Estimation of Gait Parameters based on Motion Sensor Data

Kaitai Li, Cong-Cong Zhou

2020

Abstract

Recently, the spreading application of intelligent mobile devices with integrated sensors such as inertial measurement units (IMU) has attracted the interest of the researchers for designing gait analysis methods based on the captured sensor data. This paper focuses on designing a system which can evaluate the walking ability and the physical agility level of normal people and people with Parkinson’s disease or stroke. The motion signal is collected by three wearable MPU9250 sensors located on both ankles and the center of the waist. Three test scenarios, including 10 meters walking test (10MWT), Time up and go test (TUGT) and Dual-task walking (DTW), are designed in this paper. The results, which concluded time parameters such as standing up time and turning back time as well as walking parameters such as stride length and stride frequency, showed good consistency and high accuracy with Vicon device.

Paper Citation

in Harvard Style

Li K. and Zhou C. (2020). Estimation of Gait Parameters based on Motion Sensor Data. In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES; ISBN 978-989-758-398-8, SciTePress, pages 129-135. DOI: 10.5220/0008963901290135

in Bibtex Style

@conference{biodevices20,
author={Kaitai Li and Cong-Cong Zhou},
title={Estimation of Gait Parameters based on Motion Sensor Data},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES},
year={2020},
pages={129-135},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008963901290135},
isbn={978-989-758-398-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2020) - Volume 1: BIODEVICES
TI - Estimation of Gait Parameters based on Motion Sensor Data
SN - 978-989-758-398-8
AU - Li K.
AU - Zhou C.
PY - 2020
SP - 129
EP - 135
DO - 10.5220/0008963901290135
PB - SciTePress