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. 2022 Oct 25;11(21):6270. doi: 10.3390/jcm11216270

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© 2022 by the authors.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

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Sagittal knee joint angles for a complete gait cycle in 4 patients (black lines) and mean ± standard deviation (SD) interval in healthy group (grey areas). (A) Patient with UKG from k1 cluster. (B) Patient with BKG from k2 cluster. (C) Patient with FLG from k3 cluster. (D) Patient with non-SKG pattern from k4 cluster. The 5 kinematic parameters used to feed the machine learning algorithm were: (1) the first peak knee flexion angle (PKF1), (2) the angle difference (KFE) between PKF1 and the knee flexion angle at homolateral toe-off (grey vertical bars), (3) the duration from homolateral toe-off to PKF1 (T1), (4) the angle difference (KFM) between PKF1 and the knee flexion angle at the end of the mid-swing when the homolateral tibia is vertical, and (5) the duration from PKF1 to the end of the mid-swing (T2). Total range of knee flexion (RKT) is also shown.