Literature DB >> 25173920

Suitability of Kinect for measuring whole body movement patterns during exergaming.

Mike van Diest1, Jan Stegenga2, Heinrich J Wörtche2, Klaas Postema3, Gijsbertus J Verkerke4, Claudine J C Lamoth5.   

Abstract

Exergames provide a challenging opportunity for home-based training and evaluation of postural control in the elderly population, but affordable sensor technology and algorithms for assessment of whole body movement patterns in the home environment are yet to be developed. The aim of the present study was to evaluate the use of Kinect, a commonly available video game sensor, for capturing and analyzing whole body movement patterns. Healthy adults (n=20) played a weight shifting exergame under five different conditions with varying amplitudes and speed of sway movement, while 3D positions of ten body segments were recorded in the frontal plane using Kinect and a Vicon 3D camera system. Principal Component Analysis (PCA) was used to extract and compare movement patterns and the variance in individual body segment positions explained by these patterns. Using the identified patterns, balance outcome measures based on spatiotemporal sway characteristics were computed. The results showed that both Vicon and Kinect capture >90% variance of all body segment movements within three PCs. Kinect-derived movement patterns were found to explain variance in trunk movements accurately, yet explained variance in hand and foot segments was underestimated and overestimated respectively by as much as 30%. Differences between both systems with respect to balance outcome measures range 0.3-64.3%. The results imply that Kinect provides the unique possibility of quantifying balance ability while performing complex tasks in an exergame environment.
Copyright © 2014 Elsevier Ltd. All rights reserved.

Keywords:  Balance quantification; Exergame; Fall prevention; Principal Component Analysis

Mesh:

Year:  2014        PMID: 25173920     DOI: 10.1016/j.jbiomech.2014.07.017

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


  22 in total

1.  Modifying Kinect placement to improve upper limb joint angle measurement accuracy.

Authors:  Na Jin Seo; Mojtaba F Fathi; Pilwon Hur; Vincent Crocher
Journal:  J Hand Ther       Date:  2016-10-18       Impact factor: 1.950

2.  Use of the Microsoft Kinect system to characterize balance ability during balance training.

Authors:  Dohyung Lim; ChoongYeon Kim; HoHyun Jung; Dukyoung Jung; Keyoung Jin Chun
Journal:  Clin Interv Aging       Date:  2015-06-30       Impact factor: 4.458

3.  Adaptive training with full-body movements to reduce bradykinesia in persons with Parkinson's disease: a pilot study.

Authors:  Susanna Summa; Angelo Basteris; Enrico Betti; Vittorio Sanguineti
Journal:  J Neuroeng Rehabil       Date:  2015-02-14       Impact factor: 4.262

4.  MIT-Skywalker: considerations on the Design of a Body Weight Support System.

Authors:  Rogério Sales Gonçalves; Hermano Igo Krebs
Journal:  J Neuroeng Rehabil       Date:  2017-09-06       Impact factor: 4.262

5.  Visual Data Exploration for Balance Quantification in Real-Time During Exergaming.

Authors:  Venustiano Soancatl Aguilar; Jasper J van de Gronde; Claudine J C Lamoth; Mike van Diest; Natasha M Maurits; Jos B T M Roerdink
Journal:  PLoS One       Date:  2017-01-30       Impact factor: 3.240

6.  Quantifying Postural Control during Exergaming Using Multivariate Whole-Body Movement Data: A Self-Organizing Maps Approach.

Authors:  Mike van Diest; Jan Stegenga; Heinrich J Wörtche; Jos B T M Roerdink; Gijsbertus J Verkerke; Claudine J C Lamoth
Journal:  PLoS One       Date:  2015-07-31       Impact factor: 3.240

7.  Interactive balance training integrating sensor-based visual feedback of movement performance: a pilot study in older adults.

Authors:  Michael Schwenk; Gurtej S Grewal; Bahareh Honarvar; Stefanie Schwenk; Jane Mohler; Dharma S Khalsa; Bijan Najafi
Journal:  J Neuroeng Rehabil       Date:  2014-12-13       Impact factor: 4.262

Review 8.  A Review on Technical and Clinical Impact of Microsoft Kinect on Physical Therapy and Rehabilitation.

Authors:  Hossein Mousavi Hondori; Maryam Khademi
Journal:  J Med Eng       Date:  2014-12-10

9.  3D Analysis of Upper Limbs Motion during Rehabilitation Exercises Using the KinectTM Sensor: Development, Laboratory Validation and Clinical Application.

Authors:  Bruno Bonnechère; Victor Sholukha; Lubos Omelina; Serge Van Sint Jan; Bart Jansen
Journal:  Sensors (Basel)       Date:  2018-07-10       Impact factor: 3.576

10.  Effects of Kinect exergames on balance training among community older adults: A randomized controlled trial.

Authors:  Chi-Min Yang; Jun Scott Chen Hsieh; Yi-Chen Chen; Shu-Yu Yang; Hao-Chiang Koong Lin
Journal:  Medicine (Baltimore)       Date:  2020-07-10       Impact factor: 1.817

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