| Title |
Stride Length Estimation based on Plantar Pressure data during Walking with 1D CNN |
| Authors |
호종갑(Jong Gab Ho) ; 정아현(A Hyun Jung) ; 최지희(Ji Hee Choe) ; 민세동(Se Dong Min) |
| DOI |
https://doi.org/10.5370/KIEE.2023.72.11.1420 |
| Keywords |
Gait; Plantar pressure; Stride length estimation; 1D Convolutional neural networks |
| Abstract |
Gait analysis is an essential component of clinical examination. In particular, stride length is used as an important indicator in personal health management. In this study, an evaluation study was conducted on the feasibility of estimating stride length based on a deep-learning model using only plantar pressure data. For the experiment, 10 subjects were recruited and plantar pressure data and gait movies were collected while walking. From the gait data, one stride length of raw data, center of pressure, and gait cycle index were extracted. afterward, three datasets were built and used as input deep learning models. As a result, the performance of the 1D CNN model was the best, with MAE of 3.57 ± 2.64 cm and MARE of 2.82%, confirming the feasibility of step length estimation based on plantar pressure data. The results of this study can be used for personal health monitoring and PDR estimation research. |