Effectiveness of gait cycle detection using unsupervised time series analysis - Komatsu, Akira; Iwami, Takehiro (Intelligent Informatics and Biomedical Sciences (ICIIBMS), International Conference on, 2022)
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.