A Method of Self-Supervised Denoising and Classification for Sensor-Based Human Activity Recognition

Human activity recognition (HAR) is an important subfield of pervasive computing and pattern recognition. While researchers have achieved remarkable results in feature extraction and classification for sensor-based HAR, they have encountered performance bottlenecks. Sensor signal denoising has emerged as an excellent approach to enhance the performance of sensor-based HAR architectures. In this study, we propose a novel self-supervised blind denoising method for sensor signals, which serves as a new module in the HAR task and significantly improves the overall system performance. Our method applies the idea of masking from image inpainting to temporal signal processing, and utilizes the neighboring signal to predict the signal at the center, leveraging independent noisy measurements and temporal relationships between real neighboring signals, without the need to understand the noise distribution. The denoising function is learned from samples, and the denoised signals are then fed into the classification model. Experimental results conducted on benchmark datasets WISDM, UCI-HAR, and PAMAP2 demonstrate the effectiveness of our denoising method. After denoising, the inner-class divergence of actions decreases, while the outer-class divergence increases. As a result, the recognition accuracy of the system significantly improves to 98.6%, 97.64%, and 97.12% on the respective datasets.