Abstract
In order to study the application of robotic technology in fitness motion detection, this study develops a modular high accuracy and low-latency Human Gesture Recognition (HGR) system. Firstly, an improved HGR algorithm is introduced, which compresses the HGR model through content extraction and reduces the number of parameters. Methods such as Simulated Annealing and Semi-Supervised Learning are introduced to improve content extraction, further improving the compression of the model. Secondly, a human pose recognition system is constructed based on Deep Learning (DL) and robotics, and each module is recommended. Finally, the effectiveness of the system is verified. The results show that when the accuracy is close to HRNet-32. The inference speed of the model is increased by about 2.4 times, and the model parameters are compressed by about 67%. The data demonstrate the effectiveness of the method. The robot-human posture detection technology can effectively solve human posture recognition in fitness exercises.
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