Effectiveness of gait cycle detection using unsupervised time series analysis

In this study, we use a time series analysis method to detect the gait cycle from foot pressure sensor data attached to the heel and thenar and verify the usefulness of this method. After attaching thin foot pressure sensors to the heel and thenar of one healthy male subject, a gait measurement expe...

Full description

Saved in:
Bibliographic Details
Published inIntelligent Informatics and Biomedical Sciences (ICIIBMS), International Conference on Vol. 7; pp. 318 - 319
Main Authors Komatsu, Akira, Iwami, Takehiro
Format Conference Proceeding
LanguageEnglish
Published IEEE 24.11.2022
Subjects
Biomedical measurement
change point detection
foot pressure sensor
Gait analysis
gait cycle
Legged locomotion
Phase measurement
Pressure measurement
Pressure sensors
Stairs
Time series analysis
Online AccessGet full text

Cover

More Information
Summary:In this study, we use a time series analysis method to detect the gait cycle from foot pressure sensor data attached to the heel and thenar and verify the usefulness of this method. After attaching thin foot pressure sensors to the heel and thenar of one healthy male subject, a gait measurement experiment was conducted on a straight path. The measured foot pressure data were normalized to the presence or absence of ground contact (stance phase and swing phase), and a gait cycle waveform was generated. The change finder was used to detect the gait cycle from the original waveform of the foot pressure sensor, and the results were compared with the normalized gait cycle waveform. As a result, the waveforms detected by time series analysis were generally in good agreement with the original gait cycle waveforms. However, a delay was observed at the end of the stance phase.
ISSN:2189-8723
DOI:10.1109/ICIIBMS55689.2022.9971502