Literature DB >> 28865963

Can shoulder range of movement be measured accurately using the Microsoft Kinect sensor plus Medical Interactive Recovery Assistant (MIRA) software?

James D Wilson1, Jennifer Khan-Perez2, Dominic Marley3, Susan Buttress4, Michael Walton5, Baihua Li6, Bibhas Roy7.   

Abstract

BACKGROUND: This study compared the accuracy of measuring shoulder range of movement (ROM) with a simple laptop-sensor combination vs. trained observers (shoulder physiotherapists and shoulder surgeons) using motion capture (MoCap) laboratory equipment as the gold standard.
METHODS: The Microsoft Kinect sensor (Microsoft Corp., Redmond, WA, USA) tracks 3-dimensional human motion. Ordinarily used with an Xbox (Microsoft Corp.) video game console, Medical Interactive Recovery Assistant (MIRA) software (MIRA Rehab Ltd., London, UK) allows this small sensor to measure shoulder movement with a standard computer. Shoulder movements of 49 healthy volunteers were simultaneously measured by trained observers, MoCap, and the MIRA device. Internal rotation was assessed with the shoulder abducted 90° and external rotation with the shoulder adducted. Visual estimation and MIRA measurements were compared with gold standard MoCap measurements for agreement using Bland-Altman methods.
RESULTS: There were 1670 measurements analyzed. The MIRA evaluations of all 4 cardinal shoulder movements were significantly more precise, with narrower limits of agreement, than the measurements of trained observers. MIRA achieved ±11° (95% confidence interval [CI], 8.7°-12.6°) for forward flexion vs. ±16° (95% CI, 14.6°-17.6°) by trained observers. For abduction, MIRA showed ±11° (95% CI, 8.7°-12.8°) against ±15° (95% CI, 13.4°-16.2°) for trained observers. MIRA attained ±10° (95% CI, 8.1°-11.9°) during external rotation measurement, whereas trained observers only reached ±21° (95% CI, 18.7°-22.6°). For internal rotation, MIRA achieved ±9° (95% CI, 7.2°-10.4°), which was again better than TOs at ±18° (95% CI, 16.0°-19.3°).
CONCLUSIONS: A laptop combined with a Microsoft Kinect sensor and the MIRA software can measure shoulder movements with acceptable levels of accuracy. This technology, which can be easily set up, may also allow precise shoulder ROM measurement outside the clinic setting.
Copyright © 2017 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Kinect; MIRA; Sensor; exergames; gamification; measurement; technology; validation

Mesh:

Year:  2017        PMID: 28865963     DOI: 10.1016/j.jse.2017.06.004

Source DB:  PubMed          Journal:  J Shoulder Elbow Surg        ISSN: 1058-2746            Impact factor:   3.019


  8 in total

1.  The contribution of the scapula to active shoulder motion and self-assessed function in three hundred and fifty two patients prior to elective shoulder surgery.

Authors:  Jason E Hsu; David Andrew Hulet; Chris McDonald; Anastasia Whitson; Stacy M Russ; Frederick A Matsen
Journal:  Int Orthop       Date:  2018-07-09       Impact factor: 3.075

2.  The Reliability and Validity of Wearable Inertial Sensors Coupled with the Microsoft Kinect to Measure Shoulder Range-of-Motion.

Authors:  Peter Beshara; Judy F Chen; Andrew C Read; Pierre Lagadec; Tian Wang; William Robert Walsh
Journal:  Sensors (Basel)       Date:  2020-12-17       Impact factor: 3.576

Review 3.  The Reliability of the Microsoft Kinect and Ambulatory Sensor-Based Motion Tracking Devices to Measure Shoulder Range-of-Motion: A Systematic Review and Meta-Analysis.

Authors:  Peter Beshara; David B Anderson; Matthew Pelletier; William R Walsh
Journal:  Sensors (Basel)       Date:  2021-12-08       Impact factor: 3.576

4.  Reliability of a human pose tracking algorithm for measuring upper limb joints: comparison with photography-based goniometry.

Authors:  Jingyuan Fan; Fanbin Gu; Lulu Lv; Zhejin Zhang; Changbing Zhu; Jian Qi; Honggang Wang; Xiaolin Liu; Jiantao Yang; Qingtang Zhu
Journal:  BMC Musculoskelet Disord       Date:  2022-09-21       Impact factor: 2.562

5.  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

6.  Validation of a Kinect V2 based rehabilitation game.

Authors:  Mengxuan Ma; Rachel Proffitt; Marjorie Skubic
Journal:  PLoS One       Date:  2018-08-24       Impact factor: 3.240

7.  Kinect v2-Assisted Semi-Automated Method to Assess Upper Limb Motor Performance in Children.

Authors:  Celia Francisco-Martínez; José A Padilla-Medina; Juan Prado-Olivarez; Francisco J Pérez-Pinal; Alejandro I Barranco-Gutiérrez; Juan J Martínez-Nolasco
Journal:  Sensors (Basel)       Date:  2022-03-15       Impact factor: 3.576

8.  Improving patient rehabilitation performance in exercise games using collaborative filtering approach.

Authors:  Waidah Ismail; Ismail Ahmed Al-Qasem Al-Hadi; Crina Grosan; Rimuljo Hendradi
Journal:  PeerJ Comput Sci       Date:  2021-07-14
  8 in total

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