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Many existing techniques, such as deep learning, have been developed for specific activity recognition, but little for the recognition of the transitions between activities. This work proposes a deep learning based scheme that can recognize both specific activities and the transitions between two different activities of short duration and low frequency for health care applications. In this work, we first build a deep convolutional neural network (CNN) for extracting features from the data collected by sensors. Then, the long short-term memory (LTSM) network is used to capture long-term dependencies between two actions to further improve the HAR identification rate. By combing CNN and LSTM, a wearable sensor based model is proposed that can accurately recognize activities and their transitions. The experimental results show that the proposed approach can help improve the recognition rate up to 95.87% and the recognition rate for transitions higher than 80%, which are better than those of most existing similar models over the open HAPT dataset.<\/jats:p>","DOI":"10.1155\/2020\/2132138","type":"journal-article","created":{"date-parts":[[2020,7,27]],"date-time":"2020-07-27T23:31:53Z","timestamp":1595892713000},"page":"1-12","source":"Crossref","is-referenced-by-count":87,"title":["Wearable Sensor-Based Human Activity Recognition Using Hybrid Deep Learning Techniques"],"prefix":"10.1155","volume":"2020","author":[{"given":"Huaijun","family":"Wang","sequence":"first","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory for Network Computing and Security Technology, Xi\u2019an 710048, China"}]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5483-5175","authenticated-orcid":true,"given":"Junhuai","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory for Network Computing and Security Technology, Xi\u2019an 710048, China"}]},{"given":"Ling","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Pengjia","family":"Tu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Ting","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory for Network Computing and Security Technology, Xi\u2019an 710048, China"}]},{"given":"Yang","family":"An","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"}]},{"given":"Kan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering, Xi\u2019an University of Technology, Xi\u2019an 710048, China"},{"name":"Shaanxi Key Laboratory for Network Computing and Security Technology, Xi\u2019an 710048, China"}]},{"given":"Shancang","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Creative Technologies, UWE Bristol, Bristol BS16 1QY, UK"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1109\/jsen.2016.2609392"},{"key":"2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2015.08.096"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1109\/surv.2012.110112.00192"},{"key":"5","doi-asserted-by":"publisher","DOI":"10.3390\/s16010115"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.1109\/lra.2019.2895266"},{"key":"7","first-page":"7","volume":"56","year":"2019","journal-title":"Laser Optoelectron. 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