Activity sensing in the wild: a field trial of ubifit garden

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. Author Keywords persuasive technology, sensing, activity inference, mobile phone, ambient display, fitness, activity-based applications. ACM Classification Keywords H.5.2 User Interfaces, H.5.m Miscellaneous.

• Type: Conference
• Year: 2008
• Where: CHI
• Authors: Sunny Consolvo, David W. McDonald, Tammy Toscos, Mike Y. Chen, Jon Froehlich, Beverly L. Harrison, Predrag V. Klasnja, Anthony LaMarca, Louis LeGrand, Ryan Libby, Ian E. Smith, James A. Landay
• Keywords: Activity Inference, Activity Modeling, CHI 2008, Human Computer Interaction, Physical Activity