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Volume 18
Issue 10
10.3390/s18103287
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Article

A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer

by
Satyabrata Aich
1,
Pyari Mohan Pradhan
2,
Jinse Park
3,
Nitin Sethi
2,
Vemula Sai Sri Vathsa
2 and
Hee-Cheol Kim
1,*
1
Department of Computer Engineering/Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae 50834, Korea
2
Department of Electronics and Communication Engineering, IIT Roorkee, Uttarakhand 247667, India
3
Department of Neurology, Haeundae Paik Hospital, Inje University, Busan 47392, Korea
*
Author to whom correspondence should be addressed.
Sensors 2018, 18(10), 3287; https://doi.org/10.3390/s18103287
Received: 31 August 2018 / Revised: 23 September 2018 / Accepted: 26 September 2018 / Published: 30 September 2018
(This article belongs to the Section Physical Sensors)
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Abstract

One of the most common symptoms observed among most of the Parkinson’s disease patients that affects movement pattern and is also related to the risk of fall, is usually termed as “freezing of gait (FoG)”. To allow systematic assessment of FoG, objective quantification of gait parameters and automatic detection of FoG are needed. This will help in personalizing the treatment. In this paper, the objectives of the study are (1) quantification of gait parameters in an objective manner by using the data collected from wearable accelerometers; (2) comparison of five estimated gait parameters from the proposed algorithm with their counterparts obtained from the 3D motion capture system in terms of mean error rate and Pearson’s correlation coefficient (PCC); (3) automatic discrimination of FoG patients from no FoG patients using machine learning techniques. It was found that the five gait parameters have a high level of agreement with PCC ranging from 0.961 to 0.984. The mean error rate between the estimated gait parameters from accelerometer-based approach and 3D motion capture system was found to be less than 10%. The performances of the classifiers are compared on the basis of accuracy. The best result was accomplished with the SVM classifier with an accuracy of approximately 88%. The proposed approach shows enough evidence that makes it applicable in a real-life scenario where the wearable accelerometer-based system would be recommended to assess and monitor the FoG. View Full-Text
Keywords: machine learning; freezing of gait; feature extraction; prediction; wearable accelerometer; gait parameters; mean error rate machine learning; freezing of gait; feature extraction; prediction; wearable accelerometer; gait parameters; mean error rate
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MDPI and ACS Style

Aich, S.; Pradhan, P.M.; Park, J.; Sethi, N.; Vathsa, V.S.S.; Kim, H.-C. A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer. Sensors 2018, 18, 3287. https://doi.org/10.3390/s18103287

AMA Style

Aich S, Pradhan PM, Park J, Sethi N, Vathsa VSS, Kim H-C. A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer. Sensors. 2018; 18(10):3287. https://doi.org/10.3390/s18103287

Chicago/Turabian Style

Aich, Satyabrata, Pyari M. Pradhan, Jinse Park, Nitin Sethi, Vemula S.S. Vathsa, and Hee-Cheol Kim. 2018. "A Validation Study of Freezing of Gait (FoG) Detection and Machine-Learning-Based FoG Prediction Using Estimated Gait Characteristics with a Wearable Accelerometer" Sensors 18, no. 10: 3287. https://doi.org/10.3390/s18103287

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