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Digitizing a Therapeutic: Development of an Augmented Reality Dual-Task Training Platform for Parkinson’s Disease

Authors :
Jay L. Alberts
Ryan D. Kaya
Kathryn Scelina
Logan Scelina
Eric M. Zimmerman
Benjamin L. Walter
Anson B. Rosenfeldt
Source :
Sensors, Vol 22, Iss 22, p 8756 (2022)
Publication Year :
2022
Publisher :
MDPI AG, 2022.

Abstract

Augmented reality (AR) may be a useful tool for the delivery of dual-task training. This manuscript details the development of the Dual-task Augmented Reality Treatment (DART) platform for individuals with Parkinson’s disease (PD) and reports initial feasibility, usability, and efficacy of the DART platform in provoking dual-task interference in individuals with PD. The DART platform utilizes the head-mounted Microsoft HoloLens2 AR device to deliver concurrent motor and cognitive tasks. Biomechanical metrics of gait and cognitive responses are automatically computed and provided to the supervising clinician. To assess feasibility, individuals with PD (N = 48) completed a bout of single-task and dual-task walking using the DART platform. Usability was assessed by the System Usability Scale (SUS). Dual-task interference was assessed by comparing single-task walking and walking during an obstacle course while performing a cognitive task. Average gait velocity decreased from 1.06 to 0.82 m/s from single- to dual-task conditions. Mean SUS scores were 81.3 (11.3), which placed the DART in the “good” to “excellent” category. To our knowledge, the DART platform is the first to use a head-mounted AR system to deliver a dual-task paradigm and simultaneously provide biomechanical data that characterize cognitive and motor performance. Individuals with PD were able to successfully use the DART platform with satisfaction, and dual-task interference was provoked. The DART platform should be investigated as a platform to treat dual-task declines associated with PD.

Details

Language :
English
ISSN :
22228756 and 14248220
Volume :
22
Issue :
22
Database :
Directory of Open Access Journals
Journal :
Sensors
Publication Type :
Academic Journal
Accession number :
edsdoj.5da2292412a4836842dfca3f2765318
Document Type :
article
Full Text :
https://doi.org/10.3390/s22228756