Enhancing Privacy Protection for Time-Series Signals in Ergonomics Studies via Data Synthesis

Electromyography (EMG) signal analysis is critical in understanding and treating work-related musculoskeletal disorders (WMSDs). Despite the increasing use of EMG signals combined with machine learning to assess biomechanical risks in various occupational settings, a significant shortage of extensive EMG datasets hinders progress. This shortage is primarily due to stringent data management plans and the limited availability of EMG datasets representing occupational tasks. To address this, our research leverages diffusion models to synthesize EMG signals tailored to manual material handling (MMH) tasks, aiming to enrich occupational EMG data repositories while maintaining data privacy. Using a conditional diffusion model with a residual U-Net architecture, we synthesized EMG signals for MMH activities such as pulling, pushing, and lifting. The synthesized data, evaluated across time and frequency domains, demonstrated fidelity to the original EMG signals, capturing distinct patterns and amplitudes characteristic of different tasks. Our findings highlight the potential of diffusion models in generating high-fidelity EMG data, providing a novel solution to the data scarcity challenge in occupational health research.