{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T21:10:49Z","timestamp":1772917849908,"version":"3.50.1"},"reference-count":36,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2013,10,1]],"date-time":"2013-10-01T00:00:00Z","timestamp":1380585600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>In this paper, we present an approach to sense human body capacitance and apply it to recognize lower limb locomotion modes. The proposed wearable sensing system includes sensing bands, a signal processing circuit and a gait event detection module. Experiments on long-term working stability, adaptability to disturbance and locomotion mode recognition are carried out to validate the effectiveness of the proposed approach. Twelve able-bodied subjects are recruited, and eleven normal gait modes are investigated. With an event-dependent linear discriminant analysis classifier and feature selection procedure, four time-domain features are used for pattern recognition and satisfactory recognition accuracies (97:3% \u00b1 0:5%, 97:0% \u00b1 0:4%, 95:6% \u00b1 0:9% and 97:0% \u00b1 0:4% for four phases of one gait cycle respectively) are obtained. The accuracies are comparable with that from electromyography-based systems and inertial-based systems. The results validate the effectiveness of the proposed lower limb capacitive sensing approach in recognizing human normal gaits.<\/jats:p>","DOI":"10.3390\/s131013334","type":"journal-article","created":{"date-parts":[[2013,10,1]],"date-time":"2013-10-01T10:36:24Z","timestamp":1380623784000},"page":"13334-13355","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Lower Limb Wearable Capacitive Sensing and Its Applications to Recognizing Human Gaits"],"prefix":"10.3390","volume":"13","author":[{"given":"Enhao","family":"Zheng","sequence":"first","affiliation":[{"name":"Intelligent Control Laboratory, College of Engineering, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Baojun","family":"Chen","sequence":"additional","affiliation":[{"name":"Intelligent Control Laboratory, College of Engineering, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kunlin","family":"Wei","sequence":"additional","affiliation":[{"name":"Motion Control Laboratory, Department of Psychology, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qining","family":"Wang","sequence":"additional","affiliation":[{"name":"Intelligent Control Laboratory, College of Engineering, Peking University, Beijing 100871, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2013,10,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1109\/3516.491408","article-title":"Sensor technologies and microsensor issues for mechatronics systems","volume":"1","author":"Luo","year":"1996","journal-title":"IEEE\/ASME Trans. 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