Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson’s Disease
by
Rana Zia Ur Rehman 1,2, 
Philipp Klocke 2,3
, Sofia Hryniv 2,4, Brook Galna 1,2,5, Lynn Rochester 1,2,6, 
Silvia Del Din 1,2 and Lisa Alcock 1,2,*
Rana Zia Ur Rehman 1,2, Philipp Klocke 2,3, Sofia Hryniv 2,4, Brook Galna 1,2,5, Lynn Rochester 1,2,6, Silvia Del Din 1,2 and Lisa Alcock 1,2,*
1
Translational and Clinical Research Institute, Faculty of Medical Sciences, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
2
Clinical Ageing Research Unit, Campus for Ageing and Vitality, Newcastle University, Newcastle upon Tyne NE4 5PL, UK
3
Faculty of Medicine, University of Southampton, Southampton SO17 1BJ, UK
4
Department of Psychology, University of Cambridge, Cambridge CB2 3EB, UK
5
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)
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.
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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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MDPI and ACS Style
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.
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