Taekwondo motion image recognition model based on hybrid neural network algorithm for wearable sensor of Internet of Things

Conventional IoT wearable sensor Taekwondo motion image recognition model mainly uses Anchor fixed proportion whole body target anchor frame to extract recognition features, which is vulnerable to dynamic noise, resulting in low displacement recognition rate of motion image. Therefore, a new IoT wearable sensor Taekwondo motion image recognition model needs to be designed based on hybrid neural network algorithm. That is, the wearable sensor Taekwondo motion image features are extracted, and the hybrid neural network algorithm is used to generate the optimization model of the wearable sensor Taekwondo motion image recognition of the Internet of Things, so as to achieve effective recognition of Taekwondo motion images. The experimental results show that the designed wearable sensor of the Internet of Things based on the hybrid neural network algorithm has a high recognition rate of the motion image displacement of the Taekwondo motion image recognition model, which proves that the designed Taekwondo motion image recognition model has good recognition effect, reliability, and certain application value, and has made certain contributions to optimizing the Taekwondo movement.