Unsupervised Statistical Feature-Guided Diffusion Model for Sensor-based
  Human Activity Recognition
Si Zuo Vitor Fortes Rey Sungho Suh Stephan Sigg Paul Lukowicz
Recognizing human activities from sensor data is a vital task in various
domains, but obtaining diverse and labeled sensor data remains challenging and
costly. In this paper, we propose an unsupervised statistical feature-guided
diffusion model for sensor-based human activity recognition. The proposed
method aims to generate synthetic time-series sensor data without relying on
labeled data, addressing the scarcity and annotation difficulties associated
with real-world sensor data. By conditioning the diffusion model on statistical
information such as mean, standard deviation, Z-score, and skewness, we
generate diverse and representative synthetic sensor data. We conducted
experiments on public human activity recognition datasets and compared the
proposed method to conventional oversampling methods and state-of-the-art
generative adversarial network methods. The experimental results demonstrate
that the proposed method can improve the performance of human activity
recognition and outperform existing techniques.