Development of Postures Estimation System for Walking Training

Zhihao Zheng, Xing Li, Shigeyuki Tateno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this era of sub-health, walking has been proclaimed as the best sport because it can gradually enhance body function and relieve stress. However, bad walking postures during sports time could cause unnoticed bad effect on the spine, joints, and muscles. It is necessary to develop a system to monitor the walking postures and remind people of the bad postures. There are some limitations of traditional gait monitoring systems, such as the scant number of walking postures to be detected and the specific using environment. This paper presents a walking postures estimation system with inertial measurement unit (IMU) that is able to detect the walking postures correctly, meanwhile the device of the system is easy to be attached. This system uses random forest method with the features using acceleration data and gyro date collected by IMU to classify walking postures such as bow legs and knock knees. According to the classification results, the presented system is able to classify different walking postures of users correctly.

Original languageEnglish
Title of host publication2018 International Automatic Control Conference, CACS 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538662786
DOIs
Publication statusPublished - 2019 Jan 9
Event2018 International Automatic Control Conference, CACS 2018 - Taoyuan, Taiwan, Province of China
Duration: 2018 Nov 42018 Nov 7

Publication series

Name2018 International Automatic Control Conference, CACS 2018

Conference

Conference2018 International Automatic Control Conference, CACS 2018
CountryTaiwan, Province of China
CityTaoyuan
Period18/11/418/11/7

Fingerprint

Units of measurement
Sports
Muscle
Health
Classify
Monitoring
Unit
Spine
Random Forest
Gait
Monitoring System
Date
Monitor
Training
Necessary

ASJC Scopus subject areas

  • Control and Optimization
  • Modelling and Simulation

Cite this

Zheng, Z., Li, X., & Tateno, S. (2019). Development of Postures Estimation System for Walking Training. In 2018 International Automatic Control Conference, CACS 2018 [8606746] (2018 International Automatic Control Conference, CACS 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CACS.2018.8606746

Development of Postures Estimation System for Walking Training. / Zheng, Zhihao; Li, Xing; Tateno, Shigeyuki.

2018 International Automatic Control Conference, CACS 2018. Institute of Electrical and Electronics Engineers Inc., 2019. 8606746 (2018 International Automatic Control Conference, CACS 2018).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Zheng, Z, Li, X & Tateno, S 2019, Development of Postures Estimation System for Walking Training. in 2018 International Automatic Control Conference, CACS 2018., 8606746, 2018 International Automatic Control Conference, CACS 2018, Institute of Electrical and Electronics Engineers Inc., 2018 International Automatic Control Conference, CACS 2018, Taoyuan, Taiwan, Province of China, 18/11/4. https://doi.org/10.1109/CACS.2018.8606746
Zheng Z, Li X, Tateno S. Development of Postures Estimation System for Walking Training. In 2018 International Automatic Control Conference, CACS 2018. Institute of Electrical and Electronics Engineers Inc. 2019. 8606746. (2018 International Automatic Control Conference, CACS 2018). https://doi.org/10.1109/CACS.2018.8606746
Zheng, Zhihao ; Li, Xing ; Tateno, Shigeyuki. / Development of Postures Estimation System for Walking Training. 2018 International Automatic Control Conference, CACS 2018. Institute of Electrical and Electronics Engineers Inc., 2019. (2018 International Automatic Control Conference, CACS 2018).
@inproceedings{49a83653df954d7989a99cbd660435c2,
title = "Development of Postures Estimation System for Walking Training",
abstract = "In this era of sub-health, walking has been proclaimed as the best sport because it can gradually enhance body function and relieve stress. However, bad walking postures during sports time could cause unnoticed bad effect on the spine, joints, and muscles. It is necessary to develop a system to monitor the walking postures and remind people of the bad postures. There are some limitations of traditional gait monitoring systems, such as the scant number of walking postures to be detected and the specific using environment. This paper presents a walking postures estimation system with inertial measurement unit (IMU) that is able to detect the walking postures correctly, meanwhile the device of the system is easy to be attached. This system uses random forest method with the features using acceleration data and gyro date collected by IMU to classify walking postures such as bow legs and knock knees. According to the classification results, the presented system is able to classify different walking postures of users correctly.",
author = "Zhihao Zheng and Xing Li and Shigeyuki Tateno",
year = "2019",
month = "1",
day = "9",
doi = "10.1109/CACS.2018.8606746",
language = "English",
series = "2018 International Automatic Control Conference, CACS 2018",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2018 International Automatic Control Conference, CACS 2018",

}

TY - GEN

T1 - Development of Postures Estimation System for Walking Training

AU - Zheng, Zhihao

AU - Li, Xing

AU - Tateno, Shigeyuki

PY - 2019/1/9

Y1 - 2019/1/9

N2 - In this era of sub-health, walking has been proclaimed as the best sport because it can gradually enhance body function and relieve stress. However, bad walking postures during sports time could cause unnoticed bad effect on the spine, joints, and muscles. It is necessary to develop a system to monitor the walking postures and remind people of the bad postures. There are some limitations of traditional gait monitoring systems, such as the scant number of walking postures to be detected and the specific using environment. This paper presents a walking postures estimation system with inertial measurement unit (IMU) that is able to detect the walking postures correctly, meanwhile the device of the system is easy to be attached. This system uses random forest method with the features using acceleration data and gyro date collected by IMU to classify walking postures such as bow legs and knock knees. According to the classification results, the presented system is able to classify different walking postures of users correctly.

AB - In this era of sub-health, walking has been proclaimed as the best sport because it can gradually enhance body function and relieve stress. However, bad walking postures during sports time could cause unnoticed bad effect on the spine, joints, and muscles. It is necessary to develop a system to monitor the walking postures and remind people of the bad postures. There are some limitations of traditional gait monitoring systems, such as the scant number of walking postures to be detected and the specific using environment. This paper presents a walking postures estimation system with inertial measurement unit (IMU) that is able to detect the walking postures correctly, meanwhile the device of the system is easy to be attached. This system uses random forest method with the features using acceleration data and gyro date collected by IMU to classify walking postures such as bow legs and knock knees. According to the classification results, the presented system is able to classify different walking postures of users correctly.

UR - http://www.scopus.com/inward/record.url?scp=85062405402&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85062405402&partnerID=8YFLogxK

U2 - 10.1109/CACS.2018.8606746

DO - 10.1109/CACS.2018.8606746

M3 - Conference contribution

T3 - 2018 International Automatic Control Conference, CACS 2018

BT - 2018 International Automatic Control Conference, CACS 2018

PB - Institute of Electrical and Electronics Engineers Inc.

ER -