Research on gait recognition method based on multimodal information feature level fusion

Gait recognition, as a non-contact biometric technology, has broad application prospects in intelligent monitoring, human-computer interaction, medical rehabilitation and other fields. In response to the problems of low recognition accuracy and sensitivity to environmental changes in existing gait recognition methods, this paper proposes a multimodal information feature level fusion gait recognition method based on acceleration sensors, surface electromyography signal sensors, EEG signal sensors, and plantar force signal sensors. Accurate recognition of gait was achieved by preprocessing, feature extraction, and feature fusion of signals from four types of sensors. Experimental results show that the recognition accuracy of this method is higher than that of the single mode method in different scenes, and it has strong robustness to environmental changes. This study provides new ideas for the further development of gait recognition.