Integration of wearable electromyography sensors with cloud-based analytics for biomechanical overload monitoring and ergonomic evaluation

Abstract

Work-related musculoskeletal disorders (WMSDs) remain a pressing issue in industrial environments, posing both health and legal risks. This study explores the feasibility of a wearable system for assessing biomechanical overload risk using surface electromyography (EMG). The proposed approach integrates wearable EMG sensors, a mobile application, and a cloud-based information system to facilitate reliable, real-time data acquisition and preliminary analysis. A pilot experiment was conducted on a diverse group of subjects to evaluate signal processing robustness and to test four hypotheses related to physiological and ergonomic variability. Results indicate the system's potential for capturing muscle load dynamics and generating interpretable indicators of physical workload. While promising, the method's current validation is limited in scale and focused on signal-level analysis rather than long-term occupational outcomes. This study serves as a foundational step toward scalable ergonomic evaluation using wearable biosensors, with further validation required in diverse and larger industrial settings.