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The proposed design consists of three Deep Neural Networks (DNNs): a 2D Convolutional Neural Network (CNN) as the recognition algorithm, a 1D CNN as the state machine, and a reinforcement learning agent for neural architecture search. The recognition algorithm learns location- and person-independent features from different perspectives of CSI data. The state machine learns temporal dependency information from history classification results. The reinforcement learning agent optimizes the neural architecture of the recognition algorithm using a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM). The proposed design is evaluated in a lab environment with different WiFi device locations, antenna orientations, sitting\/standing\/walking locations\/orientations, and multiple persons. The proposed design has 97% average accuracy when testing devices and persons are not seen during training. The proposed design is also evaluated by two public datasets with accuracy of 80% and 83%. The proposed design needs very little human efforts for ground truth labeling, feature engineering, signal processing, and tuning of learning parameters and hyperparameters.<\/jats:p>","DOI":"10.1145\/3424739","type":"journal-article","created":{"date-parts":[[2021,1,21]],"date-time":"2021-01-21T17:25:39Z","timestamp":1611249939000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":61,"title":["Location- and Person-Independent Activity Recognition with WiFi, Deep Neural Networks, and Reinforcement Learning"],"prefix":"10.1145","volume":"2","author":[{"given":"Yongsen","family":"Ma","sequence":"first","affiliation":[{"name":"William 8 Mary, Williamsburg, VA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheheryar","family":"Arshad","sequence":"additional","affiliation":[{"name":"University of Texas at Arlington, Arlington, TX, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Swetha","family":"Muniraju","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Sunnyvale, CA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric","family":"Torkildson","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Sunnyvale, CA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Enrico","family":"Rantala","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Sunnyvale, CA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Klaus","family":"Doppler","sequence":"additional","affiliation":[{"name":"Nokia Bell Labs, Sunnyvale, CA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gang","family":"Zhou","sequence":"additional","affiliation":[{"name":"William 8 Mary, Williamsburg, VA, US"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,1,21]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2789168.2790109"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSAC.2017.2680998"},{"key":"e_1_2_1_3_1","volume-title":"Proceedings of the IEEE 18th International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM\u201917)","author":"Arshad Sheheryar","year":"2017","unstructured":"Sheheryar Arshad , Chunhai Feng , Yonghe Liu , Yupeng Hu , Ruiyun Yu , Siwang Zhou , and Heng Li . 2017 . 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