Logo image
Validation of an algorithm for detecting turning in people with cognitive impairment, considering dementia disease subtype
Journal article   Open access   Peer reviewed

Validation of an algorithm for detecting turning in people with cognitive impairment, considering dementia disease subtype

Ríona Mc Ardle, Leigh J Ryan, Rana Zia Ur Rehman, Emily Dignan, Abbie Thompson, Silvia Del Din, Brook Galna, Alan J Thomas, Lynn Rochester and Lisa Alcock
Gait & posture, Vol.118, pp.141-147
2025
PMID: 39970572
pdf
Published824.93 kBDownloadView
CC BY V4.0 Open Access

Abstract

Validation Turning Gait Algorithm Cognitive impairment Dementia
Background Turning manoeuvres are an essential component of mobility and are vital for effective real-world navigation. Turning is more challenging than straight-line walking, involving complex cognitive functions to execute multi-segment co-ordination. Therefore, people with cognitive impairment (PwCI) may be more susceptible to impaired turning performance. Inertial measurement units (IMUs) can be used to quantify turning performance; however, IMU-based algorithms have not yet been validated for PwCI, or across dementia disease subtypes. Research question Is a custom-built algorithm for accurately detecting turn start and end valid for use in PwCI and in different dementia disease subtypes? Methods Sixty-six PwCI due to Alzheimer’s disease, Lewy body disease and vascular dementia, along with 23 cognitively healthy older adults (controls) were included. Participants wore an IMU on their lower back while completing six 10-m intermittent walks, segmented by 180° turns. A 2D colour video camera was used as the reference system. Videos were reviewed by two independent blinded raters annotating turn start and end. Agreement (intra-class correlation (ICC (2,1)), Spearman’s rho and Limits of agreement) and error (Root mean square error; RMSE and bias) between the raters (rater 1 vs. 2) and the algorithm (rater vs. algorithm) were evaluated. Results There was excellent agreement (rater-rater and rater-algorithm) for detecting turn start and end for PwCI and across dementia disease subtypes (rho = 1.00, ICC = 1.00). The error between raters was lower (RMSE < 0.72 s, bias < 0.41 s) than the error between raters and algorithm (RMSE < 1.29 s, bias < 1.4 s). Error was lowest for controls (RMSE < 0.94 s), followed by AD (RMSE < 1.21 s) and LBD (RMSE < 1.29 s). Significance Key findings suggest that this algorithm can detect turn start and end using an IMU in PwCI in agreement with a reference system (video ratings). Future research should consider the clinical application of turning assessment in PwCI, such as its ability to differentiate dementia disease subtypes to support accurate diagnosis.

Details

UN Sustainable Development Goals (SDGs)

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

#3 Good Health and Well-Being

Metrics

31 File views/ downloads
30 Record Views

InCites Highlights

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

Collaboration types
Industry collaboration
Domestic collaboration
International collaboration
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