Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease

Rehman, Rana Zia Ur; Klocke, Philipp; Hryniv, Sofia; Galna, Brook; Rochester, Lynn; Del Din, Silvia; Alcock, Lisa

doi:10.3390/s20185377

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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

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and

Lisa Alcock

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Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK

Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne NE4 5PL, UK

Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK

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Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK

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School of Biomedical, Nutritional and Sport Sciences, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK

⁶

The Newcastle upon Tyne NHS Foundation Trust, Newcastle upon Tyne NE1 1AA, UK

Author to whom correspondence should be addressed.

Sensors 2020, 20(18), 5377; https://doi.org/10.3390/s20185377

Received: 14 August 2020 / Revised: 11 September 2020 / Accepted: 16 September 2020 / Published: 19 September 2020

(This article belongs to the Special Issue Wearable Inertial Sensors)

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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. View Full-Text

Keywords: inertial measurement unit (IMU); wearables; upper body; lower body; spatial-temporal characteristics; signal-based characteristics; validation; machine learning; PLS-DA

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Rehman, R.Z.U.; Klocke, P.; Hryniv, S.; Galna, B.; Rochester, L.; Del Din, S.; Alcock, L. Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease. Sensors 2020, 20, 5377. https://doi.org/10.3390/s20185377

AMA Style

Rehman RZU, Klocke P, Hryniv S, Galna B, Rochester L, Del Din S, Alcock L. Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease. Sensors. 2020; 20(18):5377. https://doi.org/10.3390/s20185377

Chicago/Turabian Style

Rehman, Rana Zia Ur, Philipp Klocke, Sofia Hryniv, Brook Galna, Lynn Rochester, Silvia Del Din, and Lisa Alcock. 2020. "Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease" Sensors 20, no. 18: 5377. https://doi.org/10.3390/s20185377

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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

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