@inproceedings{c9e8181a2ef34ea0bc7a1f619bcfca75,
title = "Knee Lift Detection using Convolutional Neural Network Method with FPGA Hardware Design",
abstract = "In this paper we propose a convolutional neural network (CNN) model to detect whether the action of knee lift is standard or not. We use HRNet as the previous part of system which can localize human anatomical keypoints to generate 2D keypoints as the input of our model. We can more easily distinguish whether the knee lift action has reached the required position or not. The simulation is based on the 2D skeleton points as the keypoints. Besides, we also implement this CNN model in FPGA hardware to reduce inference time. ",
author = "Chen, {Tzu Chieh} and Luo, {Yi Jhen} and Wan, {Wei Chung} and Tsai, {Tsung Han}",
note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 8th IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021 ; Conference date: 15-09-2021 Through 17-09-2021",
year = "2021",
doi = "10.1109/ICCE-TW52618.2021.9603148",
language = "???core.languages.en_GB???",
series = "2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2021 IEEE International Conference on Consumer Electronics-Taiwan, ICCE-TW 2021",
}