Ankhzaya, Jamsrandorj Nguyen, Quynh Hoang Ngan Jung, Da Woon Min Seok Baek Mun, Kyung Ryoul Kim, Jin wook 2024-01-12T02:45:34Z 2024-01-12T02:45:34Z 2023-11-28 2023-07-25 https://pubs.kist.re.kr/handle/201004/76406 https://arinex.com.au/EMBC/content.html Vision-based gait analysis can play an important role in the remote and continuous monitoring of the elderly’s health conditions. However, most vision-based approaches compute gait spatiotemporal parameters using human pose information and provide average parameters. This study aimed to propose a straightforward method for stride-by-stride gait spatiotemporal parameters estimation. A total of 160 elderly individuals participated in this study. Data were gathered with a GAITRite system and a mobile camera simultaneously. Three deep learning networks were trained with a few RGB frames as input and a continuous 1D signal containing both spatial and temporal gait parameters as output. The trained networks estimated the stride lengths with correlations of 0.938 and more and detected gait events with F1-scores of 0.914 and more. IEEE Engineering in Medicine and Biology Society Image-based Gait Spatiotemporal Parameters Estimation using a Single Camera and CNN-Transformer Hybrid Network Conference 10.1109/EMBC40787.2023.10339950 1 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 45th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) AT Sydney, Australia 2023-07-24 2023 45TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY, EMBC 001133788300008 2-s2.0-85179644874