Literature DB >> 29475751

Validity of time series kinematical data as measured by a markerless motion capture system on a flatland for gait assessment.

Ryo Tanaka1, Haruka Takimoto2, Takahiro Yamasaki3, Ariaki Higashi4.   

Abstract

As a cost-effective, clinician-friendly gait assessment tool, the Kinect v2 sensor may be effective for assessing lower extremity joint kinematics. This study aims to examine the validity of time series kinematical data as measured by the Kinect v2 on a flatland for gait assessment. In this study, 51 healthy subjects walked on a flatland while kinematic data were extracted concurrently using the Kinect and Vicon systems. The kinematic outcomes comprised the hip and knee joint angles. Parallel translation of Kinect data obtained throughout the gait cycle was performed to minimize the differences between the Kinect and Vicon data. The ensemble curves of the hip and knee joint angles were compared to investigate whether the Kinect sensor can consistently and accurately assess lower extremity joint motion throughout the gait cycle. Relative consistency was assessed using Pearson correlation coefficients. Joint angles measured by the Kinect v2 followed the trend of the trajectories made by the Vicon data in both the hip and knee joints in the sagittal plane. The trajectories of the hip and knee joint angles in the frontal plane differed between the Kinect and Vicon data. We observed moderate to high correlation coefficients of 20%-60% of the gait cycle, and the largest difference between Kinect and Vicon data was 4.2°. Kinect v2 time series kinematical data obtained on the flatland are validated if the appropriate correction procedures are performed. Future studies are warranted to examine the reproducibility and systematic bias of the Kinect v2.
Copyright © 2018. Published by Elsevier Ltd.

Keywords:  Gait; Kinematics; Microsoft Kinect v2; Validity

Mesh:

Year:  2018        PMID: 29475751     DOI: 10.1016/j.jbiomech.2018.01.035

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


  8 in total

1.  Semi-automated 3D segmentation of human skeletal muscle using Focused Ion Beam-Scanning Electron Microscopic images.

Authors:  Brian J Caffrey; Alexander V Maltsev; Marta Gonzalez-Freire; Lisa M Hartnell; Luigi Ferrucci; Sriram Subramaniam
Journal:  J Struct Biol       Date:  2019-03-23       Impact factor: 2.867

2.  Applications and limitations of current markerless motion capture methods for clinical gait biomechanics.

Authors:  Logan Wade; Laurie Needham; Polly McGuigan; James Bilzon
Journal:  PeerJ       Date:  2022-02-25       Impact factor: 2.984

3.  Efficacy of Wearable Device Gait Training on Parkinson's Disease: A Randomized Controlled Open-label Pilot Study.

Authors:  Noriko Kawashima; Kazuko Hasegawa; Masako Iijima; Kayo Nagami; Tomomi Makimura; Aya Kumon; Shigeaki Ohtsuki
Journal:  Intern Med       Date:  2022-02-08       Impact factor: 1.282

4.  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

5.  3D Tracking of Human Motion Using Visual Skeletonization and Stereoscopic Vision.

Authors:  Matteo Zago; Matteo Luzzago; Tommaso Marangoni; Mariolino De Cecco; Marco Tarabini; Manuela Galli
Journal:  Front Bioeng Biotechnol       Date:  2020-03-05

6.  Validation of a Novel Device for the Knee Monitoring of Orthopaedic Patients.

Authors:  Mahmut Enes Kayaalp; Alison N Agres; Jan Reichmann; Maxim Bashkuev; Georg N Duda; Roland Becker
Journal:  Sensors (Basel)       Date:  2019-11-27       Impact factor: 3.576

7.  The Accuracy of the Microsoft Kinect V2 Sensor for Human Gait Analysis. A Different Approach for Comparison with the Ground Truth.

Authors:  Diego Guffanti; Alberto Brunete; Miguel Hernando; Javier Rueda; Enrique Navarro Cabello
Journal:  Sensors (Basel)       Date:  2020-08-07       Impact factor: 3.576

8.  Use of the Azure Kinect to measure foot clearance during obstacle crossing: A validation study.

Authors:  Kohei Yoshimoto; Masahiro Shinya
Journal:  PLoS One       Date:  2022-03-11       Impact factor: 3.240

  8 in total

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