Brook Galna1, Gillian Barry1, Dan Jackson2, Dadirayi Mhiripiri1, Patrick Olivier2, Lynn Rochester3. 1. Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom. 2. Culture Lab, School of Computing Science, Newcastle University, Newcastle upon Tyne, United Kingdom. 3. Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, United Kingdom. Electronic address: lynn.rochester@ncl.ac.uk.
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.
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.
Authors: Pierre Cabaraux; Sunil K Agrawal; Huaying Cai; Rocco Salvatore Calabro; Carlo Casali; Loic Damm; Sarah Doss; Christophe Habas; Anja K E Horn; Winfried Ilg; Elan D Louis; Hiroshi Mitoma; Vito Monaco; Maria Petracca; Alberto Ranavolo; Ashwini K Rao; Serena Ruggieri; Tommaso Schirinzi; Mariano Serrao; Susanna Summa; Michael Strupp; Olivia Surgent; Matthis Synofzik; Shuai Tao; Hiroo Terasi; Diego Torres-Russotto; Brittany Travers; Jaimie A Roper; Mario Manto Journal: Cerebellum Date: 2022-04-12 Impact factor: 3.847