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한국생산제조학회 학술지 영문 홈페이지
[ Papers ]
Journal of the Korean Society of Manufacturing Technology Engineers - Vol. 31, No. 2, pp.108-115
ISSN: 2508-5107 (Online)
Print publication date 15 Apr 2022
Received 14 Mar 2022 Revised 31 Mar 2022 Accepted 05 Apr 2022
DOI: https://doi.org/10.7735/ksmte.2022.31.2.108

액티브 시니어를 위한 머신 러닝 기반 거동 분석 시스템 구축

함상우a ; 유창영b ; 정재윤a ; 장웅기a, b ; 김병희a, b ; 최성욱a, b ; 하석진a, b ; 하호진a, b, *
Gait Analysis System for Active Seniors Based on Machine Learning
Sang-Woo Hama ; Chang-Young Yoob ; Jae-Youn Junga ; Woong-Ki Janga, b ; Byeong-Hee Kima, b ; Seong-Wook Choia, b ; Suk-Jin Haa, b ; Ho-Jin Haa, b, *
aBiohealth-Machinery Convergence Engineering, Kangwon National University
bDepartment of Smart Health Science and Technology, Kangwon National University

Correspondence to: *Tel.: +82-33-250-6310 E-mail address: hojinha@kangwon.ac.kr (Ho-Jin Ha).

Abstract

Gait impairment is one of the main symptoms of neurodegenerative diseases such as dementia, Parkinson’s disease, etc. For this reason, lots of previous studies tried to develop new methodologies based on statical analysis for predicting brain diseases. Statistical analysis is a good choice for solving most engineering problems. However, neurodegeneration patients cannot wait for progression because of their limited time. In this study, we focused on analysis time reduction. We acquired ten sets of the gait sample by Arduino pro micro using the MPU6050 accelerometer. The sampling frequency was 200Hz, and data were acquired on the x, y, and z acceleration, pitch, roll, and yaw. The raw data sets were pre-processed to 100 normal and 60 abnormal gait data, where ten were used in the test, and the others in the study of the machine. The machine learning achieved an 80% total accuracy at the end of this study.

Keywords:

Machine learning, Gait recognition, Active senior, Condition monitoring, Healthcare, Aged society

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Sang-Woo Ham

Ph.D. Candidate in the Interdisciplinary Program in Biohealth-Machinery Convergence Engineering, Graduate School, Kangwon National University. His research interest is Hydro Turbo Machinery.

E-mail: it-hing@kangwon.ac.kr

Chang-Young Yoo

Doctoral Course Student in the Department of Smart Health Science and Technology, Graduate School, Kangwon National University. His research interests are in the development of biomedical instrument.

E-mail: yoo458@kangwon.ac.kr

Jae-Youn Jung

Master of biotechnology, Kangwon National University. His research interests are Metabolic and Generic Engineering.

E-mail: jaeyun9321@gmail.com

Woong-Ki Jang

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is Micro/nano scale surface texturing technologies and digital twin.

E-mail: wkddndrl@kangwon.ac.kr

Byeong-Hee Kim

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is micro and nano system design and precision control of machine tools.

E-mail: kbh@kangwon.ac.kr

Seong-Wook Choi

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interest is developing biomedical instrument.

E-mail: swche@kangwon.ac.kr

Suk-Jin Ha

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interests are Metabolic and Generic Engineering.

E-mail: sjha@kangwon.ac.kr

Ho-Jin Ha

Professor in the Department of Smart Health Science and Technology, Kangwon National University. His research interests are PIV, 4D Flow MRI In-vitro cardiovascular and CFD.

E-mail: hojinha@kangwon.ac.kr