Gait Parameters Can Be Derived Reliably and Validly from Augmented Reality Glasses in People with Parkinson’s Disease Performing 10-m Walk Tests at Comfortable and Fast Speeds

The 10-m walk test (10MWT) is a stopwatch-based clinical mobility assessment. To better understand mobility limitations, 10MWT test completion times may be complemented with gait parameters like step length. State-of-the-art augmented reality (AR) glasses can potentially do this given their unique 3D-positional data from which gait parameters may be derived. We examined the test-retest reliability, concurrent validity, and face validity of gait parameters derived from AR glasses during a 10MWT in 20 people with Parkinson's disease, performed at self-selected comfortable and fast-but-safe walking speeds. AR-derived 10MWT completion times and gait parameters (mean step length, cadence, and maximal gait speed) were compared across repetitions and with lab-based (Interactive Walkway) and clinical (stopwatch) reference systems. Good-to-excellent test-retest reliability statistics were observed for test completion times and gait parameters for all systems and conditions alike. Concurrent validity was demonstrated between AR, lab-based, and clinical references for test completion times (good-to-excellent agreement: ICC > 0.879) and gait parameters (excellent agreement: ICC > 0.942). Face validity was confirmed by significant differences in test completion times and gait parameters between speed conditions in a-priori expected directions. These findings support the conclusion that gait parameters can be derived reliably and validly from AR glasses in people with Parkinson's disease.

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Publication Details

Journal
Sensors
Published
2025-02-18
DOI
https://doi.org/10.3390/s25041230
Citations
4
Primary Topic
Balance, Gait, and Falls Prevention
Type
article
Field-Weighted Citation Impact
19.04

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article

Gait Parameters Can Be Derived Reliably and Validly from Augmented Reality Glasses in People with Parkinson’s Disease Performing 10-m Walk Tests at Comfortable and Fast Speeds

Pieter F van Doorn, Edward Nyman, Daphne J. Geerse, Melvyn Roerdink et al.
4 citations
Sensors
Balance, Gait, and Falls Prevention
19.04
article

Gait Parameters Can Be Derived Reliably and Validly from Augmented Reality Glasses in People with Parkinson’s Disease Performing 10-m Walk Tests at Comfortable and Fast Speeds

Pieter F van Doorn, Edward Nyman, Daphne J. Geerse, Melvyn Roerdink, Eva M. Hoogendoorn, Jara S. van Bergem
article en
4 citations

Abstract

The 10-m walk test (10MWT) is a stopwatch-based clinical mobility assessment. To better understand mobility limitations, 10MWT test completion times may be complemented with gait parameters like step length. State-of-the-art augmented reality (AR) glasses can potentially do this given their unique 3D-positional data from which gait parameters may be derived. We examined the test-retest reliability, concurrent validity, and face validity of gait parameters derived from AR glasses during a 10MWT in 20 people with Parkinson's disease, performed at self-selected comfortable and fast-but-safe walking speeds. AR-derived 10MWT completion times and gait parameters (mean step length, cadence, and maximal gait speed) were compared across repetitions and with lab-based (Interactive Walkway) and clinical (stopwatch) reference systems. Good-to-excellent test-retest reliability statistics were observed for test completion times and gait parameters for all systems and conditions alike. Concurrent validity was demonstrated between AR, lab-based, and clinical references for test completion times (good-to-excellent agreement: ICC > 0.879) and gait parameters (excellent agreement: ICC > 0.942). Face validity was confirmed by significant differences in test completion times and gait parameters between speed conditions in a-priori expected directions. These findings support the conclusion that gait parameters can be derived reliably and validly from AR glasses in people with Parkinson's disease.

SensorsVol. 25(4)
Maastricht University (NL), Vrije Universiteit Amsterdam (NL)
UK Research and Innovation, European Commission, HORIZON EUROPE Framework Programme, HORIZON EUROPE European Innovation Council
Openalex Percentile: Top 1%
Balance, Gait, and Falls Prevention
19.04
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