This study introduces a novel wearable Inertial Measurement Unit (IMU)-based system for an objective and comprehensive assessment of Work-Related Musculoskeletal Disorders (WMSDs), thus enhancing workplace safety. The system integrates wearable technology with a user-friendly interface, providing magnetometer-free orientation estimation, joint angle measurements, and WMSDs risk evaluation. Tested in a cable manufacturing facility, the system was evaluated with ten female employees. The evaluation involved work cycle identification, inter-subject comparisons, and benchmarking against standard WMSD risk assessments like RULA, REBA, Strain Index, and Rodgers Muscle Fatigue Analysis. The evaluation demonstrated uniform joint patterns across participants ($ICC=0.72\pm 0.23$) and revealed a higher occurrence of postures warranting further investigation, which is not easily detected by traditional methods such as RULA. The experimental results showed that the proposed system’s risk assessments closely aligned with the established methods and enabled detailed and targeted risk assessments, pinpointing specific bodily areas for immediate ergonomic interventions. This approach not only enhances the detection of ergonomic risks but also supports the development of personalized intervention strategies, addressing common workplace issues such as tendinitis, low back pain, and carpal tunnel syndrome. The outcomes highlight the system’s sensitivity and specificity in identifying ergonomic hazards. Future efforts should focus on broader validation and exploring the relative influence of various WMSDs risk factors to refine risk assessment and intervention strategies for improved applicability in occupational health.

This study addresses challenges in sensor fusion for accurate and robust joint orientation estimation in human movement analysis using wearable inertial measurement units (IMUs). A magnetometer-free refined Kalman filter (KF) approach is presented and validated to address various indoor environmental constraints and challenges posed by human movement. These include variability in motion and dynamics, as well as magnetic disturbances caused by ferromagnetic materials or electronic interferences. Our proposed approach utilizes a Kalman-filter-based framework that analyzes the accelerometer’s alignment with the Earth’s frame to estimate orientation and correct gyroscope readings, eliminating reliance on magnetometer inputs. The algorithm was tested on both controlled robotic movements and real-world upper-limb-motion-monitoring scenarios. First, a comparative analysis was conducted on the double-stage Kalman filter (DSKF) and complementary filter using the collected robot motion encoder data. The results demonstrated superior performance in orientation estimation, particularly in yaw measurements, where the proposed method significantly improved accuracy. It achieved a lower root mean square error (RMSE = $ {2.447}^{\circ } $ ) and mean absolute error (MAE = $ {2.006}^{\circ } $ ), outperforming both the DSKF and complementary filter approaches. Additionally, the study’s findings were validated against a standard motion capture system, revealing error metrics within generally acceptable ranges ( $ \le 12.4\% $ of the joint range of motion [ROM]) and strong correlation coefficients ( $ {r}^2>0.89 $ ). However, some deviations were observed during complex motion cycle intervals, highlighting opportunities for further refinement. These findings suggest that the proposed approach presents a promising alternative for human joint orientation estimation in industrial settings with magnetic distortions.

Occupational ApplicationsIn this study, we present force myography (FMG) as an objective method for quantifying physical effort. We utilized a noninvasive wearable FMG sensor to capture the radial force exerted by muscles during contractions, and we evaluated the effectiveness of FMG in assessing varying perceived exertion levels. We found a strong correlation between FMG measurements and perceived exertion, which highlights its potential as an indicator of physical effort. Integrating FMG sensors could allow for monitoring of worker exertion, facilitating adjustments to workloads and the design of ergonomic tools and interfaces. Doing so could help identify risks of fatigue and musculoskeletal disorders and provide a valuable metric for refining job design and ergonomics assessments, ultimately contributing to healthier workplaces and enhanced worker productivity.