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Please use this identifier to cite or link to this item: http://repository.iitr.ac.in/handle/123456789/8215
Title: Design of a Machine Learning-Assisted Wearable Accelerometer-Based Automated System for Studying the Effect of Dopaminergic Medicine on Gait Characteristics of Parkinson's Patients
Authors: Aich S.
Pradhan, Pyari Mohan
Chakraborty S.
Kim H.-C.
Kim H.-T.
Lee H.-G.
Kim I.H.
Joo M.-I.
Jong Seong S.
Park J.
Published in: Journal of Healthcare Engineering
Abstract: In the last few years, the importance of measuring gait characteristics has increased tenfold due to their direct relationship with various neurological diseases. As patients suffering from Parkinson's disease (PD) are more prone to a movement disorder, the quantification of gait characteristics helps in personalizing the treatment. The wearable sensors make the measurement process more convenient as well as feasible in a practical environment. However, the question remains to be answered about the validation of the wearable sensor-based measurement system in a real-world scenario. This paper proposes a study that includes an algorithmic approach based on collected data from the wearable accelerometers for the estimation of the gait characteristics and its validation using the Tinetti mobility test and 3D motion capture system. It also proposes a machine learning-based approach to classify the PD patients from the healthy older group (HOG) based on the estimated gait characteristics. The results show a good correlation between the proposed approach, the Tinetti mobility test, and the 3D motion capture system. It was found that decision tree classifiers outperformed other classifiers with a classification accuracy of 88.46%. The obtained results showed enough evidence about the proposed approach that could be suitable for assessing PD in a home-based free-living real-time environment. © 2020 Satyabrata Aich et al.
Citation: Journal of Healthcare Engineering (2020), 2020(): -
URI: https://doi.org/10.1155/2020/1823268
http://repository.iitr.ac.in/handle/123456789/8215
Issue Date: 2020
Publisher: Hindawi Limited
ISSN: 20402295
Author Scopus IDs: 56149932800
26639724100
57208747159
55739535700
7410136705
57215501911
57202402657
54787873400
57215495079
54938807200
Author Affiliations: Aich, S., Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae, South Korea
Pradhan, P.M., Department of Electronics and Communication Engineering, IIT, Roorkee, India
Chakraborty, S., Department of Computer Engineering, Inje University, Gimhae, South Korea
Kim, H.-C., Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae, South Korea
Kim, H.-T., Department of Neurology, Hanyang University Hospital, College of Medicine, Seoul, South Korea
Lee, H.-G., Department of Industrial Design, Kyoung Sung University, Busan, South Korea
Kim, I.H., Department of Oncology, Haeundae Paik Hospital, Inje University, Busan, South Korea
Joo, M.-I., Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae, South Korea
Jong Seong, S., Institute of Digital Anti-Aging Healthcare, Inje University, Gimhae, South Korea
Park, J., Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, South Korea
Funding Details: This research was supported by the National Research Foundation (NRF) of Korea grant funded by the Korea government (MSIT) (Grant number 2019R1C1C1011197) and also funded by Ministry of Trade, Industry and Energy (MOTIE), Korea, through the Education program for Creative and Industrial Convergence (Grant number N0000717).
Corresponding Author: Park, J.; Department of Neurology, Haeundae Paik Hospital, Inje UniversitySouth Korea; email: jinsepark@gmail.com
Appears in Collections:Journal Publications [ECE]

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