Research Article
GAIToe: Gait Analysis Utilizing an IMU for Toe Walking Detection and Intervention
@INPROCEEDINGS{10.1007/978-3-030-99197-5_15, author={Ghazal Ershadi and Migyeong Gwak and Jane Liu and Gichan Lee and Afshin Aminian and Majid Sarrafzadeh}, title={GAIToe: Gait Analysis Utilizing an IMU for Toe Walking Detection and Intervention}, proceedings={IoT Technologies for Health Care. 8th EAI International Conference, HealthyIoT 2021, Virtual Event, November 24-26, 2021, Proceedings}, proceedings_a={HEALTHYIOT}, year={2022}, month={3}, keywords={Idiopathic toe walking Gait analysis Wearable sensor Inertial Measurement Unit (IMU) Machine learning}, doi={10.1007/978-3-030-99197-5_15} }- Ghazal Ershadi
Migyeong Gwak
Jane Liu
Gichan Lee
Afshin Aminian
Majid Sarrafzadeh
Year: 2022
GAIToe: Gait Analysis Utilizing an IMU for Toe Walking Detection and Intervention
HEALTHYIOT
Springer
DOI: 10.1007/978-3-030-99197-5_15
Abstract
Idiopathic toe walking (ITW) is a walking pattern in which a person habitually walks on their forefoot with the absence of heel contact during the gait cycle. Gait rehabilitation can be achieved through behavior modification by employing a wearable device and giving the user immediate feedback. In this paper, we introduce GAIToe, a real-time toe walking detection and intervention platform that remotely monitors walking patterns. GAIToe utilizes an Inertial Measurement Unit (IMU) located in the insole and incorporates a machine learning model to detect different walking, sitting, and standing behaviors. GAIToe identifies these activities with 88% accuracy and provides vibration feedback following consecutive toe strikes. It also provides an Android application to transmit the data and a visual context to monitor the walking patterns. For the preliminary evaluation of GAIToe, we collected activity samples from ten healthy subjects.
