The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Activity sensing in the wild: a field trial of ubifit garden
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.
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(2016) · PubMed Central
Metadata
- Authors
- Consolvo, S., McDonald, D. W., Toscos, T., Chen, M. Y., Froehlich, J., Harrison, B., Klasnja, P., LaMarca, A., LeGrand, L., Libby, R., Smith, I., & Landay, J. A.
- Venue
- Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
- Publisher
- ACM
- Published
- 2008-04-06
- Pages
- 1797-1806
- OpenAlex
- W2156221064
- Cited by
- 1,131
- References
- 4
- FWCI
- 44.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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