Logo image
Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease
Journal article   Open access   Peer reviewed

Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease

B. Galna, G. Barry, D. Jackson, D. Mhiripiri, P. Olivier and L. Rochester
Gait and Posture, Vol.39(4), pp.1062-1068
2014
pdf
Accuracy of the Microsoft Kinect sensor for measuring movement in people with Parkinson's disease.pdfDownloadView
Published (Version of Record)CC BY-NC-SA V4.0 Open Access
url
Free to Read *No subscription requiredView

Abstract

Background The Microsoft Kinect sensor (Kinect) is potentially a low-cost solution for clinical and home-based assessment of movement symptoms in people with Parkinson's disease (PD). The purpose of this study was to establish the accuracy of the Kinect in measuring clinically relevant movements in people with PD. Methods Nine people with PD and 10 controls performed a series of movements which were measured concurrently with a Vicon three-dimensional motion analysis system (gold-standard) and the Kinect. The movements included quiet standing, multidirectional reaching and stepping and walking on the spot, and the following items from the Unified Parkinson's Disease Rating Scale: hand clasping, finger tapping, foot, leg agility, chair rising and hand pronation. Outcomes included mean timing and range of motion across movement repetitions. Results The Kinect measured timing of movement repetitions very accurately (low bias, 95% limits of agreement <10% of the group mean, ICCs >0.9 and Pearson's r > 0.9). However, the Kinect had varied success measuring spatial characteristics, ranging from excellent for gross movements such as sit-to-stand (ICC = .989) to very poor for fine movement such as hand clasping (ICC = .012). Despite this, results from the Kinect related strongly to those obtained with the Vicon system (Pearson's r > 0.8) for most movements. Conclusions The Kinect can accurately measure timing and gross spatial characteristics of clinically relevant movements but not with the same spatial accuracy for smaller movements, such as hand clasping.

Details

UN Sustainable Development Goals (SDGs)

This output has contributed to the advancement of the following goals:

#3 Good Health and Well-Being

Source: InCites

Metrics

23 File views/ downloads
52 Record Views

InCites Highlights

These are selected metrics from InCites Benchmarking & Analytics tool, related to this output

Citation topics
1 Clinical & Life Sciences
1.82 Gait & Posture
1.82.263 Gait and Balance
Web Of Science research areas
Neurosciences
Orthopedics
Sport Sciences
ESI research areas
Clinical Medicine
Logo image