Inferring human activity in mobile devices by computing multiple contexts
| dc.contributor.author | Chen, Ruizhi | |
| dc.contributor.author | Chu, Tianxing | |
| dc.contributor.author | Liu, Keqiang | |
| dc.contributor.author | Liu, Jingbin | |
| dc.contributor.author | Chen, Yuwei | |
| dc.creator.orcid | https://orcid.org/0000-0003-0148-3609 | en_US |
| dc.creator.orcid | https://orcid.org/0000-0003-0148-3609 | |
| dc.creator.orcid | https://orcid.org/0000-0003-0148-3609https://orcid.org/0000-0003-0148-3609 | |
| dc.creator.orcid | https://orcid.org/0000-0003-0148-3609 | |
| dc.creator.orcid | https://orcid.org/0000-0003-0148-3609 | |
| dc.date.accessioned | 2021-10-28T19:15:55Z | |
| dc.date.available | 2021-10-28T19:15:55Z | |
| dc.date.issued | 2015-08-28 | |
| dc.description.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 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. | en_US |
| dc.description.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 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. | |
| dc.identifier.citation | Chen, R., Chu, T., Liu, K., Liu, J. and Chen, Y., 2015. Inferring human activity in mobile devices by computing multiple contexts. Sensors, 15(9), pp.21219-21238. | en_US |
| dc.identifier.citation | Chen, R., Chu, T., Liu, K., Liu, J. and Chen, Y., 2015. Inferring human activity in mobile devices by computing multiple contexts. Sensors, 15(9), pp.21219-21238. | |
| dc.identifier.doi | https://doi.org/10.3390/s150921219 | |
| dc.identifier.uri | https://hdl.handle.net/1969.6/89920 | |
| dc.language.iso | en_US | en_US |
| dc.language.iso | en_US | |
| dc.publisher | MDPI | en_US |
| dc.publisher | MDPI | |
| dc.rights | Attribution 4.0 International | * |
| dc.rights | Attribution 4.0 International | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | human activity recognition | en_US |
| dc.subject | mobile context computation | en_US |
| dc.subject | location awareness | en_US |
| dc.subject | smartphone positioning | en_US |
| dc.subject | human activity recognition | |
| dc.subject | mobile context computation | |
| dc.subject | location awareness | |
| dc.subject | smartphone positioning | |
| dc.title | Inferring human activity in mobile devices by computing multiple contexts | en_US |
| dc.title | Inferring human activity in mobile devices by computing multiple contexts | |
| dc.type | Article | en_US |
| dc.type | Article |