Activity Sensing in the Wild: A Field Trial of UbiFit Garden.
Proceedings of the 26th Annual ACM Conference on Human Factors in Computing Systems
ACM Digital Library
Place of Publication
Recent advances in small inexpensive sensors, low-power processing, and activity modeling have enabled applications that use on-body sensing and machine learning to infer people’s activities throughout everyday life. To address the growing rate of sedentary lifestyles, we have developed a system, UbiFit Garden, which uses these technologies and a personal, mobile display to encourage physical activity. We conducted a 3-week field trial in which 12 participants used the system and report findings focusing on their experiences with the sensing and activity inference. We discuss key implications for systems that use on-body sensing and activity inference to encourage physical activity.
Science and Technology Studies
Sunny Consolvo, David W. McDonald, Tammy R. Toscos, Mike Y. Chen, Jon Froehlich, Beverly Harrison, Predrag Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby, Ian Smith, and James A. Landay (2008).
Activity Sensing in the Wild: A Field Trial of UbiFit Garden.. Proceedings of the 26th Annual ACM Conference on Human Factors in Computing Systems. 1797-1806. New York: ACM Digital Library.
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