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Recognizing human activities from sensor data is a vital task in variousdomains, but obtaining diverse and labeled sensor data remains challenging andcostly. In this paper, we propose an unsupervised statistical feature-guideddiffusion model for sensor-based human activity recognition. The proposedmethod aims to generate synthetic time-series sensor data without relying onlabeled data, addressing the scarcity and annotation difficulties associatedwith real-world sensor data. By conditioning the diffusion model on statisticalinformation such as mean, standard deviation, Z-score, and skewness, wegenerate diverse and representative synthetic sensor data. We conductedexperiments on public human activity recognition datasets and compared theproposed method to conventional oversampling methods and state-of-the-artgenerative adversarial network methods. The experimental results demonstratethat the proposed method can improve the performance of human activityrecognition and outperform existing techniques.

Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based Human Activity Recognition