Abstract
The risk of low-back pain in manual material handling could potentially be reduced by back-support exoskeletons. Preferably, the level of exoskeleton support relates to the required muscular effort, and therefore should be proportional to the moment generated by trunk muscle activities. To this end, a regression-based prediction model of this moment could be implemented in exoskeleton control. Such a model must be calibrated to each user according to subject-specific musculoskeletal properties and lifting technique variability through several calibration tasks. Given that an extensive calibration limits the practical feasibility of implementing this approach in the workspace, we aimed to optimize the calibration for obtaining appropriate predictive accuracy during work-related tasks, i.e., symmetric lifting from the ground, box stacking, lifting from a shelf, and pulling/pushing. The root-mean-square error (RMSE) of prediction for the extensive calibration was 21.9 nm (9% of peak moment) and increased up to 35.0 nm for limited calibrations. The results suggest that a set of three optimally selected calibration trials suffice to approach the extensive calibration accuracy. An optimal calibration set should cover each extreme of the relevant lifting characteristics, i.e., mass lifted, lifting technique, and lifting velocity. The RMSEs for the optimal calibration sets were below 24.8 nm (10% of peak moment), and not substantially different than that of the extensive calibration.
Highlights
Published: 23 December 2021Low back loading during manual material handling in the workplace is a risk factor for low back pain [1,2]
Developers need to decide on the optimal level of desirable support that is required in manual material handling activities [4]
The predictive accuracy for EMG-Driven Muscle Model (EMGMod) and Regression Model (RegMod) varied for different subjects, trials, and calibration sets
Summary
Published: 23 December 2021Low back loading during manual material handling in the workplace is a risk factor for low back pain [1,2]. To reduce low back loading during manual material handling, spring-based or actuated back-support exoskeletons can be used to provide support [3]. The magnitude of the support is determined by the exoskeleton control strategy and generated by actuated components. Developers need to decide on the optimal level of desirable support that is required in manual material handling activities [4]. For back-support exoskeleton control, the desirable support could be determined based on an estimate of MHuman. These moments are produced by active forces through muscle activation [7], together with passive forces generated through strain of tissues such as muscles, tendons, ligaments, and fascia [8].
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