Inferring Human Activity in Mobile Devices by Computing Multiple Contexts.
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&amp;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.
Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412-5868, USA. ruizhi.chen@tamucc.edu.
Ruizhi
R
Chen
Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412-5868, USA. tianxing.chu@tamucc.edu.
Tianxing
T
Chu
Conrad Blucher Institute for Surveying & Science, Texas A&M University Corpus Christi, Corpus Christi, TX 78412-5868, USA. keqiang.liu@tamucc.edu.|School of Environment Science and Spatial Informatics, China University of Mining and Technology, Xuzhou 221116, China. keqiang.liu@tamucc.edu.
Keqiang
K
Liu
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Masala 02431, Finland. jingbin.liu@nls.fi.
Jingbin
J
Liu
Department of Remote Sensing and Photogrammetry, Finnish Geospatial Research Institute, National Land Survey of Finland, Masala 02431, Finland. yuwei.chen@nls.fi.
Yuwei
Y
Chen
human activity recognition; location awareness; mobile context computation; smartphone positioning