{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T02:47:54Z","timestamp":1777085274190,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T00:00:00Z","timestamp":1490745600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union - Horizon 2020","award":["687283"],"award-info":[{"award-number":["687283"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The postures of wheelchair users can reveal their sitting habit, mood, and even predict health risks such as pressure ulcers or lower back pain. Mining the hidden information of the postures can reveal their wellness and general health conditions. In this paper, a cushion-based posture recognition system is used to process pressure sensor signals for the detection of user\u2019s posture in the wheelchair. The proposed posture detection method is composed of three main steps: data level classification for posture detection, backward selection of sensor configuration, and recognition results compared with previous literature. Five supervised classification techniques\u2014Decision Tree (J48), Support Vector Machines (SVM), Multilayer Perceptron (MLP), Naive Bayes, and k-Nearest Neighbor (k-NN)\u2014are compared in terms of classification accuracy, precision, recall, and F-measure. Results indicate that the J48 classifier provides the highest accuracy compared to other techniques. The backward selection method was used to determine the best sensor deployment configuration of the wheelchair. Several kinds of pressure sensor deployments are compared and our new method of deployment is shown to better detect postures of the wheelchair users. Performance analysis also took into account the Body Mass Index (BMI), useful for evaluating the robustness of the method across individual physical differences. Results show that our proposed sensor deployment is effective, achieving 99.47% posture recognition accuracy. Our proposed method is very competitive for posture recognition and robust in comparison with other former research. Accurate posture detection represents a fundamental basic block to develop several applications, including fatigue estimation and activity level assessment.<\/jats:p>","DOI":"10.3390\/s17040719","type":"journal-article","created":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T11:26:44Z","timestamp":1490786804000},"page":"719","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":118,"title":["Posture Detection Based on Smart Cushion for Wheelchair Users"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1579-2229","authenticated-orcid":false,"given":"Congcong","family":"Ma","sequence":"first","affiliation":[{"name":"School of Logistics Engineering, Wuhan University of Technology, Wuhan, 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wenfeng","family":"Li","sequence":"additional","affiliation":[{"name":"School of Logistics Engineering, Wuhan University of Technology, Wuhan, 430070, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raffaele","family":"Gravina","sequence":"additional","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, 87036, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4039-891X","authenticated-orcid":false,"given":"Giancarlo","family":"Fortino","sequence":"additional","affiliation":[{"name":"Department of Informatics, Modeling, Electronics and Systems, University of Calabria, Rende, 87036, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,3,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1109\/MNET.2010.5510920","article-title":"G-sense: A scalable architecture for global sensing and monitoring","volume":"24","author":"Perez","year":"2010","journal-title":"IEEE Netw."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"237","DOI":"10.1002\/spe.998","article-title":"SPINE: A domain-specific framework for rapid prototyping of WBSN applications","volume":"41","author":"Bellifemine","year":"2011","journal-title":"Softw. 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