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Occupationally-relevant low back disorders are an important health concern in the workplace. Measuring workers' exposure to biomechanical risk factors such as non-neutral postures and high force exertions is an important step for managing and mitigating the risk of such cumulative disorders. Accurate and precise quantification of exposures to biomechanical risk factors in non-repetitive material handling present unique challenges, particularly in jobs where the magnitude of hand loads vary during the workday and/or are difficult to measure directly (e.g., warehousing, construction). Wireless wearable inertial sensors offer new opportunities to acquire continuous posture data in situ and has received considerable interest in ergonomics research. This research explores several emerging issues and opportunities afforded by body-worn inertial sensing technology for quantifying biomechanical exposures and low back disorder risk particularly in relation to nonrepetitive Manual Material Handling (MMH) jobs.
The specific goal of this research is to develop, implement, and assess a framework that combines occupational biomechanics principles and empirical findings, wearable inertial sensing, and predictive modeling for quantifying biomechanical exposures associated with low back disorder risk in non-repetitive MMH work. The research is specifically targeting manual load carriage, lifting and lowering, which represent common MMH tasks associated with an increased risk of low back disorders.
The below video is showing the true vs. predicted task duration, type, and intensity while the sample participant is performing MMH tasks. Low-back compression force (Fc) can be estimated (bottom graph in the video) based on the measured posture, predicted task duration, type, and intensity to quantify the worker's biomechanical exposures and low back disorder risk.
More details can be found on:
Lim, S., & D’Souza, C. (2020). Measuring Effects of Two-Handed Side and Anterior Load Carriage on Thoracic-Pelvic Coordination using Wearable Gyroscopes, Sensors, 20(18), 5206, pg: 1-28. DOI: 10.3390/s20185206
Lim, S., & D’Souza, C. (2020). A Narrative Review on Contemporary and Emerging Uses of Inertial Sensing in Occupational Ergonomics, International Journal of Industrial Ergonomics, 76. DOI: 10.1016/j.ergon.2020.102937
Lim, S., & D’Souza, C. (2019). Statistical Prediction of Load Carriage Mode and Magnitude from Inertial Sensor Derived Gait Kinematics, Applied Ergonomics, 76, 1-11. DOI: 10.1016/j.apergo.2018.11.007
Lim, S., & D’Souza, C. (2019). Gender and Parity in Statistical Prediction of Anterior Carry Hand-Loads from Inertial Sensor Data. Proceedings of the 63rd Annual Meeting of the Human Factors and Ergonomics Society (HFES), Seattle, WA, October 2019. DOI: 10.1177/1071181319631193
Lim, S., & D’Souza, C. (2018). Inertial Sensor-based Measurement of Thoracic-Pelvic Coordination Measures Predicts Hand-Load Levels in Two-handed Anterior Carry. Proceedings of the 62nd Annual Meeting of the Human Factors and Ergonomics Society (HFES), Philadelphia, PA, October 2018, pg: 798 - 799. DOI: 10.1177/1541931218621181
Lim, S., & D’Souza, C. (2017). Statistical Prediction of Hand Force Exertion Levels in a Simulated Push Task using Posture Kinematics. Proceedings of the 61st Annual Meeting of the Human Factors and Ergonomics Society (HFES), Austin, TX, October 2017. DOI: 10.1177/1541931213601741
Lim, S., Case, A., & D’Souza, C. (2016). Comparative Analysis of Inertial and Optical Motion Capture Derived Kinematics during Isometric Push-Pull Exertions. Proceedings of the 60th Annual Meeting of the Human Factors and Ergonomics Society (HFES), Washington, DC, September 2016, pg: 970 - 974. DOI: 10.1177/1541931213601224
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