{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T01:32:07Z","timestamp":1778290327796,"version":"3.51.4"},"reference-count":38,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2018,5,28]],"date-time":"2018-05-28T00:00:00Z","timestamp":1527465600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Accurate estimation of gait parameters is essential for obtaining quantitative information on motor deficits in Parkinson\u2019s disease and other neurodegenerative diseases, which helps determine disease progression and therapeutic interventions. Due to the demand for high accuracy, unobtrusive measurement methods such as optical motion capture systems, foot pressure plates, and other systems have been commonly used in clinical environments. However, the high cost of existing lab-based methods greatly hinders their wider usage, especially in developing countries. In this study, we present a low-cost, noncontact, and an accurate temporal gait parameters estimation method by sensing and analyzing the electrostatic field generated from human foot stepping. The proposed method achieved an average 97% accuracy on gait phase detection and was further validated by comparison to the foot pressure system in 10 healthy subjects. Two results were compared using the Pearson coefficient r and obtained an excellent consistency (r = 0.99, p &lt; 0.05). The repeatability of the purposed method was calculated between days by intraclass correlation coefficients (ICC), and showed good test-retest reliability (ICC = 0.87, p &lt; 0.01). The proposed method could be an affordable and accurate tool to measure temporal gait parameters in hospital laboratories and in patients\u2019 home environments.<\/jats:p>","DOI":"10.3390\/s18061737","type":"journal-article","created":{"date-parts":[[2018,5,29]],"date-time":"2018-05-29T02:58:18Z","timestamp":1527562698000},"page":"1737","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method"],"prefix":"10.3390","volume":"18","author":[{"given":"Mengxuan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mechatronics Engineering and Control, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pengfei","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechatronics Engineering and Control, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanshan","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechatronics Engineering and Control, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Tang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechatronics Engineering and Control, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xi","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechatronics Engineering and Control, Beijing Institute of Technology, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,5,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.robot.2014.10.012","article-title":"Recognition of gait cycle phases using wearable sensors","volume":"75","author":"Mohammed","year":"2016","journal-title":"Rob. 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