Inferring Human Activity in Mobile Devices by Computing Multiple Contexts

Sensors
Ruizhi ChenYuwei Chen

Abstract

This paper introduces a framework for inferring human activities in mobile devices by computing spatial contexts, temporal contexts, spatiotemporal contexts, and user contexts. A spatial context is a significant location that is defined as a geofence, which can be a node associated with a circle, or a polygon; a temporal context contains time-related information that can be e.g., a local time tag, a time difference between geographical locations, or a timespan; a spatiotemporal context is defined as a dwelling length at a particular spatial context; and a user context includes user-related information that can be the user's mobility contexts, environmental contexts, psychological contexts or social contexts. Using the measurements of the built-in sensors and radio signals in mobile devices, we can snapshot a contextual tuple for every second including aforementioned contexts. Giving a contextual tuple, the framework evaluates the posteriori probability of each candidate activity in real-time using a Naïve Bayes classifier. A large dataset containing 710,436 contextual tuples has been recorded for one week from an experiment carried out at Texas A&M University Corpus Christi with three participants. The test results demonstr...Continue Reading

References

May 9, 2008·Journal of Medical Systems·Gye Hwan JinTae Soo Lee
Dec 14, 2012·Sensors·Jingbin LiuHeidi Kuusniemi
Jan 26, 2013·Sensors·Ling PeiJyrki Kaistinen
Jan 26, 2013·Sensors·Melania SusiGérard Lachapelle
Oct 1, 2013·Gerontechnology : International Journal on the Fundamental Aspects of Technology to Serve the Ageing Society·Lawrence B Holder, Diane J Cook

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