  Human activity recognition (HAR) from on-body sensors is a core functionality
in many AI applications: from personal health, through sports and wellness to
Industry 4.0. A key problem holding up progress in wearable sensor-based HAR,
compared to other ML areas, such as computer vision, is the unavailability of
diverse and labeled training data. Particularly, while there are innumerable
annotated images available in online repositories, freely available sensor data
is sparse and mostly unlabeled. We propose an unsupervised statistical
feature-guided diffusion model specifically optimized for wearable sensor-based
human activity recognition with devices such as inertial measurement unit (IMU)
sensors. The method generates synthetic labeled time-series sensor data without
relying on annotated training data. Thereby, it addresses 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 method to conventional oversampling and
state-of-the-art generative adversarial network methods. Experimental results
demonstrate that this can improve the performance of human activity recognition
and outperform existing techniques.
