Inferring Human Activity in Mobile Devices by Computing Multiple Contexts
AbstractThis 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 demonstrate that the multi-context solution significantly outperforms the spatial-context-only solution. A classification accuracy of 61.7% is achieved for the spatial-context-only solution, while 88.8% is achieved for the multi-context solution. View Full-Text
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Chen, R.; Chu, T.; Liu, K.; Liu, J.; Chen, Y. Inferring Human Activity in Mobile Devices by Computing Multiple Contexts. Sensors 2015, 15, 21219-21238.
Chen R, Chu T, Liu K, Liu J, Chen Y. Inferring Human Activity in Mobile Devices by Computing Multiple Contexts. Sensors. 2015; 15(9):21219-21238.Chicago/Turabian Style
Chen, Ruizhi; Chu, Tianxing; Liu, Keqiang; Liu, Jingbin; Chen, Yuwei. 2015. "Inferring Human Activity in Mobile Devices by Computing Multiple Contexts." Sensors 15, no. 9: 21219-21238.