@inproceedings{04877fb1493046238846027a8bb7e038,
title = "Deep-learning-based human motion tracking for rehabilitation applications using 3D image features",
abstract = "Motion rehabilitation is increasingly required owing to an aging population and suffering of stroke, which means human motion analysis must be valued. Based on the concept mentioned above, a deep-learning-based system is proposed to track human motion based on three-dimensional (3D) images in this work; meanwhile, the features of traditional red green blue (RGB) images, known as two-dimensional (2D) images, were used as a comparison. The results indicate that 3D images have an advantage over 2D images due to the information of spatial relationships, which implies that the proposed system can be a potential technology for human motion analysis applications.",
author = "Chen, {Kai Yu} and Zheng, {Wei Zhong} and Lin, {Yu Yi} and Tang, {Shih Tsang} and Chou, {Li Wei} and Lai, {Ying Hui}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; null ; Conference date: 20-07-2020 Through 24-07-2020",
year = "2020",
month = jul,
doi = "10.1109/EMBC44109.2020.9176120",
language = "English",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "803--807",
booktitle = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society",
address = "United States",
}