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Open AccessArticle

A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals

1
Terenz Co., Limited, Busan 48060, Korea
2
Department of Neurology, Samsung Medical Center, School of medicine Sungkyunkwan University, Seoul 06351, Korea
3
Department of Electronics and Communication Engineering, IIT Roorkee 247667, India
4
Department of Respiratory Medicine, Haeundae Paik Hospital, Inje University, Busan 48108, Korea
5
Department of Neurology, Haeundae Paik Hospital, Inje University, Busan 48108 Korea
*
Authors to whom correspondence should be addressed.
Diagnostics 2020, 10(6), 421; https://doi.org/10.3390/diagnostics10060421
Received: 30 April 2020 / Revised: 12 June 2020 / Accepted: 18 June 2020 / Published: 20 June 2020
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
Fluctuations in motor symptoms are mostly observed in Parkinson’s disease (PD) patients. This characteristic is inevitable, and can affect the quality of life of the patients. However, it is difficult to collect precise data on the fluctuation characteristics using self-reported data from PD patients. Therefore, it is necessary to develop a suitable technology that can detect the medication state, also termed the “On”/“Off” state, automatically using wearable devices; at the same time, this could be used in the home environment. Recently, wearable devices, in combination with powerful machine learning techniques, have shown the potential to be effectively used in critical healthcare applications. In this study, an algorithm is proposed that can detect the medication state automatically using wearable gait signals. A combination of features that include statistical features and spatiotemporal gait features are used as inputs to four different classifiers such as random forest, support vector machine, K nearest neighbour, and Naïve Bayes. In total, 20 PD subjects with definite motor fluctuations have been evaluated by comparing the performance of the proposed algorithm in association with the four aforementioned classifiers. It was found that random forest outperformed the other classifiers with an accuracy of 96.72%, a recall of 97.35%, and a precision of 96.92%. View Full-Text
Keywords: Parkinson’s disease; medication state; machine learning; wearable device; “On” and “Off” Parkinson’s disease; medication state; machine learning; wearable device; “On” and “Off”
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MDPI and ACS Style

Aich, S.; Youn, J.; Chakraborty, S.; Pradhan, P.M.; Park, J.-H.; Park, S.; Park, J. A Supervised Machine Learning Approach to Detect the On/Off State in Parkinson’s Disease Using Wearable Based Gait Signals. Diagnostics 2020, 10, 421.

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