Donohue, and S. Marshall, “Click ‘Like’ to change your behavior: A mixed methods study of college students’ exposure to and engagement with Facebook content designed for weight-loss,” Journal of Medical Internet Research, 2014.
    [Bibtex]
    @article{merchant2014click,
     area = {ubicomp_health, pervasive_sensing},
     author = {Merchant, Gina and Weibel, Nadir and Patrick, Kevin and Fowler, James H. and Norman, Greg J. and Gupta, Anjali and Servetas, Christina and Calfas, Karen and Raste, Ketaki and Pina, Laura and Donohue, Mike and Marshall, Simon},
     interhash = {996dc861e949864dae09f7dafa3fcea9},
     intrahash = {8de14708c12962d8fa44e43605ae3058},
     journal = {Journal of Medical Internet Research},
     month = may, projects = {smart, three-two-me},
     title = {Click '{Like}' to change your behavior: {A} mixed methods study of college students' exposure to and engagement with {Facebook} content designed for weight-loss},
     year = 2014 
    }
  • [PDF] G. Merchant, L. Pina, M. Black, E. Bales, N. Weibel, W. Griswold, J. Fowler, and K. Patrick, “Online and face-to-face: How do ad-hoc and existing networks support weight-related behavior change in young adults?,” in Abstracts (Rapid Communication) of SBM 2014, Annual Meeting of the Society of Behavioral Medicine, Philadelphia, USA, 2014.
    [Bibtex]
    @inproceedings{merchant2014online,
     address = {Philadelphia, USA},
     area = {pervasive_sensing, ubicomp_health},
     author = {Merchant, Gina and Pina, Laura and Black, Michelle and Bales, Elizabeth and Weibel, Nadir and Griswold, William and Fowler, James and Patrick, Kevin},
     booktitle = {Abstracts ({Rapid} {Communication}) of {SBM} 2014, {Annual} {Meeting} of the {Society} of {Behavioral} {Medicine}},
     interhash = {4c6a874f02168989fc6e2fb915b0f5f8},
     intrahash = {d62b2dc557f1402e7614907cca1bfc30},
     month = apr, projects = {smart},
     title = {Online and face-to-face: {How} do ad-hoc and existing networks support weight-related behavior change in young adults?},
     year = 2014 
    }

Understanding Patient-Physician Communication

Communication in the medical office between physician and patient is challenging. This is often complicated by third party in the room such as relatives or an interpreter if the patient is not mother language and by the presence of the Electronic Health Record (EHR). We want to better understand the system defined by the medical office and the role that people, artifacts and technology play in this environment to inform the design of the next generation interactive tools and visualization.

Funding

ahrq-logo QUICK – Quantifying Electronic Medical Record Usability to Improve Clinical Workflow
(AHRQ R01 – HS 021290, September 2012 – June 2016)
va_logo DEUCE – Design and Evaluation of User Centered Electronic Health Records
(VA Merit 1 I01-HX000982-01A1) April 2015 – Ongoing

Publications

  • A. Calvitti, H. Hochheiser, S. Ashfaq, K. Bell, Y. Chen, R. El Kareh, M. T. Gabuzda, L. Liu, S. Mortensen, B. Pandey, S. Rick, R. Street, N. Weibel, C. Weir, and Z. Agha, “Physician Activity During Outpatient Visits and Subjective Workload,” Journal of Biomedical Informatics, 2017.
    [Abstract] [Bibtex]

    We describe methods for capturing and analyzing EHR use and clinical workflow of physicians during outpatient encounters and relating activity to physicians’ self-reported workload. We collected temporally-resolved activity data including audio, video, EHR activity, and eye-gaze along with post-visit assessments of workload. These data are then analyzed through a combination of manual content analysis and computational techniques to temporally align streams, providing a range of process measures of EHR usage, clinical workflow, and physician-patient communication. Data was collected from primary care and specialty clinics at the Veterans Administration San Diego Healthcare System and UCSD Health, who use Electronic Health Record (EHR) platforms, CPRS and Epic, respectively. Grouping visit activity by physician, site, specialty, and patient status enables rank-ordering activity factors by their correlation to physicians’ subjective work-load as captured by NASA Task Load Index survey. We developed a coding scheme that enabled us to compare timing studies between CPRS and Epic and extract patient and visit complexity profile. We identified similar patterns of EHR use and navigation at the 2 sites despite differences in functions, user interface and consequent coded representation. Both sites displayed similar proportions of EHR function use and navigation, and distribution of visit length, proportion of time physicians attended to EHRs (gaze), and subjective work-load as measured by task load survey. We found that visit activity was highly variable across individual physicians, and the observed activity metrics ranged widely as correlates to subjective workload. We discuss implications of our study for methodology, clinical workflow and EHR redesign.

    @article{calvitti2017jbi,
     abstract = {We describe methods for capturing and analyzing EHR use and clinical workflow of physicians during outpatient encounters and relating activity to physicians' self-reported workload. We collected temporally-resolved activity data including audio, video, EHR activity, and eye-gaze along with post-visit assessments of workload. These data are then analyzed through a combination of manual content analysis and computational techniques to temporally align streams, providing a range of process measures of EHR usage, clinical workflow, and physician-patient communication. Data was collected from primary care and specialty clinics at the Veterans Administration San Diego Healthcare System and UCSD Health, who use Electronic Health Record (EHR) platforms, CPRS and Epic, respectively. Grouping visit activity by physician, site, specialty, and patient status enables rank-ordering activity factors by their correlation to physicians' subjective work-load as captured by NASA Task Load Index survey. We developed a coding scheme that enabled us to compare timing studies between CPRS and Epic and extract patient and visit complexity profile. We identified similar patterns of EHR use and navigation at the 2 sites despite differences in functions, user interface and consequent coded representation. Both sites displayed similar proportions of EHR function use and navigation, and distribution of visit length, proportion of time physicians attended to EHRs (gaze), and subjective work-load as measured by task load survey. We found that visit activity was highly variable across individual physicians, and the observed activity metrics ranged widely as correlates to subjective workload. We discuss implications of our study for methodology, clinical workflow and EHR redesign.},
     area = {pervasive_sensing},
     author = {Calvitti, Alan and Hochheiser, Harry and Ashfaq, Shazia and Bell, Kristin and Chen, Yunan and El Kareh, Robert and Gabuzda, Mark T and Liu, Lin and Mortensen, Sara and Pandey, Braj and Rick, Steven and Street, Rick and Weibel, Nadir and Weir, Charlene and Agha, Zia},
     interhash = {a94cc5b603926daf203862d93306e55a},
     intrahash = {7ac99ea30089a236039ef26bcf526cb5},
     journal = {Journal of Biomedical Informatics},
     projects = {quick},
     title = {Physician Activity During Outpatient Visits and Subjective Workload},
     year = 2017 
    }
  • [URL] J. Zhang, K. Avery, Y. Chen, S. Ashfaq, S. Rick, K. Zhang, N. and Weibel, H. S. Hochheiser, C. Weir, K. M. Bell, M. T. Gabuzda, N. Farber, B. Pandey, A. Calvitti, L. Liu, R. Street, and Z. Agha, “A Preliminary Study on EHR-Associated Extra Workload Among Physicians,” in Proceedings (Posters) of AMIA 2015, American Medical Informatics Association, Annual Symposium, San Francisco, USA, 2015.
    [Bibtex]
    @inproceedings{zhang2015preliminary,
     address = {San Francisco, USA},
     area = {pervasive_sensing},
     author = {Zhang, Jing and Avery, Kellie and Chen, Yunan and Ashfaq, Shazia and Rick, Steven and Zhang, Kai and and Weibel, Nadir and Hochheiser, Harry S. and Weir, Charlene and Bell, Kristin M. and Gabuzda, Mark T. and Farber, Neil and Pandey, Braj and Calvitti, Alan and Liu, Lin and Street, Richard and Agha, Zia},
     booktitle = {Proceedings ({Posters}) of {AMIA} 2015, {American} {Medical} {Informatics} {Association},
     {Annual} {Symposium}},
     interhash = {a5c106803b5f30ad0e873a0c0bcd6eca},
     intrahash = {002b1fa76ca4130834725e35656cd29b},
     month = nov, note = {In Press},
     projects = {quick, medical_informatics},
     title = {A {Preliminary} {Study} on {EHR}-{Associated} {Extra} {Workload} {Among} {Physicians}},
     url = {http://knowledge.amia.org/59310-amia-1.2741865/t005-1.2744350/f005-1.2744351/2248934-1.2744373/2248934-1.2744374},
     year = 2015 
    }
  • [URL] S. Ashfaq, K. M. Bell, M. Difley, S. Mortensen, K. Avery, S. Rick, N. Weibel, B. Pandey, C. Weir, H. S. Hochheiser, Y. Chen, J. Zhang, K. Zhang, R. Street, M. T. Gabuzda, N. Farber, L. Liu, A. Calvitti, and Z. Agha, “Analysis of Computerized Clinical Reminder Activity and Usability Issues,” in Proceedings (Posters) of AMIA 2015, American Medical Informatics Association, Annual Symposium, San Francisco, USA, 2015.
    [Bibtex]
    @inproceedings{ashfaq2015analysis,
     address = {San Francisco, USA},
     area = {pervasive_sensing},
     author = {Ashfaq, Shazia and Bell, Kristin M. and Difley, Megan and Mortensen, Sara and Avery, Kellie and Rick, Steven and Weibel, Nadir and Pandey, Braj and Weir, Charlene and Hochheiser, Harry S. and Chen, Yunan and Zhang, Jing and Zhang, Kai and Street, Richard and Gabuzda, Mark T. and Farber, Neil and Liu, Lin and Calvitti, Alan and Agha, Zia},
     booktitle = {Proceedings ({Posters}) of {AMIA} 2015, {American} {Medical} {Informatics} {Association},
     {Annual} {Symposium}},
     interhash = {aae4c27bbc39c79ee5270e2976ec286d},
     intrahash = {f0bfc4c235a5801a11e9a4d1ca06a4e1},
     month = nov, note = {In Press},
     projects = {quick, medical_informatics},
     title = {Analysis of {Computerized} {Clinical} {Reminder} {Activity} and {Usability} {Issues}},
     url = {http://knowledge.amia.org/59310-amia-1.2741865/t005-1.2744350/f005-1.2744351/2248936-1.2745427/2248936-1.2745428?qr=1},
     year = 2015 
    }
  • [PDF] S. Rick, A. Calvitti, Z. Agha, and N. Weibel, “Eyes on the Clinic: Accelerating Meaningful Interface Analysis through Unobtrusive Eye Tracking,” in Proceedings of PervasiveHealth 2015, International Conference on Pervasive Computing Technologies for Healthcare, Istanbul, Turkey, 2015.
    [Bibtex]
    @inproceedings{rick2015clinic,
     address = {Istanbul, Turkey},
     area = {pervasive_sensing},
     author = {Rick, Steven and Calvitti, Alan and Agha, Zia and Weibel, Nadir},
     booktitle = {Proceedings of {PervasiveHealth} 2015, {International} {Conference} on {Pervasive} {Computing} {Technologies} for {Healthcare}},
     interhash = {62adea43383b70c3b9ae5dc2bb995bab},
     intrahash = {8f3a3a660d7a9e74258c7178faac8d62},
     month = may, note = {In Press},
     projects = {quick, medical_informatics, computational_ethnography},
     title = {Eyes on the {Clinic}: {Accelerating} {Meaningful} {Interface} {Analysis} through {Unobtrusive} {Eye} {Tracking}},
     year = 2015 
    }
  • [PDF] K. Zheng, D. Hanauer, Z. Agha, and N. Weibel, “Computational Ethnography: Automated and Unobtrusive Means for Collecting Data in situ for Human-Computer Interaction Studies,” in Cognitive Informatics in Health and Biomedicine: Human Computer Interaction in Healthcare, V. L. Patel, T. G. Kannampallil, and D. Kaufman, Eds., Springer, 2015.
    [Bibtex]
    @incollection{zheng2015computational,
     area = {pervasive_sensing},
     author = {Zheng, Kai and Hanauer, David and Agha, Zia and Weibel, Nadir},
     booktitle = {Cognitive {Informatics} in {Health} and {Biomedicine}: {Human} {Computer} {Interaction} in {Healthcare}},
     editor = {Patel, Vimla L. and Kannampallil, Thomas G. and Kaufman, David},
     interhash = {e3273a755240be319b05d3d052dcab02},
     intrahash = {8949ae5ded1b43502b1a6b0a8432e15a},
     month = jan, note = {ISBN 978-3-319-17271-2},
     projects = {quick, patient-physician-communication,computational_ethnography},
     publisher = {Springer},
     title = {Computational {Ethnography}: {Automated} and {Unobtrusive} {Means} for {Collecting} {Data} in situ for {Human}-{Computer} {Interaction} {Studies}},
     year = 2015 
    }
  • [PDF] N. Weibel, S. Rick, C. Emmenegger, S. Ashfaq, A. Calvitti, and Z. Agha, “LAB-IN-A-BOX: Semi-Automatic Tracking of Activity in the Medical Office,” Pers Ubiquit Comput – Health, 2014.
    [Bibtex]
    @article{weibel2014labinabox,
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Rick, Steven and Emmenegger, Colleen and Ashfaq, Shazia and Calvitti, Alan and Agha, Zia},
     interhash = {dcecbcd424ffc9f5e032165284e4f13a},
     intrahash = {5da8ceba93930c415bdf7775db44bbff},
     journal = {Pers Ubiquit Comput - Health},
     month = sep, projects = {quick, medical_informatics, stroke-kinect, ergokinect, gestures, kinect, computational_ethnography},
     title = {{LAB}-{IN}-{A}-{BOX}: {Semi}-{Automatic} {Tracking} of {Activity} in the {Medical} {Office}},
     year = 2014 
    }
  • [URL] A. Calvitti, N. Weibel, H. Hochheiser, L. Liu, K. Zheng, C. Weir, S. Ashfaq, S. Rick, Z. Agha, and B. Gray, “Can eye tracking and EHR mouse activity tell us when clinicians are overloaded?,” Human Factors Quarterly, Veteran Health Administration, 2014.
    [Bibtex]
    @article{calvitti2014tracking,
     area = {pervasive_sensing},
     author = {Calvitti, Alan and Weibel, Nadir and Hochheiser, Harry and Liu, Lin and Zheng, Kai and Weir, Charlene and Ashfaq, Shazia and Rick, Steven and Agha, Zia and Gray, Barbara},
     interhash = {63723b1b64d631168cf02d4c337bb8c0},
     intrahash = {4395dd0ad3ea36df93a1bcfc83ea5413},
     journal = {Human Factors Quarterly, Veteran Health Administration},
     month = sep, projects = {quick, medical_informatics,computational_ethnography},
     title = {Can eye tracking and {EHR} mouse activity tell us when clinicians are overloaded?},
     url = {https://content.govdelivery.com/accounts/USVHA/bulletins/cfd5d2#article4},
     year = 2014 
    }
  • [PDF] N. Weibel, S. Ashfaq, A. Calvitti, J. D. Hollan, and Z. Agha, “Multimodal Data Analysis and Visualization to Study Usability of Electronic Health Records,” in Proceedings of PervasiveHealth 2013, International Conference on Pervasive Computing Technologies for Healthcare (Poster Track), Venice, Italy, 2013, pp. 282-283.
    [Bibtex]
    @inproceedings{weibel2013multimodal,
     address = {Venice, Italy},
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Ashfaq, Shazia and Calvitti, Alan and Hollan, James D. and Agha, Zia},
     booktitle = {Proceedings of {PervasiveHealth} 2013, {International} {Conference} on {Pervasive} {Computing} {Technologies} for {Healthcare} ({Poster} {Track})},
     interhash = {ed3d21b7605e575861886eb709ac8b45},
     intrahash = {450e0fa1576bbef68ac5c693b74338e1},
     month = may, pages = {282--283},
     projects = {quick, medical_informatics,computational_ethnography},
     title = {Multimodal {Data} {Analysis} and {Visualization} to {Study} {Usability} of {Electronic} {Health} {Records}},
     year = 2013 
    }
  • [PDF] A. Rule, S. Rick, M. Chiu, P. Rios, S. Ashfaq, A. Calvitti, W. Chan, N. Weibel, and Z. Agha, “Validating free-text order entry for a note-centric EHR,” in Proceedings of AMIA 2015, American Medical Informatics Association, Annual Symposium, San Francisco, USA, 2015.
    [Bibtex]
    @inproceedings{rule2015validating,
     address = {San Francisco, USA},
     area = {pervasive_sensing},
     author = {Rule, Adam and Rick, Steven and Chiu, Michael and Rios, Phillip and Ashfaq, Shazia and Calvitti, Alan and Chan, Wesley and Weibel, Nadir and Agha, Zia},
     booktitle = {Proceedings of {AMIA} 2015, {American} {Medical} {Informatics} {Association},
     {Annual} {Symposium}},
     interhash = {c91f26d1853fe7e9bf187ec08009ae02},
     intrahash = {36adcf2baa5c21ad528c4c68231957a2},
     month = nov, note = {In Press},
     projects = {anotes, medical_informatics},
     title = {Validating free-text order entry for a note-centric {EHR}},
     year = 2015 
    }

Archived Projects

Publications – Pervasive Sensing and Health

  • G. Merchant, N. Weibel, L. Pina, W. G. Griswold, J. H. Fowler, G. X. Ayala, L. C. Gallo, J. Hollan, and K. Patrick, “Face-to-Face and Online Networks: College Students’ Experiences in a Weight-Loss Trial,” Journal of Health Communication, pp. 1-9, 2017.
    [Abstract] [Bibtex]

    This study aimed to understand how college students participating in a 2-year randomized controlled trial (Project SMART: Social and Mobile Approach to Reduce Weight; N = 404) engaged their social networks and used social and mobile technologies to try and lose weight. Participants in the present study (n = 20 treatment, n = 18 control) were approached after a measurement visit and administered semi-structured interviews. Interviews were analyzed using principles from grounded theory. Treatment group participants appreciated the timely support provided by the study and the integration of content across multiple technologies. Participants in both groups reported using non-study-designed apps to help them lose weight, and many participants knew one another outside of the study. Individuals talked about weight-loss goals with their friends face to face and felt accountable to follow through with their intentions. Although seeing others’ success online motivated many, there was a range of perceived acceptability in talking about personal health-related information on social media. The findings from this qualitative study can inform intervention trials using social and mobile technologies to promote weight loss. For example, weight-loss trials should measure participants’ use of direct-to-consumer technologies and interconnectivity so that treatment effects can be isolated and cross-contamination accounted for.

    @article{merchant2017face,
     abstract = {This study aimed to understand how college students participating in a 2-year randomized controlled trial (Project SMART: Social and Mobile Approach to Reduce Weight; N = 404) engaged their social networks and used social and mobile technologies to try and lose weight. Participants in the present study (n = 20 treatment, n = 18 control) were approached after a measurement visit and administered semi-structured interviews. Interviews were analyzed using principles from grounded theory. Treatment group participants appreciated the timely support provided by the study and the integration of content across multiple technologies. Participants in both groups reported using non-study-designed apps to help them lose weight, and many participants knew one another outside of the study. Individuals talked about weight-loss goals with their friends face to face and felt accountable to follow through with their intentions. Although seeing others’ success online motivated many, there was a range of perceived acceptability in talking about personal health-related information on social media. The findings from this qualitative study can inform intervention trials using social and mobile technologies to promote weight loss. For example, weight-loss trials should measure participants’ use of direct-to-consumer technologies and interconnectivity so that treatment effects can be isolated and cross-contamination accounted for.},
     area = {pervasive_sensing},
     author = {Merchant, Gina and Weibel, Nadir and Pina, Laura and Griswold, William G and Fowler, James H and Ayala, Guadalupe X and Gallo, Linda C and Hollan, James and Patrick, Kevin},
     interhash = {42f0a4bbcf0cd3633b57bd3a2f05b6b1},
     intrahash = {595bbb1b7ff92c334fc872116999b62a},
     journal = {Journal of Health Communication},
     month = {January},
     pages = {1--9},
     projects = {smart},
     publisher = {Taylor & Francis},
     title = {Face-to-Face and Online Networks: College Students’ Experiences in a Weight-Loss Trial},
     year = 2017 
    }
  • N. Weibel, P. Desai, L. Saul, A. Gupta, and S. Little, “HIV Risk on Twitter: the Ethical Dimension of Social Media Evidence-based Prevention for Vulnerable Populations,” in Proceedings of HICSS-50, Hawaii International Conference on System Sciences, Big Island, HI, USA, 2017.
    [Abstract] [Bibtex]

    As of 2016 the HIV/AIDS epidemics is still a key public health problem. Recent reports showed that alarmingly high numbers of people in vulnerable populations are not reached by preventative efforts. Despite technology improvement, we are not yet able to identify populations that are most susceptible to HIV infections. In order to enable evidence-based prevention, we are studying new methods to identify HIV at-risk populations exploiting Twitter posts as indicators of HIV risk. Our research on social network analysis and machine learning outlined the feasibility of using tweets as monitoring tool for HIV-related risk at the demographic, geographical, and social network level. However, this approach highlights ethical dilemmas in three different areas: data collection and analysis, risk inference through imperfect probabilistic approaches, and data-driven prevention. We contribute a description, analysis and discussion of ethics based on our 2-year experience with clinicians, IRBs, and local HIV communities in San Diego, California.

    @inproceedings{weibel2017twitter,
     abstract = {As of 2016 the HIV/AIDS epidemics is still a key public health problem. Recent reports showed that alarmingly high numbers of people in vulnerable populations are not reached by preventative efforts. Despite technology improvement, we are not yet able to identify populations that are most susceptible to HIV infections. In order to enable evidence-based prevention, we are studying new methods to identify HIV at-risk populations exploiting Twitter posts as indicators of HIV risk. Our research on social network analysis and machine learning outlined the feasibility of using tweets as monitoring tool for HIV-related risk at the demographic, geographical, and social network level. However, this approach highlights ethical dilemmas in three different areas: data collection and analysis, risk inference through imperfect probabilistic approaches, and data-driven prevention. We contribute a description, analysis and discussion of ethics based on our 2-year experience with clinicians, IRBs, and local HIV communities in San Diego, California.},
     address = {Big Island, HI, USA},
     area = {data_analysis, pervasive_sensing},
     author = {Weibel, Nadir and Desai, Purvi and Saul, Lawrence and Gupta, Amarnath and Little, Susan},
     booktitle = {Proceedings of {HICSS}-50, {Hawaii} {International} {Conference} on {System} {Sciences}},
     interhash = {186ce1d8dba35a1f1286736a41eab680},
     intrahash = {e1a470aa4f364b7ed5a3ffb7572f6741},
     month = jan, note = {In Press},
     projects = {pircnet, core},
     title = {{HIV} {Risk} on {Twitter}: the {Ethical} {Dimension} of {Social} {Media} {Evidence}-based {Prevention} for {Vulnerable} {Populations}},
     year = 2017 
    }
  • S. Dunseath, N. Weibel, C. Bloss, and C. Nebeker, “NIH support of Mobile, Imaging, pervasive Sensing, Social media and location Tracking (MISST) research: Laying the foundation to examine research ethics in the digital age,” Nature: Digital Medicine, 2017.
    [Bibtex]
    @article{dunseath2017,
     area = {pervasive_sensing},
     author = {Dunseath, Sarah and Weibel, Nadir and Bloss, Cinnamon and Nebeker, Camille},
     interhash = {4bf0b7eed1ad0e380eb7d4bd88afd75c},
     intrahash = {46fb255f71f000385574cda590c8a69b},
     journal = {{Nature: Digital Medicine}},
     projects = {core},
     title = {{NIH} support of {Mobile, Imaging, pervasive Sensing, Social media and location Tracking (MISST)} research: Laying the foundation to examine research ethics in the digital age},
     year = 2017 
    }
  • A. Calvitti, H. Hochheiser, S. Ashfaq, K. Bell, Y. Chen, R. El Kareh, M. T. Gabuzda, L. Liu, S. Mortensen, B. Pandey, S. Rick, R. Street, N. Weibel, C. Weir, and Z. Agha, “Physician Activity During Outpatient Visits and Subjective Workload,” Journal of Biomedical Informatics, 2017.
    [Abstract] [Bibtex]

    We describe methods for capturing and analyzing EHR use and clinical workflow of physicians during outpatient encounters and relating activity to physicians’ self-reported workload. We collected temporally-resolved activity data including audio, video, EHR activity, and eye-gaze along with post-visit assessments of workload. These data are then analyzed through a combination of manual content analysis and computational techniques to temporally align streams, providing a range of process measures of EHR usage, clinical workflow, and physician-patient communication. Data was collected from primary care and specialty clinics at the Veterans Administration San Diego Healthcare System and UCSD Health, who use Electronic Health Record (EHR) platforms, CPRS and Epic, respectively. Grouping visit activity by physician, site, specialty, and patient status enables rank-ordering activity factors by their correlation to physicians’ subjective work-load as captured by NASA Task Load Index survey. We developed a coding scheme that enabled us to compare timing studies between CPRS and Epic and extract patient and visit complexity profile. We identified similar patterns of EHR use and navigation at the 2 sites despite differences in functions, user interface and consequent coded representation. Both sites displayed similar proportions of EHR function use and navigation, and distribution of visit length, proportion of time physicians attended to EHRs (gaze), and subjective work-load as measured by task load survey. We found that visit activity was highly variable across individual physicians, and the observed activity metrics ranged widely as correlates to subjective workload. We discuss implications of our study for methodology, clinical workflow and EHR redesign.

    @article{calvitti2017jbi,
     abstract = {We describe methods for capturing and analyzing EHR use and clinical workflow of physicians during outpatient encounters and relating activity to physicians' self-reported workload. We collected temporally-resolved activity data including audio, video, EHR activity, and eye-gaze along with post-visit assessments of workload. These data are then analyzed through a combination of manual content analysis and computational techniques to temporally align streams, providing a range of process measures of EHR usage, clinical workflow, and physician-patient communication. Data was collected from primary care and specialty clinics at the Veterans Administration San Diego Healthcare System and UCSD Health, who use Electronic Health Record (EHR) platforms, CPRS and Epic, respectively. Grouping visit activity by physician, site, specialty, and patient status enables rank-ordering activity factors by their correlation to physicians' subjective work-load as captured by NASA Task Load Index survey. We developed a coding scheme that enabled us to compare timing studies between CPRS and Epic and extract patient and visit complexity profile. We identified similar patterns of EHR use and navigation at the 2 sites despite differences in functions, user interface and consequent coded representation. Both sites displayed similar proportions of EHR function use and navigation, and distribution of visit length, proportion of time physicians attended to EHRs (gaze), and subjective work-load as measured by task load survey. We found that visit activity was highly variable across individual physicians, and the observed activity metrics ranged widely as correlates to subjective workload. We discuss implications of our study for methodology, clinical workflow and EHR redesign.},
     area = {pervasive_sensing},
     author = {Calvitti, Alan and Hochheiser, Harry and Ashfaq, Shazia and Bell, Kristin and Chen, Yunan and El Kareh, Robert and Gabuzda, Mark T and Liu, Lin and Mortensen, Sara and Pandey, Braj and Rick, Steven and Street, Rick and Weibel, Nadir and Weir, Charlene and Agha, Zia},
     interhash = {a94cc5b603926daf203862d93306e55a},
     intrahash = {7ac99ea30089a236039ef26bcf526cb5},
     journal = {Journal of Biomedical Informatics},
     projects = {quick},
     title = {Physician Activity During Outpatient Visits and Subjective Workload},
     year = 2017 
    }
  • S. Rick, V. Ramesh, D. Gasques Rodrigues, and N. Weibel, “Pervasive Sensing in Healthcare: From Observing and Collecting to Seeing and Understanding,” in In Proc. of WISH, Workshop on Interactive System for Healthcare, CHI 2017, 2017.
    [Abstract] [Bibtex]

    From analyzing complex socio-technical systems, to evaluating novel interactions, increasingly pervasive sensing technologies provide researchers with new ways to observe the world. This paradigm shift is enabling capture of richer and more diverse data, combining elements from in-depth study of activity and behavior with modern sensors, and providing the means to accelerate sense-making of complex behavioral data. At the same time novel multimodal signal processing and machine learning techniques are equipping us with ‘super powers’ that enable understanding of these data in real-time, opening up new opportunities for embracing the concept of ‘Data Science in the Wild’. In this paper we present what this transition means in the context of Health and Healthcare, focusing on how it leads to the ‘UbiScope’, a ubiquitous computing microscope for detecting particular health conditions in real-time, promoting reflection on care, and guiding medical practices. Just as the microscope supported key scientific advances, the UbiScope will act as a proxy for understanding and supporting human activity and inform specific interventions in the years ahead.

    @inproceedings{wish2017,
     abstract = {From analyzing complex socio-technical systems, to evaluating novel interactions, increasingly pervasive sensing technologies provide researchers with new ways to observe the world. This paradigm shift is enabling capture of richer and more diverse data, combining elements from in-depth study of activity and behavior with modern sensors, and providing the means to accelerate sense-making of complex behavioral data. At the same time novel multimodal signal processing and machine learning techniques are equipping us with 'super powers' that enable understanding of these data in real-time, opening up new opportunities for embracing the concept of 'Data Science in the Wild'. In this paper we present what this transition means in the context of Health and Healthcare, focusing on how it leads to the 'UbiScope', a ubiquitous computing microscope for detecting particular health conditions in real-time, promoting reflection on care, and guiding medical practices. Just as the microscope supported key scientific advances, the UbiScope will act as a proxy for understanding and supporting human activity and inform specific interventions in the years ahead.},
     area = {pervasive_sensing},
     author = {Rick, Steven and Ramesh, Vish and Gasques Rodrigues, Danilo and Weibel, Nadir},
     booktitle = {In Proc. of WISH, Workshop on Interactive System for Healthcare, CHI 2017},
     interhash = {14a61e317efb0273cf9b9387910c3c2a},
     intrahash = {ac36ea34e7460fb52fac2f8004823299},
     projects = {ubiscope},
     title = {Pervasive Sensing in Healthcare: From Observing and Collecting to Seeing and Understanding},
     year = 2017 
    }
  • C. Bloss, C. Nebeker, M. Bietz, D. Bae, B. Bigby, M. Devereaux, J. Fowler, A. Waldo, N. Weibel, K. Patrick, and others, “Reimagining Human Research Protections for 21st Century Science,” Journal of Medical Internet Research (JMIR), vol. 18, iss. 12, p. e329, 2017.
    [Bibtex]
    @article{bloss2016reimagining,
     area = {pervasive_sensing},
     author = {Bloss, Cinnamon and Nebeker, Camille and Bietz, Matthew and Bae, Deborah and Bigby, Barbara and Devereaux, Mary and Fowler, James and Waldo, Ann and Weibel, Nadir and Patrick, Kevin and others},
     interhash = {4aa43c331dfae18e3b724c783e0ca60c},
     intrahash = {f763583b48011e729cc5b7de4fb25091},
     journal = {Journal of Medical Internet Research (JMIR)},
     number = 12, pages = {e329},
     projects = {core},
     publisher = {JMIR Publications Inc., Toronto, Canada},
     title = {Reimagining Human Research Protections for 21st Century Science},
     volume = 18, year = 2017 
    }
  • V. Ramesh, S. Rick, B. Meyer, G. Cauwenberghs, and N. Weibel, “A Neurobehavioral Evaluation System Using 3D Depth Tracking & Computer Vision: The Case of Stroke-Kinect.,” in Proceedings of Neuroscience 2016, Annual Meeting of the Society for Neuroscience (Poster presentation), San Diego, CA, USA, 2016.
    [Abstract] [Bibtex]

    Due to the subtlety of their symptoms – slight tremors, blurred vision, and loss of mobility, for example – many neurological diseases are challenging to diagnose. As such, a computational tool that can identify and analyze these symptoms accurately will be of immense use to neurologists. We aim to characterize human motor and cognitive abilities through a multimodal approach that will lead to signatures for neurological disorders, based on patterns in relevant identifiers. We focus here on stroke. Stroke is the 4th leading cause of death and the leading cause of disability in the United States. But Recombinant Tissue Plasminogen Activator (rt-PA), the only FDA-approved treatment currently available, is administered in less than 5% of acute stroke cases. The decision to prescribe rt-PA is based on the National Institute of Health Stroke Scale (NIHSS), a combination of multiple tests conducted by a neurologist to assess visual fields and motor and sensory impairments. Stroke evaluation with the NIHSS is inherently subjective. An inexperienced evaluator may miss key or almost imperceptible tells, misdiagnose the severity of a stroke, forego rt-PA prescriptions, and crudely predict long term outcomes. If this gap in objective and reliable stroke diagnosis is not addressed, stroke survivors will endure an arduous rehabilitation process. We are therefore developing Stroke-Kinect, a new system for automatic eye motion and body motion analysis to assist in the diagnosis of stroke. We obtain high-definition images and the spatial and temporal positions of 25 body joints in stroke and healthy control patients with the Microsoft Kinect v2. We employ machine learning classification algorithms and computer vision techniques to replicate the subjective NIHSS test computationally. Furthermore, we develop new tests for identifiers not captured by the NIHSS that are difficult to detect by the human eye: joint angles and thus body posture, velocity of gestures, twitches and jerks, and center of mass. Our analysis of depth data collected from stroke patients indicates accurate testing for the synchronicity of movements and reliable eye gaze tracking. The data also identifies posture as a key indicator of left side versus right side weakness. These results suggest that larger data sets will permit identification of only the vital indicators in stroke diagnosis, to simplify the NIHSS and mitigate the risk of false negatives and erroneous prescriptions of rt-PA. Stroke-Kinect also paves the way for the computational diagnosis of other neurological disorders, furthering the health sciences and ultimately aiding patients in their recovery.

    @inproceedings{sfn2016_ramesh,
     abstract = {Due to the subtlety of their symptoms - slight tremors, blurred vision, and loss of mobility, for example - many neurological diseases are challenging to diagnose. As such, a computational tool that can identify and analyze these symptoms accurately will be of immense use to neurologists. We aim to characterize human motor and cognitive abilities through a multimodal approach that will lead to signatures for neurological disorders, based on patterns in relevant identifiers. We focus here on stroke. Stroke is the 4th leading cause of death and the leading cause of disability in the United States. But Recombinant Tissue Plasminogen Activator (rt-PA), the only FDA-approved treatment currently available, is administered in less than 5% of acute stroke cases. The decision to prescribe rt-PA is based on the National Institute of Health Stroke Scale (NIHSS), a combination of multiple tests conducted by a neurologist to assess visual fields and motor and sensory impairments. Stroke evaluation with the NIHSS is inherently subjective. An inexperienced evaluator may miss key or almost imperceptible tells, misdiagnose the severity of a stroke, forego rt-PA prescriptions, and crudely predict long term outcomes. If this gap in objective and reliable stroke diagnosis is not addressed, stroke survivors will endure an arduous rehabilitation process. We are therefore developing Stroke-Kinect, a new system for automatic eye motion and body motion analysis to assist in the diagnosis of stroke. We obtain high-definition images and the spatial and temporal positions of 25 body joints in stroke and healthy control patients with the Microsoft Kinect v2. We employ machine learning classification algorithms and computer vision techniques to replicate the subjective NIHSS test computationally. Furthermore, we develop new tests for identifiers not captured by the NIHSS that are difficult to detect by the human eye: joint angles and thus body posture, velocity of gestures, twitches and jerks, and center of mass. Our analysis of depth data collected from stroke patients indicates accurate testing for the synchronicity of movements and reliable eye gaze tracking. The data also identifies posture as a key indicator of left side versus right side weakness. These results suggest that larger data sets will permit identification of only the vital indicators in stroke diagnosis, to simplify the NIHSS and mitigate the risk of false negatives and erroneous prescriptions of rt-PA. Stroke-Kinect also paves the way for the computational diagnosis of other neurological disorders, furthering the health sciences and ultimately aiding patients in their recovery.},
     address = {San Diego, CA, USA},
     area = {pervasive_sensing},
     author = {Ramesh, Vish and Rick, Steven and Meyer, Brett and Cauwenberghs, Gert and Weibel, Nadir},
     booktitle = {Proceedings of Neuroscience 2016, Annual Meeting of the Society for Neuroscience (Poster presentation)},
     interhash = {ae008fd247b1d2f695a9ced3b4c3bc47},
     intrahash = {d363538113a55afe4c39d0a77ef605e5},
     month = nov, projects = {ubiscope},
     title = {{A Neurobehavioral Evaluation System Using 3D Depth Tracking & Computer Vision: The Case of Stroke-Kinect.}},
     year = 2016 
    }
  • N. Weibel and J. Favela, PervasiveHealth ’16: Proceedings of the 10th International Conference on Pervasive Computing Technologies for Healthcare, New York, NY, USA: ACM, 2016.
    [Abstract] [Bibtex]

    Pervasive Health Conference is a premier international forum with specific focus on technologies and human factors related to the use of ubiquitous computing in healthcare and for wellbeing. The overall goal of the Pervasive Health Conference is to take a multidisciplinary approach to Pervasive Healthcare Technology research and development. The Pervasive Healthcare Community is addressing a broad scope of research topics and concerns: identify and understand problems from a technological, social, medical, and legal as well as financial perspective (with a particular emphasis on understanding and supporting patients’ and practitioners’ needs); design, implementation, and evaluation of supporting hardware and software infrastructures, algorithms, services and applications; and organizational strategies that facilitate integration of Pervasive Healthcare Technology into the healthcare enterprise

    @book{weibel2016pervasivehealth,
     abstract = {Pervasive Health Conference is a premier international forum with specific focus on technologies and human factors related to the use of ubiquitous computing in healthcare and for wellbeing. The overall goal of the Pervasive Health Conference is to take a multidisciplinary approach to Pervasive Healthcare Technology research and development. The Pervasive Healthcare Community is addressing a broad scope of research topics and concerns: identify and understand problems from a technological, social, medical, and legal as well as financial perspective (with a particular emphasis on understanding and supporting patients’ and practitioners’ needs); design, implementation, and evaluation of supporting hardware and software infrastructures, algorithms, services and applications; and organizational strategies that facilitate integration of Pervasive Healthcare Technology into the healthcare enterprise},
     address = {New York, NY, USA},
     area = {ubicomp_health, pervasive_sensing},
     author = {Weibel, Nadir and Favela, Jesus},
     interhash = {49df5a2314a607c549ff1d7d56fb74a0},
     intrahash = {67ba2f457781c6247a480c3c2c8549a3},
     month = may, note = {ISBN 978-1-XXXX-XXXX-X},
     publisher = {ACM},
     title = {{PervasiveHealth} '16: {Proceedings} of the 10th {International} {Conference} on {Pervasive} {Computing} {Technologies} for {Healthcare}},
     year = 2016 
    }
  • [PDF] X. Ochoa, M. Worsley, N. Weibel, and S. Oviatt, “Multimodal learning analytics data challenges,” in Proceedings of LAK’16, Sixth International Conference on Learning Analytics & Knowledge, 2016, pp. 498-499.
    [Bibtex]
    @inproceedings{ochoa2016multimodal,
     area = {pervasive_sensing, data_analysis},
     author = {Ochoa, Xavier and Worsley, Marcelo and Weibel, Nadir and Oviatt, Sharon},
     booktitle = {Proceedings of {LAK}'16, {Sixth} {International} {Conference} on {Learning} {Analytics} \& {Knowledge}},
     interhash = {b4e38aaa8ad361c36456ac4106dad1dd},
     intrahash = {5126fd65f78281c59848f9b0f6b2e1fd},
     month = apr, pages = {498--499},
     projects = {multimodal, mmla},
     title = {Multimodal learning analytics data challenges},
     year = 2016 
    }
  • S. Hwang, S. Rick, E. Sayyari, D. Lenzen, and N. Weibel, “The signals of communicative efficiency and linguistic organization: findings from depth sensor skeleton tracking,” in Proceedings (Posters) of TISLR 12, 12th Theoretical Issues in Sign Language Research Conference, Melbourne, Australia, 2016.
    [Bibtex]
    @inproceedings{hwang2016signals,
     address = {Melbourne, Australia},
     area = {pervasive_sensing, ubicomp_health},
     author = {Hwang, So-One and Rick, Steven and Sayyari, Erfan and Lenzen, Dan and Weibel, Nadir},
     booktitle = {Proceedings ({Posters}) of {TISLR} 12, 12th {Theoretical} {Issues} in {Sign} {Language} {Research} {Conference}},
     interhash = {261d9d95076255248db58d346ef092a3},
     intrahash = {d03284b5ae707c41db108a93fded2d4b},
     month = jan, note = {In Press},
     projects = {gestures, sign-language},
     title = {The signals of communicative efficiency and linguistic organization: findings from depth sensor skeleton tracking},
     year = 2016 
    }
  • [PDF] J. Calderon, G. X. Ayala, J. P. Elder, G. E. Belch, I. A. Castro, N. Weibel, and J. Pickrel, “What happens when parents and children go grocery shopping? An observational study of Latinos in Southern California,” Health Education and Behavior, 2016.
    [Bibtex]
    @article{calderon2016happens,
     area = {pervasive_sensing},
     author = {Calderon, J. and Ayala, G.X. and Elder, J.P. and Belch, G.E. and Castro, I.A. and Weibel, Nadir and Pickrel, J.},
     interhash = {3c966d9c821798ebcd105e578565ca8d},
     intrahash = {068aca2f2e825b07cb8879b644005a77},
     journal = {Health Education and Behavior},
     month = jan, projects = {eye-tracking, shop2gether},
     title = {What happens when parents and children go grocery shopping? {An} observational study of {Latinos} in {Southern} {California}},
     year = 2016 
    }
  • [PDF] N. Weibel, S. Hwang, S. Rick, E. Sayyari, D. Lenzen, and J. Hollan, “Hands that Speak: An Integrated Approach to Studying Complex Human Communicative Body Movements,” in Proceedings of HICSS-49, Hawaii International Conference on System Sciences, Kauai, HI, USA, 2016.
    [Bibtex]
    @inproceedings{weibel2016hands,
     address = {Kauai, HI, USA},
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Hwang, So-One and Rick, Steven and Sayyari, Erfan and Lenzen, Dan and Hollan, Jim},
     booktitle = {Proceedings of {HICSS}-49, {Hawaii} {International} {Conference} on {System} {Sciences}},
     interhash = {60967491ae65a5a15830ddd630b9bf15},
     intrahash = {57ffb6523faacfb33f10d70862f517fe},
     month = jan, note = {In Press},
     projects = {gestures, sign-language,computational_ethnography},
     title = {Hands that {Speak}: {An} {Integrated} {Approach} to {Studying} {Complex} {Human} {Communicative} {Body} {Movements}},
     year = 2016 
    }
  • [URL] S. Ashfaq, K. M. Bell, M. Difley, S. Mortensen, K. Avery, S. Rick, N. Weibel, B. Pandey, C. Weir, H. S. Hochheiser, Y. Chen, J. Zhang, K. Zhang, R. Street, M. T. Gabuzda, N. Farber, L. Liu, A. Calvitti, and Z. Agha, “Analysis of Computerized Clinical Reminder Activity and Usability Issues,” in Proceedings (Posters) of AMIA 2015, American Medical Informatics Association, Annual Symposium, San Francisco, USA, 2015.
    [Bibtex]
    @inproceedings{ashfaq2015analysis,
     address = {San Francisco, USA},
     area = {pervasive_sensing},
     author = {Ashfaq, Shazia and Bell, Kristin M. and Difley, Megan and Mortensen, Sara and Avery, Kellie and Rick, Steven and Weibel, Nadir and Pandey, Braj and Weir, Charlene and Hochheiser, Harry S. and Chen, Yunan and Zhang, Jing and Zhang, Kai and Street, Richard and Gabuzda, Mark T. and Farber, Neil and Liu, Lin and Calvitti, Alan and Agha, Zia},
     booktitle = {Proceedings ({Posters}) of {AMIA} 2015, {American} {Medical} {Informatics} {Association},
     {Annual} {Symposium}},
     interhash = {aae4c27bbc39c79ee5270e2976ec286d},
     intrahash = {f0bfc4c235a5801a11e9a4d1ca06a4e1},
     month = nov, note = {In Press},
     projects = {quick, medical_informatics},
     title = {Analysis of {Computerized} {Clinical} {Reminder} {Activity} and {Usability} {Issues}},
     url = {http://knowledge.amia.org/59310-amia-1.2741865/t005-1.2744350/f005-1.2744351/2248936-1.2745427/2248936-1.2745428?qr=1},
     year = 2015 
    }
  • [PDF] A. Rule, S. Rick, M. Chiu, P. Rios, S. Ashfaq, A. Calvitti, W. Chan, N. Weibel, and Z. Agha, “Validating free-text order entry for a note-centric EHR,” in Proceedings of AMIA 2015, American Medical Informatics Association, Annual Symposium, San Francisco, USA, 2015.
    [Bibtex]
    @inproceedings{rule2015validating,
     address = {San Francisco, USA},
     area = {pervasive_sensing},
     author = {Rule, Adam and Rick, Steven and Chiu, Michael and Rios, Phillip and Ashfaq, Shazia and Calvitti, Alan and Chan, Wesley and Weibel, Nadir and Agha, Zia},
     booktitle = {Proceedings of {AMIA} 2015, {American} {Medical} {Informatics} {Association},
     {Annual} {Symposium}},
     interhash = {c91f26d1853fe7e9bf187ec08009ae02},
     intrahash = {36adcf2baa5c21ad528c4c68231957a2},
     month = nov, note = {In Press},
     projects = {anotes, medical_informatics},
     title = {Validating free-text order entry for a note-centric {EHR}},
     year = 2015 
    }
  • [URL] J. Zhang, K. Avery, Y. Chen, S. Ashfaq, S. Rick, K. Zhang, N. and Weibel, H. S. Hochheiser, C. Weir, K. M. Bell, M. T. Gabuzda, N. Farber, B. Pandey, A. Calvitti, L. Liu, R. Street, and Z. Agha, “A Preliminary Study on EHR-Associated Extra Workload Among Physicians,” in Proceedings (Posters) of AMIA 2015, American Medical Informatics Association, Annual Symposium, San Francisco, USA, 2015.
    [Bibtex]
    @inproceedings{zhang2015preliminary,
     address = {San Francisco, USA},
     area = {pervasive_sensing},
     author = {Zhang, Jing and Avery, Kellie and Chen, Yunan and Ashfaq, Shazia and Rick, Steven and Zhang, Kai and and Weibel, Nadir and Hochheiser, Harry S. and Weir, Charlene and Bell, Kristin M. and Gabuzda, Mark T. and Farber, Neil and Pandey, Braj and Calvitti, Alan and Liu, Lin and Street, Richard and Agha, Zia},
     booktitle = {Proceedings ({Posters}) of {AMIA} 2015, {American} {Medical} {Informatics} {Association},
     {Annual} {Symposium}},
     interhash = {a5c106803b5f30ad0e873a0c0bcd6eca},
     intrahash = {002b1fa76ca4130834725e35656cd29b},
     month = nov, note = {In Press},
     projects = {quick, medical_informatics},
     title = {A {Preliminary} {Study} on {EHR}-{Associated} {Extra} {Workload} {Among} {Physicians}},
     url = {http://knowledge.amia.org/59310-amia-1.2741865/t005-1.2744350/f005-1.2744351/2248934-1.2744373/2248934-1.2744374},
     year = 2015 
    }
  • [PDF] S. Rick, R. Street, A. Calvitti, S. Ashfaq, Z. Agha, and N. Weibel, “Understanding Patient-Physician Communication and Turn-taking Patterns with Directional Microphone Arrays,” in Abstracts (Oral Presentation) of ICCH 2015, International Conference on Communication in Healthcare, New Orleans, USA, 2015.
    [Bibtex]
    @inproceedings{rick2015understanding,
     address = {New Orleans, USA},
     area = {pervasive_sensing},
     author = {Rick, Steven and Street, Richard and Calvitti, Alan and Ashfaq, Shazia and Agha, Zia and Weibel, Nadir},
     booktitle = {Abstracts ({Oral} {Presentation}) of {ICCH} 2015, {International} {Conference} on {Communication} in {Healthcare}},
     interhash = {a2b31d868775ee8ee5272386a757f381},
     intrahash = {eccdb5dc404abc814cd779729157a660},
     month = oct, note = {In Press},
     projects = {patient-physician communication},
     title = {Understanding {Patient}-{Physician} {Communication} and {Turn}-taking {Patterns} with {Directional} {Microphone} {Arrays}},
     year = 2015 
    }
  • [PDF] N. Thangarajan, N. Green, A. Gupta, S. Little, and N. Weibel, “Analyzing Social Media to Characterize Local HIV At-risk Populations,” in Proceedings of Wireless Health 2015, International Conference on Wireless, Connected and Mobile Health Research, Bethesda, USA, 2015.
    [Bibtex]
    @inproceedings{thangarajan2015analyzing,
     address = {Bethesda, USA},
     area = {pervasive_sensing, data_analysis},
     author = {Thangarajan, Narendran and Green, Nella and Gupta, Amaranth and Little, Susan and Weibel, Nadir},
     booktitle = {Proceedings of {Wireless} {Health} 2015, {International} {Conference} on {Wireless},
     {Connected} and {Mobile} {Health} {Research}},
     interhash = {49f82be02b37218ba544b332fb29cba3},
     intrahash = {04a357ef6b8bfce179474bbc022881eb},
     month = oct, note = {In Press},
     projects = {pircnet},
     title = {Analyzing {Social} {Media} to {Characterize} {Local} {HIV} {At}-risk {Populations}},
     year = 2015 
    }
  • [PDF] S. Rick, A. Calvitti, Z. Agha, and N. Weibel, “Eyes on the Clinic: Accelerating Meaningful Interface Analysis through Unobtrusive Eye Tracking,” in Proceedings of PervasiveHealth 2015, International Conference on Pervasive Computing Technologies for Healthcare, Istanbul, Turkey, 2015.
    [Bibtex]
    @inproceedings{rick2015clinic,
     address = {Istanbul, Turkey},
     area = {pervasive_sensing},
     author = {Rick, Steven and Calvitti, Alan and Agha, Zia and Weibel, Nadir},
     booktitle = {Proceedings of {PervasiveHealth} 2015, {International} {Conference} on {Pervasive} {Computing} {Technologies} for {Healthcare}},
     interhash = {62adea43383b70c3b9ae5dc2bb995bab},
     intrahash = {8f3a3a660d7a9e74258c7178faac8d62},
     month = may, note = {In Press},
     projects = {quick, medical_informatics, computational_ethnography},
     title = {Eyes on the {Clinic}: {Accelerating} {Meaningful} {Interface} {Analysis} through {Unobtrusive} {Eye} {Tracking}},
     year = 2015 
    }
  • [PDF] K. Zheng, D. Hanauer, Z. Agha, and N. Weibel, “Computational Ethnography: Automated and Unobtrusive Means for Collecting Data in situ for Human-Computer Interaction Studies,” in Cognitive Informatics in Health and Biomedicine: Human Computer Interaction in Healthcare, V. L. Patel, T. G. Kannampallil, and D. Kaufman, Eds., Springer, 2015.
    [Bibtex]
    @incollection{zheng2015computational,
     area = {pervasive_sensing},
     author = {Zheng, Kai and Hanauer, David and Agha, Zia and Weibel, Nadir},
     booktitle = {Cognitive {Informatics} in {Health} and {Biomedicine}: {Human} {Computer} {Interaction} in {Healthcare}},
     editor = {Patel, Vimla L. and Kannampallil, Thomas G. and Kaufman, David},
     interhash = {e3273a755240be319b05d3d052dcab02},
     intrahash = {8949ae5ded1b43502b1a6b0a8432e15a},
     month = jan, note = {ISBN 978-3-319-17271-2},
     projects = {quick, patient-physician-communication,computational_ethnography},
     publisher = {Springer},
     title = {Computational {Ethnography}: {Automated} and {Unobtrusive} {Means} for {Collecting} {Data} in situ for {Human}-{Computer} {Interaction} {Studies}},
     year = 2015 
    }
  • [URL] A. Calvitti, N. Weibel, H. Hochheiser, L. Liu, K. Zheng, C. Weir, S. Ashfaq, S. Rick, Z. Agha, and B. Gray, “Can eye tracking and EHR mouse activity tell us when clinicians are overloaded?,” Human Factors Quarterly, Veteran Health Administration, 2014.
    [Bibtex]
    @article{calvitti2014tracking,
     area = {pervasive_sensing},
     author = {Calvitti, Alan and Weibel, Nadir and Hochheiser, Harry and Liu, Lin and Zheng, Kai and Weir, Charlene and Ashfaq, Shazia and Rick, Steven and Agha, Zia and Gray, Barbara},
     interhash = {63723b1b64d631168cf02d4c337bb8c0},
     intrahash = {4395dd0ad3ea36df93a1bcfc83ea5413},
     journal = {Human Factors Quarterly, Veteran Health Administration},
     month = sep, projects = {quick, medical_informatics,computational_ethnography},
     title = {Can eye tracking and {EHR} mouse activity tell us when clinicians are overloaded?},
     url = {https://content.govdelivery.com/accounts/USVHA/bulletins/cfd5d2#article4},
     year = 2014 
    }
  • [PDF] N. Weibel, S. Rick, C. Emmenegger, S. Ashfaq, A. Calvitti, and Z. Agha, “LAB-IN-A-BOX: Semi-Automatic Tracking of Activity in the Medical Office,” Pers Ubiquit Comput – Health, 2014.
    [Bibtex]
    @article{weibel2014labinabox,
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Rick, Steven and Emmenegger, Colleen and Ashfaq, Shazia and Calvitti, Alan and Agha, Zia},
     interhash = {dcecbcd424ffc9f5e032165284e4f13a},
     intrahash = {5da8ceba93930c415bdf7775db44bbff},
     journal = {Pers Ubiquit Comput - Health},
     month = sep, projects = {quick, medical_informatics, stroke-kinect, ergokinect, gestures, kinect, computational_ethnography},
     title = {{LAB}-{IN}-{A}-{BOX}: {Semi}-{Automatic} {Tracking} of {Activity} in the {Medical} {Office}},
     year = 2014 
    }
  • N. Weibel and J. D. Hollan, “Gesture and Action Recognition,” in Abstracts of ISGS 2014, International Society of Gestures Studies 6, San Diego, USA, 2014.
    [Bibtex]
    @inproceedings{weibel2014gesture,
     address = {San Diego, USA},
     area = {pervasive_sensing, ubicomp_health},
     author = {Weibel, Nadir and Hollan, James D.},
     booktitle = {Abstracts of {ISGS} 2014, {International} {Society} of {Gestures} {Studies} 6},
     interhash = {1c8cb58665055cb0eee6f772601d9b70},
     intrahash = {9a629404529a635a9bf2ee5e49489dd9},
     month = jul, note = {Panel on Sensing Technologies},
     projects = {gestures, sign-language},
     title = {Gesture and {Action} {Recognition}},
     year = 2014 
    }
  • [PDF] G. Merchant, N. Weibel, K. Patrick, J. H. Fowler, G. J. Norman, A. Gupta, C. Servetas, K. Calfas, K. Raste, L. Pina, M. Donohue, and S. Marshall, “Click ‘Like’ to change your behavior: A mixed methods study of college students’ exposure to and engagement with Facebook content designed for weight-loss,” Journal of Medical Internet Research, 2014.
    [Bibtex]
    @article{merchant2014click,
     area = {ubicomp_health, pervasive_sensing},
     author = {Merchant, Gina and Weibel, Nadir and Patrick, Kevin and Fowler, James H. and Norman, Greg J. and Gupta, Anjali and Servetas, Christina and Calfas, Karen and Raste, Ketaki and Pina, Laura and Donohue, Mike and Marshall, Simon},
     interhash = {996dc861e949864dae09f7dafa3fcea9},
     intrahash = {8de14708c12962d8fa44e43605ae3058},
     journal = {Journal of Medical Internet Research},
     month = may, projects = {smart, three-two-me},
     title = {Click '{Like}' to change your behavior: {A} mixed methods study of college students' exposure to and engagement with {Facebook} content designed for weight-loss},
     year = 2014 
    }
  • [PDF] G. Merchant, L. Pina, M. Black, E. Bales, N. Weibel, W. Griswold, J. Fowler, and K. Patrick, “Online and face-to-face: How do ad-hoc and existing networks support weight-related behavior change in young adults?,” in Abstracts (Rapid Communication) of SBM 2014, Annual Meeting of the Society of Behavioral Medicine, Philadelphia, USA, 2014.
    [Bibtex]
    @inproceedings{merchant2014online,
     address = {Philadelphia, USA},
     area = {pervasive_sensing, ubicomp_health},
     author = {Merchant, Gina and Pina, Laura and Black, Michelle and Bales, Elizabeth and Weibel, Nadir and Griswold, William and Fowler, James and Patrick, Kevin},
     booktitle = {Abstracts ({Rapid} {Communication}) of {SBM} 2014, {Annual} {Meeting} of the {Society} of {Behavioral} {Medicine}},
     interhash = {4c6a874f02168989fc6e2fb915b0f5f8},
     intrahash = {d62b2dc557f1402e7614907cca1bfc30},
     month = apr, projects = {smart},
     title = {Online and face-to-face: {How} do ad-hoc and existing networks support weight-related behavior change in young adults?},
     year = 2014 
    }
  • [PDF] L. Morency, S. Oviatt, S. Scherer, N. Weibel, and M. Worsley, “ICMI 2013 Grand Challenge Workshop on Multimodal Learning Analytics,” in Proceedings of ICMI 2013, ACM International Conference on Multimodal Interaction, 2013, pp. 373-378.
    [Bibtex]
    @inproceedings{morency2013grand,
     area = {pervasive_sensing, data_analysis},
     author = {Morency, Louis-Philippe and Oviatt, Sharon and Scherer, Stefan and Weibel, Nadir and Worsley, Marcelo},
     booktitle = {Proceedings of {ICMI} 2013, {ACM} {International} {Conference} on {Multimodal} {Interaction}},
     interhash = {0f690df3db7e07be504472f62fd378d3},
     intrahash = {bc4b3afd2639ed4e2f130b00d0f0d1b5},
     month = dec, pages = {373--378},
     projects = {multimodal, mmla},
     title = {{ICMI} 2013 {Grand} {Challenge} {Workshop} on {Multimodal} {Learning} {Analytics}},
     year = 2013 
    }
  • [PDF] S. Oviatt, A. Cohen, and N. Weibel, “Multimodal Learning Analytics: Description of Math Data Corpus for ICMI Grand Challenge Workshop,” in Proceedings of ICMI 2013, ACM International Conference on Multimodal Interaction, 2013, pp. 563-568.
    [Bibtex]
    @inproceedings{oviatt2013multimodal,
     area = {pervasive_sensing, data_analysis},
     author = {Oviatt, Sharon and Cohen, Adrienne and Weibel, Nadir},
     booktitle = {Proceedings of {ICMI} 2013, {ACM} {International} {Conference} on {Multimodal} {Interaction}},
     interhash = {ef7903694c19456298aef43b32493f53},
     intrahash = {2cc0e06fc572819875558469216bc5e0},
     month = dec, pages = {563--568},
     projects = {multimodal, mmla},
     title = {Multimodal {Learning} {Analytics}: {Description} of {Math} {Data} {Corpus} for {ICMI} {Grand} {Challenge} {Workshop}},
     year = 2013 
    }
  • [URL] J. Kerr, N. Weibel, and C. Gurrin, SenseCam ’13: Proceedings of the 4th International SenseCam and Pervasive Imaging Conference, New York, NY, USA: ACM, 2013.
    [Bibtex]
    @book{kerr2013sensecam,
     address = {New York, NY, USA},
     area = {pervasive_sensing},
     author = {Kerr, Jacqueline and Weibel, Nadir and Gurrin, Cathal},
     interhash = {e4ecb4d9c36557cb348a4ae232f0ed9a},
     intrahash = {5aa1bf986871449626788895cac87b00},
     month = nov, note = {ISBN 978-1-4503-2247-8},
     projects = {sensecam},
     publisher = {ACM},
     title = {{SenseCam} '13: {Proceedings} of the 4th {International} {SenseCam} and {Pervasive} {Imaging} {Conference}},
     url = {http://dl.acm.org/citation.cfm?id=2526667},
     year = 2013 
    }
  • [PDF] N. Weibel, S. Ashfaq, A. Calvitti, J. D. Hollan, and Z. Agha, “Multimodal Data Analysis and Visualization to Study Usability of Electronic Health Records,” in Proceedings of PervasiveHealth 2013, International Conference on Pervasive Computing Technologies for Healthcare (Poster Track), Venice, Italy, 2013, pp. 282-283.
    [Bibtex]
    @inproceedings{weibel2013multimodal,
     address = {Venice, Italy},
     area = {pervasive_sensing},
     author = {Weibel, Nadir and Ashfaq, Shazia and Calvitti, Alan and Hollan, James D. and Agha, Zia},
     booktitle = {Proceedings of {PervasiveHealth} 2013, {International} {Conference} on {Pervasive} {Computing} {Technologies} for {Healthcare} ({Poster} {Track})},
     interhash = {ed3d21b7605e575861886eb709ac8b45},
     intrahash = {450e0fa1576bbef68ac5c693b74338e1},
     month = may, pages = {282--283},
     projects = {quick, medical_informatics,computational_ethnography},
     title = {Multimodal {Data} {Analysis} and {Visualization} to {Study} {Usability} of {Electronic} {Health} {Records}},
     year = 2013 
    }
  • [PDF] J. Lyons, R. Dixit, C. Emmenegger, L. H. Hill, N. Weibel, and J. D. Hollan, “Factors Affecting Physician-Patient Communication in the Medical Exam Room,” in Proceedings of HCI International 2013, 15th International Conference on Human-Computer Interaction, Las Vegas, NV, USA, 2013, pp. 187-191.
    [Bibtex]
    @inproceedings{lyons2013factors,
     address = {Las Vegas, NV, USA},
     area = {pervasive_sensing},
     author = {Lyons, Jennifer and Dixit, Ram and Emmenegger, Colleen and Hill, Lind H. and Weibel, Nadir and Hollan, James D.},
     booktitle = {Proceedings of {HCI} {International} 2013, 15th {International} {Conference} on {Human}-{Computer} {Interaction}},
     interhash = {11b85d5cdf44f6e5cd43ba76114d6703},
     intrahash = {aa25d6f6611fa95b05602934dd450066},
     pages = {187--191},
     projects = {patient-physician-communication},
     title = {Factors {Affecting} {Physician}-{Patient} {Communication} in the {Medical} {Exam} {Room}},
     year = 2013 
    }
  • [PDF] S. Scherer, N. Weibel, S. Oviatt, and L. Morency, “Multimodal prediction of expertise and leadership in learning groups,” in Proc. Multimodal Learning Analytics Workshop at ICMI’12, ACM International Conference on Multimodal Interaction, 2012.
    [Bibtex]
    @inproceedings{scherer2012multimodal,
     area = {pervasive_sensing, data_analysis},
     author = {Scherer, Stefan and Weibel, Nadir and Oviatt, Sharon and Morency, Louis-Philippe},
     booktitle = {Proc. {Multimodal} {Learning} {Analytics} {Workshop} at {ICMI}'12, {ACM} {International} {Conference} on {Multimodal} {Interaction}},
     interhash = {631d97ad229bd0f6a3a4cf8e52c658cf},
     intrahash = {fc09d2dfd708d643e5051922bc56ece4},
     month = oct, note = {In Press},
     projects = {multimodal, mmla},
     title = {Multimodal prediction of expertise and leadership in learning groups},
     year = 2012 
    }
  • [PDF] A. Fouse, N. Weibel, E. Hutchins, and J. D. Hollan, “ChronoViz: A System for Supporting Navigation of Time-coded Data,” in Extended Abstracts of CHI 2011, ACM Conference on Human Factors in Computing Systems, Vancouver, Canada, 2011, pp. 299-304.
    [Bibtex]
    @inproceedings{fouse2011chronoviz,
     address = {Vancouver, Canada},
     area = {data_analysis, pervasive_sensing},
     author = {Fouse, Adam and Weibel, Nadir and Hutchins, Edwin and Hollan, James D.},
     booktitle = {Extended {Abstracts} of {CHI} 2011, {ACM} {Conference} on {Human} {Factors} in {Computing} {Systems}},
     interhash = {abcde7539c9ff5d0de07bb07dfa637a2},
     intrahash = {69e79b23240d2ae52fe7548676a72cd8},
     month = may, pages = {299--304},
     projects = {chronoviz},
     title = {{ChronoViz}: {A} {System} for {Supporting} {Navigation} of {Time}-coded {Data}},
     year = 2011 
    }

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