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Turning detection during gait: Algorithm validation and influence of sensor location and turning characteristics in the classification of Parkinson’s disease

Rehman, R.Z.U., Klocke, P., Hryniv, S., Galna, B.ORCID: 0000-0002-5890-1894, Rochester, L., Del Din, S. and Alcock, L. (2020) Turning detection during gait: Algorithm validation and influence of sensor location and turning characteristics in the classification of Parkinson’s disease. Sensors, 20 (18). Article 5377.

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Abstract

Parkinson’s disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.

Item Type: Journal Article
Publisher: MDPI
Copyright: © 2020 by the authors
URI: http://researchrepository.murdoch.edu.au/id/eprint/62677
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