{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T09:50:14Z","timestamp":1767261014134,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2025,7,10]],"date-time":"2025-07-10T00:00:00Z","timestamp":1752105600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Rehabilitation Center","award":["NRCTR-IN23003","NRCTR-IN24003","NRCTR-IN25003","IITP-2025-RS-2023-00260267"],"award-info":[{"award-number":["NRCTR-IN23003","NRCTR-IN24003","NRCTR-IN25003","IITP-2025-RS-2023-00260267"]}]},{"DOI":"10.13039\/501100014188","name":"Institute for Information &amp; Communications Technology Planning &amp; Evaluation (IITP)-Innovative Human Resource Development for Local Intellectualization program grant funded by the Korean government (MSIT)","doi-asserted-by":"publisher","award":["NRCTR-IN23003","NRCTR-IN24003","NRCTR-IN25003","IITP-2025-RS-2023-00260267"],"award-info":[{"award-number":["NRCTR-IN23003","NRCTR-IN24003","NRCTR-IN25003","IITP-2025-RS-2023-00260267"]}],"id":[{"id":"10.13039\/501100014188","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Gait, the fundamental form of human locomotion, occurs across diverse environments. The technology for recognizing environmental changes during walking is crucial for preventing falls and controlling wearable robots. This study collected gait data on level ground (LG), ramps, and stairs using a feed-forward neural network (FFNN) to classify the corresponding gait environments. Gait experiments were performed on five non-disabled participants using an inertial measurement unit, a galvanic skin response sensor, and a smart insole. The collected data were preprocessed through time synchronization and filtering, then labeled according to the gait environment, yielding 47,033 data samples. Gait data were used to train an FFNN model with a single hidden layer, achieving a high accuracy of 98%, with the highest accuracy observed on LG. This study confirms the effectiveness of classifying gait environments based on signals acquired from various wearable sensors during walking. In the future, these research findings may serve as basic data for exoskeleton robot control and gait analysis.<\/jats:p>","DOI":"10.3390\/s25144302","type":"journal-article","created":{"date-parts":[[2025,7,11]],"date-time":"2025-07-11T10:26:53Z","timestamp":1752229613000},"page":"4302","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Gait Environment Recognition Using Biomechanical and Physiological Signals with Feed-Forward Neural Network: A Pilot Study"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8842-3942","authenticated-orcid":false,"given":"Kyeong-Jun","family":"Seo","sequence":"first","affiliation":[{"name":"Department of Rehabilitation & Assistive Technology, National Rehabilitation Center, Ministry of Health and Welfare, Seoul 01022, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4810-1014","authenticated-orcid":false,"given":"Jinwon","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Industrial and Management Engineering, Gangneung-Wonju National University, Wonju 26403, Gangwon-do, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4816-9716","authenticated-orcid":false,"given":"Ji-Eun","family":"Cho","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation & Assistive Technology, National Rehabilitation Center, Ministry of Health and Welfare, Seoul 01022, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9624-6096","authenticated-orcid":false,"given":"Hogene","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI 48109, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0639-0428","authenticated-orcid":false,"given":"Jung Hwan","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Rehabilitation Medicine, Ewha Womans University & Medical Center Mokdong Hospital, Seoul 07985, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,7,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s00508-016-1096-4","article-title":"Gait disorders in adults and the elderly: A clinical guide","volume":"129","author":"Pirker","year":"2017","journal-title":"Wien. 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