{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:55:20Z","timestamp":1774965320259,"version":"3.50.1"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,8,4]],"date-time":"2022-08-04T00:00:00Z","timestamp":1659571200000},"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>The short-term prediction of a person\u2019s trajectory during normal walking becomes necessary in many environments shared by humans and robots. Physics-based approaches based on Newton\u2019s laws of motion seem best suited for short-term predictions, but the intrinsic properties of human walking conflict with the foundations of the basic kinematical models compromising their performance. In this paper, we propose a short-time prediction method based on gait biomechanics for real-time applications. This method relays on a single biomechanical variable, and it has a low computational burden, turning it into a feasible solution to implement in low-cost portable devices. We evaluate its performance from an experimental benchmark where several subjects walked steadily over straight and curved paths. With this approach, the results indicate a performance good enough to be applicable to a wide range of human\u2013robot interaction applications.<\/jats:p>","DOI":"10.3390\/s22155828","type":"journal-article","created":{"date-parts":[[2022,8,5]],"date-time":"2022-08-05T02:12:39Z","timestamp":1659665559000},"page":"5828","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Real-Time Short-Term Pedestrian Trajectory Prediction Based on Gait Biomechanics"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1605-7998","authenticated-orcid":false,"given":"Leticia","family":"Gonz\u00e1lez","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Campus of Gijon, University of Oviedo, 33204 Gij\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5589-6954","authenticated-orcid":false,"given":"Antonio M.","family":"L\u00f3pez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Campus of Gijon, University of Oviedo, 33204 Gij\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8910-4855","authenticated-orcid":false,"given":"Juan C.","family":"\u00c1lvarez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Campus of Gijon, University of Oviedo, 33204 Gij\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0395-2712","authenticated-orcid":false,"given":"Diego","family":"\u00c1lvarez","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Campus of Gijon, University of Oviedo, 33204 Gij\u00f3n, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2394","DOI":"10.1109\/LRA.2018.2812906","article-title":"Human-Aware Robotic Assistant for Collaborative Assembly: Integrating Human Motion Prediction with Planning in Time","volume":"3","author":"Unhelkar","year":"2018","journal-title":"IEEE Robot. 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