Takamitsu Mizutori
ABSTRACT
In this paper, we propose a way for mobile applications to recognize the daily spatial behavior of a user in the duration of a day. A feature representation of the user's spatial behavior is created from the accumulation of GPS location data logged in the user's everyday lives. By referencing this representation - called "Behavior Map", mobile applications could infer a path the user will take, and behave proactively for locations where the user will be in.
- 1. Downs, R. M., and Stea, D., 1973, Cognitive maps and spatial behavior. In Image and Environment, pages 8-26, Chicago, IL, U.S.A., 1973. Aldine Publishing Company.Google Scholar
- 2. Harter, A., Hopper, A., Steggles, P., Ward, A., and Webster, P., 1999, The anatomy of a context-aware application. In Proceedings of the Fifth Annual ACM/IEEE International Conference on Mobile Computing and Networking, MOBICOM'99, pages 59-68, Seattle, WA, U.S.A., August 1999. ACM Press. Google ScholarDigital Library
- 3. Jebara, T., Schiele, B., Oliver, N., and Pentland, A., 1999, DyPERS: A Dynamic and Personal Enhanced Reality System. Appeared in the Second International Symposium on Wearable Computing, Pittsburgh, PA, U.S.A., October 1999. IEEE Computer Society.Google Scholar
- 4. Kohonen, T., 1995, Self-Organizing Maps, Berlin, Germany, 1995. Springer-Verlag. Google ScholarDigital Library
- 5. Kuipers, B., 1982, The Map in the Head Metaphor, Environment and Behavior, 14(2):202- 220, 1982.Google ScholarCross Ref
- 6. Rhodes, B., 1997, The Wearable Remembrance Agent: a system for augmented memory. In Proceedings of the First International Symposium on Wearable Computing, pages 123- 128, Cambridge, MA, U.S.A., October 1997. IEEE Computer Society. Google ScholarDigital Library
- 7. Sasaki, I., Goda, K., Kogawa, K., H. Tarumi, 2003, Evaluation of the Space Tag System. IPSJ SIG Technical Reports, GN47-5, 2003, Information Processing Society of Japan.Google Scholar
Index Terms
- Representing human spatial behavior by self-organizing networks
Recommendations
Self-organising object networks using context zones for distributed activity recognition
BodyNets '07: Proceedings of the ICST 2nd international conference on Body area networksActivity recognition has a high applicability scope in patient monitoring since it has the potential to observe patients' actions and recognise erratic behaviour. Our activity recognition architecture described in this paper is particularly suited for ...
Understanding Home Inactivity for Human Behavior Anomaly Detection
GoodTechs '19: Proceedings of the 5th EAI International Conference on Smart Objects and Technologies for Social GoodThe importance of Ambient Assisted Living (AAL) systems as a new paradigm to provide assistance to independent older adults at home is constantly growing. In this context, one of the most demanded feature is the possibility to promptly alert caregivers ...
The human behaviour indicator: A measure of behavioural evolution
Highlights- Introducing behavioural evolution representing progressive changes in activities.
AbstractActivities of daily living (ADL) or activities of daily working (ADW) may be affected by changes in a person’s health or well-being. Measuring progressive changes in one activity or multiple activities is representative of behavioural ...
Comments