Emotion Recognition Based on Adaptive Fusion of Multiple Gait Features

In the development of most existing gait-based emotion recognition methods, feature fusion has not been sufficiently studied; therefore, these methods have failed to fully utilize various gait features, resulting in poor performance. In this study, an emotion recognition method is developed, based on the adaptive fusion of multiple gait features. In this method, spatiotemporal features, reconstructed features, and psychology-based affective features are extracted from gait data. Spatiotemporal features capture the dynamic changes in gait patterns, reconstructed features focus on the structural aspects of gait, and psychology-based affective features provide insights into an individual's emotional state. Subsequently, an adaptive fusion strategy is used to dynamically weigh the importance of the three gait features, thereby achieving a more comprehensive representation of the individual's emotional state. Finally, ten-fold cross-validation is performed on a dataset containing four emotion categories, followed by training and testing the model on a real-world emotion-gait dataset. Experimental results show that in multi-label classification tasks, the model proposed herein improves the mean Average Precision (mAP) by two percentage points compared with the state-of-the-art TAEW method. Furthermore, in multiclass classification tasks, the accuracy of the model proposed herein is 1.88 percentage point higher than that of the STEP method. These results indicate that the method effectively leverages the spatiotemporal, reconstructed, and psychology-based affective features of gait, thereby providing a robust and accurate approach to emotion recognition.