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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

Lookup NU author(s): Dr Rana RehmanORCiD, Philipp Klocke, Sofia Hryniv, Dr Brook Galna, Professor Lynn RochesterORCiD, Dr Silvia Del DinORCiD, Dr Lisa AlcockORCiD



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2020 by the authors. Licensee MDPI, Basel, Switzerland. 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.

Publication metadata

Author(s): Rehman RZU, Klocke P, Hryniv S, Galna B, Rochester L, Del Din S, Alcock L

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2020

Volume: 20

Issue: 18

Online publication date: 19/09/2020

Acceptance date: 16/09/2020

Date deposited: 14/11/2020

ISSN (electronic): 1424-8220

Publisher: MDPI AG


DOI: 10.3390/s20185377

PubMed id: 32961799


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Funder referenceFunder name
J-0802Parkinson`s UK (formerly Parkinson`s Disease Society)