Knee Lift Detection using Convolutional Neural Network Method with FPGA Hardware Design
Tzu-Chieh Chen, Yi-Jhen Luo, Wei-Chung Wan, Tsung‐Han Tsai · 2021
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.