The SES framework and Frequency domain information fusion strategy for Human activity recognition
Han Wu, Haotian Feng, Lida Shi, Hongda Zhang, Hao Xu · 2024
Human activity recognition (HAR) is a task designed to identify and classify physical activities or behaviors in people’s daily lives. This field relies on data collected from various sensors. Due to differences in environment, user posture and habits, as well as equipment placement, the data exhibits severe heterogeneity. The channel information from different sensors exhibits more complex and uncertain characteristics in terms of shape, noise level, and data quality. These properties can lead to poor generalization of deep learning models, posing challenges for the effectiveness of deep learning algorithms and the widespread use of specific embedded devices in this field. Therefore, this article proposes an adaptive channel signal scaling method to calibrate channel characteristics from the perspective of sensor channel information. Additionally, we propose a stable feature completion strategy to enrich the data feature information using different fusion strategies at the data level. Extensive experiments were conducted on three publicly available HAR datasets, and the experimental results demonstrate that our proposed method significantly improves the performance of state-of-the-art deep learning methods.