In this UbiComp 2016 workshop, we are hoping to bring researchers together to discuss various requirements, opportunities, challenges and next steps in developing a holistic approach for sensing and intervention in the context of mental health.
Find Out MoreThis workshop has already taken place. For the current year's workshop, please go here.
Mental illness is becoming an increasingly pressing health care issue. Around a quarter of US adults suffer from a diagnosable mental health problem every year. These issues are particularly prevalent among younger generations.
But, often these mental health issues remain untreated and undiagnosed. Traditional use of manual survey as a monitoring tool frequently fails to record the crucial and subtle behavioral and contextual cues that might be indicative of illness onset. More importantly, due to lack of longitudinal data from individuals, the interventions are often not personalized in nature resulting in suboptimal outcomes.
Recent work from the UbiComp community has shown that mobile phone based computational platforms can be used to overcome some of these limitations. However, before such technologies can be deployed and used for the detection and prevention of mental health problems, a number of key challenges needs to be addressed. In this workshop, we plan to tackle some of these issues by exploring novel technologies, analysis methodologies and design techniques. We are hoping to bring together researchers interested in developing and deploying mobile systems in the context of mental health. We wish to provide a common forum to share recent findings and insights in developing and deploying such systems.
In particular we will invite to submit papers in the following areas:
Design and implementation of mobile phone based computational platforms to collect health and mental well-being data.
Integration of multimodal data from different sensor streams for personalized predictive modeling.
Automated inference of high-level contexts relevant to mental health and well-being from sensor data.
Developing robust behavioral models that can handle data sparsity and mis-labeling issues.
Designing and implementing feedback and visualization for both participants and caregivers.
Development of smartphone based automated behavioral interventions focusing on mental health and well-being.
The deadline for submission is June 14, 2016 (11:59 PDT)
Regular (up to 9 pages) or short (up to 5 pages) paper using SIGCHI Extended Abstract format. Papers should be in PDF format and not anonymized.
Submissions can be made at https://easychair.org/conferences/?conf=mhsi2016.
We encourage submissions of work-in-progress papers.
Jakob E. Bardram, Technical University of Denmark
Laura E. Barnes, University of Virginia
Masooda Bashir, University of Illinois at Urbana-Champaign
Amy Bauer, University of Washington
Michael L. Birnbaum, Hofstra Northwell School of Medicine
Steven Chan, University of California, Davis
Orianna DeMasi, University of California, Berkeley
Stephen M Schueller, Northwestern University
John Torous, Harvard Medical School
Gabriela Marcu, Drexel University
Amir Muaremi, Stanford University
Elizabeth L. Murnane, Cornell University
Mirco Musolesi, University College London
Steven Vannoy, University of Massachusetts Boston
Mi Zhang, Michigan State University
