Gait recognition using deep learning with handling defective data from multiple wearable sensors

Abstract

Gait recognition based on multiple wearable sensors has received widespread attention in recent years. Collecting data from various types of multimodal sensors allows for a more comprehensive capture of gait information. This effectively enhances gait recognition performance. However, the increase in the number of sensors also leads to the possibility of generating defective data during the process of data collection and processing, particularly data redundancy and data anomaly. Traditional solutions often involve complex data analysis and selection, which may result in important information being filtered out and high computational demands. To address these issues, this paper proposes a new method that combines the powerful feature extraction capabilities of convolutional neural networks. To address the issue of data redundancy, the Channel Multiscale-aware (CMSA) Module is designed. It extracts information with different receptive fields from different channels of the same feature map to directly reduce similarities in information. To address the issue of data anomaly, the Feature Combination based on Local Channel Attention (FCLCA) Module is designed to select channel segments least affected by data anomaly from the feature map. Attention mechanisms are incorporated into both the CMSA and FCLCA modules to facilitate the model in adapting and learning more crucial features. Additionally, improvements are made to their pooling structures. After numerous experiments, it was demonstrated that our model achieved superior performance in gait recognition tasks.