Toggle Main Menu Toggle Search

Open Access padlockePrints

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 Rochester, Dr Silvia Del DinORCiD, Dr Lisa AlcockORCiD


Full text for this publication is not currently held within this repository. Alternative links are provided below where available.


© 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


Altmetrics provided by Altmetric


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