Sussex-Huawei Locomotion Recognition Using Machine Learning and Deep Learning with Multi-sensor data

Human activity recognition (HAR) has developed rapidly in recent years due to its widespread applications in motion analysis, mobile health monitoring, security, and rehabilitation. However, due to missing sensor data, complex application scenarios, poor model robustness, existing HAR algorithms still cannot meet application requirements. In this context, the Sussex-Huawei Locomotion (SHL) recognition challenge provides a dataset for improving HAR algorithms. In this study, our team (SIAT-BIT) proposes a three-branch convolutional neural network framework for SHL recognition challenge. Firstly, the data is preprocessed for feature extraction, and then three classifiers are trained in parallel using three cross-entropy loss functions. The experimental results show that the proposed model achieves the best performance with the least model parameters. In addition, we further improved the performance through post-smoothing. Finally, we get an average accuracy of 0.9274 on the validation dataset.