Literature DB >> 23643880

Validity of the Microsoft Kinect for providing lateral trunk lean feedback during gait retraining.

Ross A Clark1, Yong-Hao Pua, Adam L Bryant, Michael A Hunt.   

Abstract

Gait retraining programs are prescribed to assist in the rehabilitation process of many clinical conditions. Using lateral trunk lean modification as the model, the aim of this study was to assess the concurrent validity of kinematic data recorded using a marker-based 3D motion analysis (3DMA) system and a low-cost alternative, the Microsoft Kinect™ (Kinect), during a gait retraining session. Twenty healthy adults were trained to modify their gait to obtain a lateral trunk lean angle of 10°. Real-time biofeedback of the lateral trunk lean angle was provided on a computer screen in front of the subject using data extracted from the Kinect skeletal tracking algorithm. Marker coordinate data were concurrently recorded using the 3DMA system, and the similarity and equivalency of the trunk lean angle data from each system were compared. The lateral trunk lean angle data obtained from the Kinect system without any form of calibration resulted in errors of a high (>2°) magnitude (mean error=3.2±2.2°). Performing global and individualized calibration significantly (P<0.001) improved this error to 1.7±1.5° and 0.8±0.8° respectively. With the addition of a simple calibration the anatomical position coordinates of the Kinect can be used to create a real-time biofeedback system for gait retraining. Given that this system is low-cost, portable and does not require any sensors to be attached to the body, it could provide numerous advantages when compared to laboratory-based gait retraining systems.
Copyright © 2013 Elsevier B.V. All rights reserved.

Keywords:  Biofeedback; Gait training; Knee adduction moment; Osteoarthritis; Video game

Mesh:

Year:  2013        PMID: 23643880     DOI: 10.1016/j.gaitpost.2013.03.029

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


  29 in total

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2.  A low cost real-time motion tracking approach using webcam technology.

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4.  Predicting fat-free mass index and sarcopenia in assisted-living older adults.

Authors:  Taylor M Campbell; Lori Ann Vallis
Journal:  Age (Dordr)       Date:  2014-07-04

5.  Walking speed measurement technology: A review.

Authors:  Yohanna MejiaCruz; Jean Franco; Garret Hainline; Stacy Fritz; Zhaoshuo Jiang; Juan M Caicedo; Benjamin Davis; Victor Hirth
Journal:  Curr Geriatr Rep       Date:  2021-01-20

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Journal:  PLoS One       Date:  2015-06-15       Impact factor: 3.240

7.  A 2D Markerless Gait Analysis Methodology: Validation on Healthy Subjects.

Authors:  Andrea Castelli; Gabriele Paolini; Andrea Cereatti; Ugo Della Croce
Journal:  Comput Math Methods Med       Date:  2015-04-30       Impact factor: 2.238

8.  Improving vision-based motor rehabilitation interactive systems for users with disabilities using mirror feedback.

Authors:  Antoni Jaume-i-Capó; Pau Martínez-Bueso; Biel Moyà-Alcover; Javier Varona
Journal:  ScientificWorldJournal       Date:  2014-09-11

9.  Muscle torques and joint accelerations provide more sensitive measures of poststroke movement deficits than joint angles.

Authors:  Ariel B Thomas; Erienne V Olesh; Amelia Adcock; Valeriya Gritsenko
Journal:  J Neurophysiol       Date:  2021-06-30       Impact factor: 2.974

10.  Gait analysis methods: an overview of wearable and non-wearable systems, highlighting clinical applications.

Authors:  Alvaro Muro-de-la-Herran; Begonya Garcia-Zapirain; Amaia Mendez-Zorrilla
Journal:  Sensors (Basel)       Date:  2014-02-19       Impact factor: 3.576

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