{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T03:39:51Z","timestamp":1769830791598,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T00:00:00Z","timestamp":1599091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003661","name":"Korea Institute for Advancement of Technology","doi-asserted-by":"publisher","award":["P0002397"],"award-info":[{"award-number":["P0002397"]}],"id":[{"id":"10.13039\/501100003661","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Classification of foot\u2013ground contact phases, as well as the swing phase is essential in biomechanics domains where lower-limb motion analysis is required; this analysis is used for lower-limb rehabilitation, walking gait analysis and improvement, and exoskeleton motion capture. In this study, sliding-window label overlapping of time-series wearable motion data in training dataset acquisition is proposed to accurately detect foot\u2013ground contact phases, which are composed of 3 sub-phases as well as the swing phase, at a frequency of 100 Hz with a convolutional neural network (CNN) architecture. We not only succeeded in developing a real-time CNN model for learning and obtaining a test accuracy of 99.8% or higher, but also confirmed that its validation accuracy was close to 85%.<\/jats:p>","DOI":"10.3390\/s20174996","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T08:40:26Z","timestamp":1599122426000},"page":"4996","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Fast Wearable Sensor\u2013Based Foot\u2013Ground Contact Phase Classification Using a Convolutional Neural Network with Sliding-Window Label Overlapping"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1276-7247","authenticated-orcid":false,"given":"Haneul","family":"Jeon","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Soongsil University, Seoul 06978, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sang Lae","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Soongsil University, Seoul 06978, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Soyeon","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Soongsil University, Seoul 06978, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1814-2683","authenticated-orcid":false,"given":"Donghun","family":"Lee","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Soongsil University, Seoul 06978, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111","DOI":"10.3795\/KSME-A.2018.42.2.111","article-title":"Human activity recognition system using multimodal sensor and deep learning based on LSTM","volume":"42","author":"Shin","year":"2018","journal-title":"Trans. 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