Instrumenting Parkrun: Usefulness and Validity of Inertial Sensors.

Journal and index pages often block iframe embedding. This reader keeps the evidence details in Orthonotes and leaves the source page one click away.

Abstract

[Indexed for MEDLINE] Conflict of interest statement: The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript; or in the decision to publish the results. 16. Gait Posture. 2022 May;94:210-216. doi: 10.1016/j.gaitpost.2022.03.007. Epub 2022 Mar 26. Pathological gait clustering in post-stroke patients using motion capture data. Kim H(1), Kim YH(2), Kim SJ(3), Choi MT(4). Author information: (1)School of Mechanical Engineering Sungkyunkwan University, Suwon, Republic of Korea. Electronic address: gudxo1229@g.skku.edu. (2)Department of Physical and Rehabilitation Medicine, Heart Vascular Stroke Institute, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea. Electronic address: yun1225.kim@samsung.com. (3)Department of Biomedical Engineering, College of Medicine, Korea University, Seoul, Republic of Korea. Electronic address: sjkim586@korea.ac.kr. (4)School of Mechanical Engineering Sungkyunkwan University, Suwon, Republic of Korea. Electronic address: mtchoi@skku.edu. BACKGROUND: Analyzing the complex gait patterns of post-stroke patients with lower limb paralysis is essential for rehabilitation. RESEARCH QUESTION: Is it feasible to use the full joint-level kinematic features extracted from the motion capture data of patients directly to identify the optimal gait types that ensure high classification performance? METHODS: In this study, kinematic features were extracted from 111 gait cycle data on joint angles, and angular velocities of 36 post-stroke patients were collected eight times over six months using a motion capture system. Simultaneous clustering and classification were applied to determine the optimal gait types for reliable classification performance. RESULTS: In the given dataset, six optimal gait groups were identified, and the clustering and classification performances were denoted by a silhouette coefficient of 0.1447 and F1 score of 1.0000, respectively. SIGNIFICANCE: There is no distinct clinical classification of post-stroke hemiplegic gaits. However, in contrast to previous studies, more optimal gait types with a high classification performance fully utilizing the kinematic features were identified in this study. Copyright © 2022. Published by Elsevier B.V. DOI: 10.1016/j.gaitpost.2022.03.007