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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/8036
Title: A validation study of freezing of gait (FoG) detection and machine-learning-based FoG prediction using estimated gait characteristics with a wearable accelerometer
Authors: Aich S.
Pradhan, Pyari Mohan
Park J.
Sethi N.
Vathsa V.S.S.
Kim H.-C.
Published in: Sensors (Switzerland)
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. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.
Citation: Sensors (Switzerland) (2018), 18(10): -
URI: https://doi.org/10.3390/s18103287
http://repository.iitr.ac.in/handle/123456789/8036
Issue Date: 2018
Publisher: MDPI AG
Keywords: Feature extraction
Freezing of gait
Gait parameters
Machine learning
Mean error rate
Prediction
Wearable accelerometer
ISSN: 14248220
Author Scopus IDs: 56149932800
26639724100
54938807200
57204012949
57204021664
55739535700
Author Affiliations: Aich, S., Department of Computer Engineering, Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae, 50834, South Korea
Pradhan, P.M., Department of Electronics and Communication Engineering, IIT, Roorkee, Uttarakhand 247667, India
Park, J., Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, 47392, South Korea
Sethi, N., Department of Electronics and Communication Engineering, IIT, Roorkee, Uttarakhand 247667, India
Vathsa, V.S.S., Department of Electronics and Communication Engineering, IIT, Roorkee, Uttarakhand 247667, India
Kim, H.-C., Department of Computer Engineering, Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae, 50834, South Korea
Funding Details: Funding: This research was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF), supported by the Ministry of Science, ICT & Future Planning (NRF-2017R1D1A3B04032905) and also funded by 2016 Inje University specialization project, and the APC was funded by Basic Science Research Program through the National Research Foundation of Korea (NRF), supported by the Ministry of Science, ICT & Future Planning (NRF-2017R1D1A3B04032905), and by Inje University.
Corresponding Author: Kim, H.-C.; Department of Computer Engineering, Institute of Digital Anti-Aging Healthcare, Inje UniversitySouth Korea; email: heeki@inje.ac.kr
Appears in Collections:Journal Publications [ECE]

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