Fitness-Assisted Counting Based on Lightweight Pose Recognition Network and k-NN Classification
Yong Li, Qiang Wu · 2023
In this paper, propose a fitness assistance algorithm based on the combination of a lightweight network human posture detection model, BlazePose, and a k-NN classification algorithm for real-time counting on mobile devices. Firstly, the BlazePose model is used to predict the target pose on the given sample image, and the obtained Landmark is normalized to build the training set of the k-NN classifier. Then Pose Classification Colab classifies each sample by targeting the entire training set while excluding outliers and underrepresented categories. Finally, the implementation of the classifier itself can realize the real-time counting of fitness exercises. In this study, squats and push-ups, two of the most representative sports, are selected as the verification objects. The experimental results show that, with real-time and accuracy guaranteed This algorithm is capable of classifying and counting push-ups and squats at a rate of 30 frames on an ordinary mobile device.