{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T15:02:42Z","timestamp":1782313362915,"version":"3.54.5"},"reference-count":43,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2018,2,24]],"date-time":"2018-02-24T00:00:00Z","timestamp":1519430400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["16SV7223K"],"award-info":[{"award-number":["16SV7223K"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002347","name":"Bundesministerium f\u00fcr Bildung und Forschung","doi-asserted-by":"publisher","award":["16SV7512"],"award-info":[{"award-number":["16SV7512"]}],"id":[{"id":"10.13039\/501100002347","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["GRK1564"],"award-info":[{"award-number":["GRK1564"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Getting a good feature representation of data is paramount for Human Activity Recognition (HAR) using wearable sensors. An increasing number of feature learning approaches\u2014in particular deep-learning based\u2014have been proposed to extract an effective feature representation by analyzing large amounts of data. However, getting an objective interpretation of their performances faces two problems: the lack of a baseline evaluation setup, which makes a strict comparison between them impossible, and the insufficiency of implementation details, which can hinder their use. In this paper, we attempt to address both issues: we firstly propose an evaluation framework allowing a rigorous comparison of features extracted by different methods, and use it to carry out extensive experiments with state-of-the-art feature learning approaches. We then provide all the codes and implementation details to make both the reproduction of the results reported in this paper and the re-use of our framework easier for other researchers. Our studies carried out on the OPPORTUNITY and UniMiB-SHAR datasets highlight the effectiveness of hybrid deep-learning architectures involving convolutional and Long-Short-Term-Memory (LSTM) to obtain features characterising both short- and long-term time dependencies in the data.<\/jats:p>","DOI":"10.3390\/s18020679","type":"journal-article","created":{"date-parts":[[2018,2,27]],"date-time":"2018-02-27T04:20:47Z","timestamp":1519705247000},"page":"679","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":249,"title":["Comparison of Feature Learning Methods for Human Activity Recognition Using Wearable Sensors"],"prefix":"10.3390","volume":"18","author":[{"given":"Fr\u00e9d\u00e9ric","family":"Li","sequence":"first","affiliation":[{"name":"Research Group for Pattern Recognition, University of Siegen, H\u00f6lderlinstr 3, 57076 Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1843-5152","authenticated-orcid":false,"given":"Kimiaki","family":"Shirahama","sequence":"additional","affiliation":[{"name":"Research Group for Pattern Recognition, University of Siegen, H\u00f6lderlinstr 3, 57076 Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3288-750X","authenticated-orcid":false,"given":"Muhammad","family":"Nisar","sequence":"additional","affiliation":[{"name":"Research Group for Pattern Recognition, University of Siegen, H\u00f6lderlinstr 3, 57076 Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas","family":"K\u00f6ping","sequence":"additional","affiliation":[{"name":"Research Group for Pattern Recognition, University of Siegen, H\u00f6lderlinstr 3, 57076 Siegen, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marcin","family":"Grzegorzek","sequence":"additional","affiliation":[{"name":"Research Group for Pattern Recognition, University of Siegen, H\u00f6lderlinstr 3, 57076 Siegen, Germany"},{"name":"Department of Knowledge Engineering, University of Economics in Katowice, Bogucicka 3, 40-226 Katowice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2018,2,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"790","DOI":"10.1109\/TSMCC.2012.2198883","article-title":"Sensor-Based Activity Recognition","volume":"42","author":"Chen","year":"2012","journal-title":"IEEE Trans. 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