Validity and inter-rater reliability of inertial gait measurements in Parkinson's disease: A pilot study
Abstract
Walking models driven by centre of mass (CoM) data obtained from inertial measurement units (IMU) or optical motion capture systems (OMCS) can be used to objectively measure gait. However current models have only been validated within typical developed adults (TDA). The purpose of this study was to compare the projected CoM movement within Parkinson's disease (PD) measured by an IMU with data collected from an OMCS after which spatio-temporal gait measures were derived using an inverted pendulum model. The inter-rater reliability of spatio-temporal parameters was explored between expert researchers and clinicians using the IMU processed data. Participants walked 10 m with an IMU attached over their centre of mass which was simultaneously recorded by an OMCS. Data was collected on two occasions, each by an expert researcher and clinician. Ten people with PD showed no difference (p=0.13) for vertical, translatory acceleration, velocity and relative position of the projected centre of mass between IMU and OMCS data. Furthermore no difference (p=0.18) was found for the derived step time, stride length and walking speed for people with PD. Measurements of step time (p=0.299), stride length (p=0.883) and walking speed (p=0.751) did not differ between experts and clinicians. There was good inter-rater reliability for these parameters (ICC3.1=0.979, ICC3.1=0.958 and ICC3.1=0.978, respectively). The findings are encouraging and support the use of IMUs by clinicians to measure CoM movement in people with PD.
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Wearables were also used in Parkinson's disease to quantify sleep disturbances, gait measures, freezing of gait, missteps and falls, and physical activity levels.
“Wearables were also used to quantify sleep disturbances [30], gait measures [31, 32], freezing of gait [33, 34], missteps and fall [35, 36], and physical activity levels [37–39].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [32]
Good agreement was found between sway and spatiotemporal gait measures from wearables and other established technologies.
“In addition, good agreement was found between sway and spatiotemporal gait measures from wearables and other established technologies [23, 31, 32].”
From Wearable sensors for clinical applications in epilepsy, Parkinson’s disease, and stroke: A mixed-methods systematic review (2018) · cites this paper as [32]
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- Authors
- Esser, P., Dawes, H., Collett, J., Feltham, M. G., & Howells, K.
- Affiliations
- Oxford Brookes University (GB)
- Corresponding author
- Esser, Patrick
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2012-03
- Volume
- 205
- Issue
- 1
- Pages
- 177-181
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W2085216068
- Cited by
- 67
- References
- 48
- Citations by year
- 2026: 5 · 2025: 2 · 2024: 6 · 2023: 5 · 2022: 2 · 2021: 5 · 2020: 6 · 2019: 10 · 2018: 7 · 2017: 3 · 2016: 4 · 2015: 5 · 2014: 4 · 2013: 2 · 2012: 1
- FWCI
- 9.55
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Neuroscience Methods (publishedVersion)
- PubMed (publishedVersion)
- University of Birmingham Research Portal (University of Birmingham) (submittedVersion)
- Funders
- World Health Organization
- Oxford Brookes University
- Wellcome Trust (089069)
- University of Oxford
- Topics
- Health Sciences › Health Professions › Physical Therapy, Sports Therapy and Rehabilitation › Balance, Gait, and Falls Prevention
- Physical Sciences › Engineering › Biomedical Engineering › Gait Recognition and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Lower Extremity Biomechanics and Pathologies
- Keywords
- Parkinson's disease, inertial measurement unit, gait analysis, optical motion capture, inter-rater reliability, spatiotemporal gait parameters, inverted pendulum model, step time, stride length, walking speed, clinical gait analysis
- MeSH terms
- Acceleration, Adult, Aged, Data Interpretation, Statistical, Female, Gait, Humans, Male, Middle Aged, Models, Neurological, Movement, Parkinson Disease, Reproducibility of Results, Observer Variation, Walking, Gait Disorders, Neurologic
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