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Issue No. 01 - Jan.-Mar. (2017 vol. 16)
ISSN: 1536-1268
pp: 74-84
Eun Kyoung Choe , Pennsylvania State University
Saeed Abdullah , Cornell University
Mashfiqui Rabbi , University of Michigan
Edison Thomaz , The University of Texas at Austin
Daniel A. Epstein , University of Washington
Felicia Cordeiro , University of Washington
Matthew Kay , University of Michigan
Gregory D. Abowd , Georgia Institute of Technology
Tanzeem Choudhury , Cornell University
James Fogarty , University of Washington
Bongshin Lee , Microsoft Research
Mark Matthews , Cornell University
Julie A. Kientz , University of Washington
The authors present an approach for designing self-monitoring technology called "semi-automated tracking," which combines both manual and automated data collection methods. Through this approach, they aim to lower the capture burdens, collect data that is typically hard to track automatically, and promote awareness to help people achieve their self-monitoring goals. They first specify three design considerations for semi-automated tracking: data capture feasibility, the purpose of self-monitoring, and the motivation level. They then provide examples of semi-automated tracking applications in the domains of sleep, mood, and food tracking to demonstrate strategies they developed to find the right balance between manual tracking and automated tracking, combining each of their benefits while minimizing their associated limitations.
Monitoring, Sensors, Insulation life, Data collection, Mood tracking, Pervasive computing, Medical devices, Internet of things, Informatics,data analysis, self-monitoring, semi-automated tracking, personal informatics, sleep tracking, food tracking, mood tracking, pervasive computing, healthcare, mobile, Internet of Things, bioinformatics
Eun Kyoung Choe, Saeed Abdullah, Mashfiqui Rabbi, Edison Thomaz, Daniel A. Epstein, Felicia Cordeiro, Matthew Kay, Gregory D. Abowd, Tanzeem Choudhury, James Fogarty, Bongshin Lee, Mark Matthews, Julie A. Kientz, "Semi-Automated Tracking: A Balanced Approach for Self-Monitoring Applications", IEEE Pervasive Computing, vol. 16, no. , pp. 74-84, Jan.-Mar. 2017, doi:10.1109/MPRV.2017.18
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