@inproceedings{4acbd61cf6194c7b8cd8221d874fa4d7,
title = "Lower Limb Rehabilitation Motion Angle Measurement Based On Deep Learning YOLOv3 Model",
abstract = "The aging of the population and the high incidence of hemiplegia have led to an increasing demand for easy-to-use rehabilitation training. The feedback sensing system which can measure and analyze the lower limb rehabilitation motions is highly significant for improving the rehabilitation outcome. Computer vision-based human motion angle measurement has attracted significant interest. This study aims to measure and analyze the lower limb motion angle in the sagittal plane with a single RGB camera. This paper proposes a method for extracting and monitoring of the lower limb marker points based on YOLOv3 and DarkNet-53 convolutional neural networks, and optimizes the pixel coordinates of the target point based on Kalman. The measurement accuracy of the proposed method is tested by JACO robotic arm, and the test shows that the standard deviation (SD) of the measurement is less than 0.5°.",
author = "Yunlong Yang and Liancheng Wang and Yufeng Yao and Chenxi He and Hesheng Yin",
note = "Publisher Copyright: {\textcopyright} VDE VERLAG GMBH · Berlin · Offenbach.; 2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021 ; Conference date: 17-12-2021 Through 19-12-2021",
year = "2021",
language = "英语",
series = "ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application",
publisher = "VDE Verlag GmbH",
pages = "324--329",
editor = "Xiansheng Ning and Yongxin Feng",
booktitle = "ICMLCA 2021 - 2nd International Conference on Machine Learning and Computer Application",
}