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| Title | Inferring user activities from spatial-temporal data in mobile phones |
| Creator | 徐國偉 Njoo, G.S. Ruan, X.W. Hsu, Kuo-Wei Peng, W.-C. |
| Contributor | 資訊科學系 |
| Key Words | Cellular telephones; Classification (of information); Mobile phones; Semantics; Telephone sets; Wearable computers; Wearable technology; Wi-Fi; Activity inference; Computing applications; Geographical features; Location-based social networks; Semantic features; Spatial temporals; Spatial-temporal data; Temporal features; Ubiquitous computing |
| Date | 2015-09 |
| Date Issued | 10-Aug-2017 15:14:39 (UTC+8) |
| Summary | Activity inference is a key to the development of various ubiquitous computing applications. Here, we observe that users perform several actions in their mobile phone: take photos, perform check-in, and access Wi-Fi networks. These behaviors generate spatial-temporal data that could be utilized to capture user activities. Hence, three features are extracted for activities inference: 1) geographical feature: indicating where user performs activities; 2) temporal feature: indicating when user performs activities; and 3) semantic feature: showing semantic concept of a place from location-based social networks. Here, we propose Spatial-Temporal Activity Inference Model (STAIM) to infer users` activities from aforementioned features. Experimental results show that STAIM is able to effectively infer users` activities, achieving 75% accuracy on average. Copyright 2015 © ACM. |
| Relation | UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers, 65-68 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015; Osaka; Japan; 7 September 2015 到 11 September 2015; 代碼 118356 |
| Type | conference |
| DOI | http://dx.doi.org/10.1145/2800835.2800868 |
| dc.contributor | 資訊科學系 | zh_Tw |
| dc.creator (作者) | 徐國偉 | zh_TW |
| dc.creator (作者) | Njoo, G.S. | en_US |
| dc.creator (作者) | Ruan, X.W. | en_US |
| dc.creator (作者) | Hsu, Kuo-Wei | en_US |
| dc.creator (作者) | Peng, W.-C. | en_US |
| dc.date (日期) | 2015-09 | en_US |
| dc.date.accessioned | 10-Aug-2017 15:14:39 (UTC+8) | - |
| dc.date.available | 10-Aug-2017 15:14:39 (UTC+8) | - |
| dc.date.issued (上傳時間) | 10-Aug-2017 15:14:39 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/111898 | - |
| dc.description.abstract (摘要) | Activity inference is a key to the development of various ubiquitous computing applications. Here, we observe that users perform several actions in their mobile phone: take photos, perform check-in, and access Wi-Fi networks. These behaviors generate spatial-temporal data that could be utilized to capture user activities. Hence, three features are extracted for activities inference: 1) geographical feature: indicating where user performs activities; 2) temporal feature: indicating when user performs activities; and 3) semantic feature: showing semantic concept of a place from location-based social networks. Here, we propose Spatial-Temporal Activity Inference Model (STAIM) to infer users` activities from aforementioned features. Experimental results show that STAIM is able to effectively infer users` activities, achieving 75% accuracy on average. Copyright 2015 © ACM. | en_US |
| dc.format.extent | 437359 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | UbiComp and ISWC 2015 - Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the Proceedings of the 2015 ACM International Symposium on Wearable Computers, 65-68 | en_US |
| dc.relation (關聯) | ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2015 ACM International Symposium on Wearable Computers, UbiComp and ISWC 2015; Osaka; Japan; 7 September 2015 到 11 September 2015; 代碼 118356 | en_US |
| dc.subject (關鍵詞) | Cellular telephones; Classification (of information); Mobile phones; Semantics; Telephone sets; Wearable computers; Wearable technology; Wi-Fi; Activity inference; Computing applications; Geographical features; Location-based social networks; Semantic features; Spatial temporals; Spatial-temporal data; Temporal features; Ubiquitous computing | en_US |
| dc.title (題名) | Inferring user activities from spatial-temporal data in mobile phones | en_US |
| dc.type (資料類型) | conference | |
| dc.identifier.doi (DOI) | 10.1145/2800835.2800868 | |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1145/2800835.2800868 |