A integrated Internet-of-Things system for sensing, rating, and reflecting on how older adults carry out tasks for indepedence. It combines unobtrusive sensing, heuristic-based activity recognition, and multiple forms of feedback mechanisms based on behavioral theory to empower individuals with greater self awareness of their abilities and empower clinicians and caregivers with early, actionable signs of cognitive or functional decline in older adults.
Lee, M.L. and Dey, A.K. 2014. Real-time Feedback to Improve Medication Taking. CHI 2014 (Best Paper Award) (abstract) (pdf)
Lee, M.L. and Dey, A.K. 2014. Sensor-based Observations of Daily Living for Aging in Place. Personal and Ubiquitous Computing. September 2014, pages 1-17. (abstract)
Lee, M.L. Task-based Embedded Assessment of Functional Abilities for Older Adults Doctoral Dissertation, August 2012 (abstract) (pdf)
Lee, M.L. and Dey, A.K. 2011. Reflecting on Pills and Phone Use: Supporting Self-Awareness of Functional Abilities for Older Adults. CHI 2011 (Best Paper Honorable Mention) (pdf)
Lee, M.L. and Dey, A.K. 2011. Smart Lifelogging Technology for Episodic Memory Support. In Smart Healthcare Applications and Services: Developments and Practices. Röcker, C. & Ziefle, M. (Eds.) IGI Global, 2011. (link)
Lee, M.L. and Dey, A.K. 2010. Embedded Assessment of Aging Adults: A Concept Validation. In Proceedings of PervasiveHealth 2010.
Lee, M.L. 2010. Creating Salient Summaries of Home Activity Lifelog Data. CHI 2010 Doctoral Consortium. (pdf)
"CMU to research sensors to track elderly" (Pittsburgh Post-Gazette)
"Testing The Ability Of Embedded Sensors To Detect Onset Of Dementia, Infirmity" (Medical News Today)
"High tech helps elderly, impaired" (Pittsburgh Tribune-Review)