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Open AccessArticle

Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease

1
Institute of Neuroscience/Institute for Ageing, Newcastle University, Newcastle Upon Tyne NE4 5PL, UK
2
School of Mathematics, Statistics, and Physics, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
3
School of Biomedical, Nutritional and Sport Sciences, Newcastle University, Newcastle Upon Tyne NE1 7RU, UK
4
Department of Physiotherapy, Auckland University of Technology, Auckland 92006, New Zealand
5
The Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle Upon Tyne NE7 7DN, UK
6
School of Computing, Newcastle University, Newcastle Upon Tyne NE4 5TG, UK
*
Author to whom correspondence should be addressed.
Sensors 2019, 19(24), 5363; https://doi.org/10.3390/s19245363
Received: 1 November 2019 / Revised: 26 November 2019 / Accepted: 2 December 2019 / Published: 5 December 2019
Early diagnosis of Parkinson’s diseases (PD) is challenging; applying machine learning (ML) models to gait characteristics may support the classification process. Comparing performance of ML models used in various studies can be problematic due to different walking protocols and gait assessment systems. The objective of this study was to compare the impact of walking protocols and gait assessment systems on the performance of a support vector machine (SVM) and random forest (RF) for classification of PD. 93 PD and 103 controls performed two walking protocols at their normal pace: (i) four times along a 10 m walkway (intermittent walk-IW), (ii) walking for 2 minutes on a 25 m oval circuit (continuous walk-CW). 14 gait characteristics were extracted from two different systems (an instrumented walkway—GAITRite; and an accelerometer attached at the lower back—Axivity). SVM and RF were trained on normalized data (accounting for step velocity, gender, age and BMI) and evaluated using 10-fold cross validation with area under the curve (AUC). Overall performance was higher for both systems during CW compared to IW. SVM performed better than RF. With SVM, during CW Axivity significantly outperformed GAITRite (AUC: 87.83 ± 7.81% vs. 80.49 ± 9.85%); during IW systems performed similarly. These findings suggest that choice of testing protocol and sensing system may have a direct impact on ML PD classification results and highlight the need for standardization for wide scale implementation. View Full-Text
Keywords: Parkinson’s disease; machine learning; classification; wearables; accelerometer; GAITRite; multi-regression normalization; SVM; random forest classifier Parkinson’s disease; machine learning; classification; wearables; accelerometer; GAITRite; multi-regression normalization; SVM; random forest classifier
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Rehman, R.Z.U.; Del Din, S.; Shi, J.Q.; Galna, B.; Lord, S.; Yarnall, A.J.; Guan, Y.; Rochester, L. Comparison of Walking Protocols and Gait Assessment Systems for Machine Learning-Based Classification of Parkinson’s Disease. Sensors 2019, 19, 5363.

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