{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T15:13:51Z","timestamp":1788448431219,"version":"build-2803163510"},"reference-count":24,"publisher":"Wiley","license":[{"start":{"date-parts":[[2021,2,18]],"date-time":"2021-02-18T00:00:00Z","timestamp":1613606400000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100006261","name":"Taif University","doi-asserted-by":"publisher","award":["TURSP-2020\/231"],"award-info":[{"award-number":["TURSP-2020\/231"]}],"id":[{"id":"10.13039\/501100006261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Healthcare Engineering"],"published-print":{"date-parts":[[2021,2,18]]},"abstract":"<jats:p>A high-accuracy gait data prediction model can be used to design prosthesis and orthosis for people having amputations or ailments of the lower limb. The objective of this study is to observe the gait data of different subjects and design a neural network to predict future gait angles for fixed speeds. The data were recorded via a Biometrics goniometer, while the subjects were walking on a treadmill for 20\u2009seconds each at 2.4\u2009kmph, 3.6\u2009kmph, and 5.4\u2009kmph. The data were then imported into Matlab, filtered to remove movement artifacts, and then used to design a neural network with 60% data for training, 20% for validation, and remaining 20% for testing using the LevenbergMarquardt method. The mean-squared error for all the cases was in the order of 10\u22123 or lower confirming that our method is correct. For further comparison, we randomly tested the neural network function with untrained data and compared the expected output with actual output of the neural network function using Pearson\u2019s correlation coefficient and correlation plots. We conclude that our framework can be successfully used to design prosthesis and orthosis for lower limb. It can also be used to validate gait data and compare it to expected data in rehabilitation engineering.<\/jats:p>","DOI":"10.1155\/2021\/5541255","type":"journal-article","created":{"date-parts":[[2021,2,19]],"date-time":"2021-02-19T02:51:54Z","timestamp":1613703114000},"page":"1-11","source":"Crossref","is-referenced-by-count":15,"title":["Human Gait Analysis and Prediction Using the Levenberg-Marquardt Method"],"prefix":"10.1155","volume":"2021","author":[{"given":"Abdullah","family":"Alharbi","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computers and Information Technology, Taif University, P.O. 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