Efficient Real-Time Monitoring of Vibrations Transmitted to the Human Body

Abstract

Mechanical vibrations transmitted to the human body, particularly in sectors such as transportation, construction, and agriculture, represent a serious occupational hazard. Whole-body vibrations (WBVs), in particular, have been linked to musculoskeletal disorders and impaired motor performance. To assess such risks, the international standards define descriptors such as A(8) and vibration dose value (VDV), but traditional measurement procedures often rely on short, isolated test intervals that fail to capture the variability of real operating conditions. Moreover, existing real-time monitoring techniques require significant computational resources, making them impractical for widespread deployment on low-cost hardware. In this paper, an innovative and computationally efficient approach for the real-time monitoring of WBVs transmitted to the human body is proposed. Unlike classical methods, the proposed approach processes small segments of the vibration signal, significantly reducing computational burden while maintaining high accuracy. The system was implemented using low-cost sensors, including the ADXL335 accelerometer integrated into a custom 3D-printed seat pad, and validated through an experimental campaign conducted along the Palermo–Altofonte route using a commercial van. Vibrations were recorded and analyzed in accordance with the international standards, and real-time descriptors were calculated using both the proposed and conventional approaches. Results demonstrate that the proposed method closely matches the classical approach in terms of A(8) and VDV values, with discrepancies below 2%, while drastically reducing computation time. Specifically, the proposed approach maintains subsecond processing times even for extended acquisition periods, unlike the classical method, which exhibits severe latency when implemented on low-cost devices. This innovative solution enables cost-effective, real-time WBV monitoring under realistic working conditions, making it a valuable tool for occupational risk prevention and ergonomic assessment, especially in resource-constrained environments.