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

  • Authors:

  • Sunny Consolvo;David W. McDonald;Tammy Toscos;Mike Y. Chen;Jon Froehlich;Beverly Harrison;Predrag Klasnja;Anthony LaMarca;Louis LeGrand;Ryan Libby;Ian Smith;James A. Landay

  • Affiliations:

  • Intel Research Seattle, Seattle, WA, USA;University of Washington, Seattle, USA;Intel Resear h Seattle, Seattle, USA;Intel Research Seattle, Seattle, USA;University of Washington, Seattle, USA;Intel Research Seattle, Seattle, USA;Intel Research Seattle, Seattle, USA;Intel Research Seattle, Seattle, USA;Intel Research Seattle, Seattle, USA;University of Washington, Seattle, USA;Intel Research Seattle, Seattle, USA;University of Washington, Seattle, USA

  • Venue:
  • Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
  • Year:
  • 2008

Quantified Score

Hi-index 0.01

Visualization

Abstract

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.