Literature DB >> 32807306

Reliability of a markerless motion capture system to measure the trunk, hip and knee angle during walking on a flatland and a treadmill.

Hiroyuki Tamura1, Ryo Tanaka2, Hiromichi Kawanishi3.   

Abstract

BACKGROUND: Markerless motion capture system (MLS) using an infrared sensor such as Microsoft Kinect has been used for gait analysis. Several studies have shown that kinematic measurements of trunk and lower limb joint angles during walking measured by MLS are valid. However, the reproducibility, presence of systematic error, or degree of random error of kinematic measurements during walking using MLS with Kinect v2 were not demonstrated. This study was made to confirm the reliability of kinematic measurements using Kinect v2 during gait. Twenty-two young, injury-free individuals volunteered to participate. Walks were made at 2 miles per hour (mph) on both the flatland and the treadmill. Intra-class correlation coefficients (ICCs) were calculated, systematic errors identified, and minimal detectable changes (MDCs) were estimated to assess the reliability of kinematic measurements of trunk, hip, and knee joint angles during walking. For trunk angles measured on the flatland, ICC was higher than 0.6, systematic error was smaller, and MDC was 2.2° smaller than that in gait on the treadmill (6.6°). In hip joint angles measured on the flatland, although systematic error was slight, ICC was not higher than on the treadmill and MDC exceeded 5°. The results for the knee joint were similar to those of the hip joint. Kinect can detect kinematic abnormalities of the trunk during slow walking on the flatland easier than on the treadmill.
Copyright © 2020 The Authors. Published by Elsevier Ltd.. All rights reserved.

Entities:  

Keywords:  Gait; Kinematics; Microsoft Kinect v2; Reliability

Mesh:

Year:  2020        PMID: 32807306     DOI: 10.1016/j.jbiomech.2020.109929

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  2 in total

1.  Development of Smartphone Application for Markerless Three-Dimensional Motion Capture Based on Deep Learning Model.

Authors:  Yukihiko Aoyagi; Shigeki Yamada; Shigeo Ueda; Chifumi Iseki; Toshiyuki Kondo; Keisuke Mori; Yoshiyuki Kobayashi; Tadanori Fukami; Minoru Hoshimaru; Masatsune Ishikawa; Yasuyuki Ohta
Journal:  Sensors (Basel)       Date:  2022-07-14       Impact factor: 3.847

2.  The Forward and Lateral Tilt Angle of the Neck and Trunk Measured by Three-Dimensional Gait and Motion Analysis as a Candidate for a Severity Index in Patients with Parkinson's Disease.

Authors:  Hirofumi Matsumoto; Makoto Shiraishi; Ariaki Higashi; Sakae Hino; Mayumi Kaburagi; Heisuke Mizukami; Futaba Maki; Junji Yamauchi; Kenichiro Tanabe; Tomoo Sato; Yoshihisa Yamano
Journal:  Neurol Int       Date:  2022-09-13
  2 in total

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