Literature DB >> 27264408

Accuracy of KinectOne to quantify kinematics of the upper body.

Roman P Kuster1, Bernd Heinlein1, Christoph M Bauer2, Eveline S Graf3.   

Abstract

Motion analysis systems deliver quantitative information, e.g. on the progress of rehabilitation programs aimed at improving range of motion. Markerless systems are of interest for clinical application because they are low-cost and easy to use. The first generation of the Kinect™ sensor showed promising results in validity assessment compared to an established marker-based system. However, no literature is available on the validity of the new 'Kinect™ for Xbox one' (KinectOne) in tracking upper body motion. Consequently, this study was conducted to analyze the accuracy and reliability of the KinectOne in tracking upper body motion. Twenty subjects performed shoulder abduction in frontal and scapula plane, flexion, external rotation and horizontal flexion in two conditions (sitting and standing). Arm and trunk motion were analyzed using the KinectOne and compared to a marker-based system. Comparisons were made using Bland Altman statistics and Coefficient of Multiple Correlation. On average, differences between systems of 3.9±4.0° and 0.1±3.8° were found for arm and trunk motion, respectively. Correlation was higher for the arm than for the trunk motion. Based on the observed bias, the accuracy of the KinectOne was found to be adequate to measure arm motion in a clinical setting. Although trunk motion showed a very low absolute bias between the two systems, the KinectOne was not able to track small changes over time. Before the KinectOne can find clinical application, further research is required analyzing whether validity can be improved using a customized tracking algorithm or other sensor placement, and to analyze test-retest reliability.
Copyright © 2016 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Kinect™ for Xbox One; Kinematics; Upper extremity; Validity

Mesh:

Year:  2016        PMID: 27264408     DOI: 10.1016/j.gaitpost.2016.04.004

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  10 in total

1.  Accuracy and Reliability of the Kinect Version 2 for Clinical Measurement of Motor Function.

Authors:  Karen Otte; Bastian Kayser; Sebastian Mansow-Model; Julius Verrel; Friedemann Paul; Alexander U Brandt; Tanja Schmitz-Hübsch
Journal:  PLoS One       Date:  2016-11-18       Impact factor: 3.240

2.  Reliability and validity of a novel Kinect-based software program for measuring posture, balance and side-bending.

Authors:  Wilhelmus Johannes Andreas Grooten; Lisa Sandberg; John Ressman; Nicolas Diamantoglou; Elin Johansson; Eva Rasmussen-Barr
Journal:  BMC Musculoskelet Disord       Date:  2018-01-08       Impact factor: 2.362

3.  Physically Consistent Whole-Body Kinematics Assessment Based on an RGB-D Sensor. Application to Simple Rehabilitation Exercises.

Authors:  Jessica Colombel; Vincent Bonnet; David Daney; Raphael Dumas; Antoine Seilles; François Charpillet
Journal:  Sensors (Basel)       Date:  2020-05-17       Impact factor: 3.576

4.  Investigating the impact of a motion capture system on Microsoft Kinect v2 recordings: A caution for using the technologies together.

Authors:  MReza Naeemabadi; Birthe Dinesen; Ole Kæseler Andersen; John Hansen
Journal:  PLoS One       Date:  2018-09-14       Impact factor: 3.240

5.  Kinect-based assessment of proximal arm non-use after a stroke.

Authors:  K K A Bakhti; I Laffont; M Muthalib; J Froger; D Mottet
Journal:  J Neuroeng Rehabil       Date:  2018-11-14       Impact factor: 4.262

6.  Wearable systems for shoulder kinematics assessment: a systematic review.

Authors:  Arianna Carnevale; Umile Giuseppe Longo; Emiliano Schena; Carlo Massaroni; Daniela Lo Presti; Alessandra Berton; Vincenzo Candela; Vincenzo Denaro
Journal:  BMC Musculoskelet Disord       Date:  2019-11-15       Impact factor: 2.362

7.  Kinect and wearable inertial sensors for motor rehabilitation programs at home: state of the art and an experimental comparison.

Authors:  Bojan Milosevic; Alberto Leardini; Elisabetta Farella
Journal:  Biomed Eng Online       Date:  2020-04-23       Impact factor: 2.819

8.  The Validity and Reliability of the Microsoft Kinect for Measuring Trunk Compensation during Reaching.

Authors:  Matthew H Foreman; Jack R Engsberg
Journal:  Sensors (Basel)       Date:  2020-12-10       Impact factor: 3.576

9.  Validity and Reliability of Kinect v2 for Quantifying Upper Body Kinematics during Seated Reaching.

Authors:  Germain Faity; Denis Mottet; Jérôme Froger
Journal:  Sensors (Basel)       Date:  2022-04-02       Impact factor: 3.576

10.  A real-time algorithm for the detection of compensatory movements during reaching.

Authors:  Edward Averell; Don Knox; Frederike van Wijck
Journal:  J Rehabil Assist Technol Eng       Date:  2022-09-01
  10 in total

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