{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T03:18:56Z","timestamp":1762917536905,"version":"build-2065373602"},"reference-count":15,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2014,9,2]],"date-time":"2014-09-02T00:00:00Z","timestamp":1409616000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Activity recognition for the purposes of recognizing a user\u2019s intentions using multimodal sensors is becoming a widely researched topic largely based on the prevalence of the smartphone. Previous studies have reported the difficulty in recognizing life-logs by only using a smartphone due to the challenges with activity modeling and real-time recognition. In addition, recognizing life-logs is difficult due to the absence of an established framework which enables the use of different sources of sensor data. In this paper, we propose a smartphone-based Hierarchical Activity Recognition Framework which extends the Na\u00efve Bayes approach for the processing of activity modeling and  real-time activity recognition. The proposed algorithm demonstrates higher accuracy than the Na\u00efve Bayes approach and also enables the recognition of a user\u2019s activities within a mobile environment. The proposed algorithm has the ability to classify fifteen activities with an average classification accuracy of 92.96%.<\/jats:p>","DOI":"10.3390\/s140916181","type":"journal-article","created":{"date-parts":[[2014,9,2]],"date-time":"2014-09-02T10:12:38Z","timestamp":1409652758000},"page":"16181-16195","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["A Lightweight Hierarchical Activity Recognition Framework Using Smartphone Sensors"],"prefix":"10.3390","volume":"14","author":[{"given":"Manhyung","family":"Han","sequence":"first","affiliation":[{"name":"Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University,  1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3675-2258","authenticated-orcid":false,"given":"Jae","family":"Bang","sequence":"additional","affiliation":[{"name":"Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University,  1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0882-7902","authenticated-orcid":false,"given":"Chris","family":"Nugent","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematics, Computer Science Research Institute, University of Ulster, Newtownabbey, Co. Antrim, BT37 0QB, UK"}]},{"given":"Sally","family":"McClean","sequence":"additional","affiliation":[{"name":"School of Computing and Information Engineering, University of Ulster, Coleraine, Co. Londonderry, BT52 1SA, UK"}]},{"given":"Sungyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Ubiquitous Computing Laboratory, Department of Computer Engineering, Kyung Hee University,  1 Seocheon-Dong, Giheung-Gu, Yongin-Si, Gyeonggi-Do 446-701, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2014,9,2]]},"reference":[{"key":"ref_1","unstructured":"Bao, L., and Intille, S.S. (2004). PERVASIVE 2004, Springer-Verlag."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Omatu, S., Rocha, M., Bravo, J., Fern\u00e1ndez, F., Corchado, E., Bustillo, A., and Corchado, J. (2009). 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Dublin, Ireland.","DOI":"10.1007\/11748625_1"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Minnen, D., Starner, T., Ward, J.A., Lukowicz, P., and Tr\u00f6ster, G. (2005, January 6). Recognizing and discovering human actions from on-body sensor data.","DOI":"10.1109\/ISWC.2006.286337"},{"key":"ref_14","unstructured":"Lau, S.L., and David, K. (2010, January 16\u201318). Movement recognition using the accelerometer in smartphones. Florence, Italy."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1553","DOI":"10.1109\/TPAMI.2006.197","article-title":"Activity recognition of assembly tasks using body-worn microphones and accelerometers","volume":"28","author":"Ward","year":"2006","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/9\/16181\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T21:15:29Z","timestamp":1760217329000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/14\/9\/16181"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2014,9,2]]},"references-count":15,"journal-issue":{"issue":"9","published-online":{"date-parts":[[2014,9]]}},"alternative-id":["s140916181"],"URL":"https:\/\/doi.org\/10.3390\/s140916181","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2014,9,2]]}}}