Multimodal emotion detection of tennis players based on deep reinforcement learning

The research on multimodal emotional detection of tennis players is considered to be of great significance in terms of understanding their psychological state, improving technical performance. The problems of high detection error and low recall rate in traditional detection methods are sought to be solved. Therefore, a multimodal emotion detection method of tennis players based on deep reinforcement learning has been designed. The facial expressions, speech emotional signals, and physical behaviour emotional feature parameters of tennis players are extracted, and the obtained emotional feature parameters are used as input vectors for a multimodal emotion detection model based on deep reinforcement learning. The problem of high dimensionality of multimodal emotion parameters is addressed through the value function of reinforcement learning, and the multimodal emotion detection results of tennis players are output by the model. The experimental results demonstrate that the proposed method yields low detection error, and high recall rate.