Mechanical loading of the low-back is an important risk factor for the development of low-back pain. Real-time estimation of the L5S1 joint moment (ML5S1) can give an insight to reduce mechanical loading. Model accuracy depends on sensor information, limiting the number of input variables to estimate ML5S1 increases practical feasibility, but may decrease accuracy. This study aimed to find a model with a limited set of input variables without a large reduction in accuracy. We compared two approaches. The first was based on a simplified inverse dynamics model (SM) that requires a limited number of input variables (EMG/ground reaction forces, and orientations derived from an optoelectronic system (OMC)). Two variations were examined, to determine to what extent arm orientations were needed. The second approach was based on a regression model (RM) that uses the SMs as ground-truth. Two variations in terms of sensor use and calibration were examined. Test trials consisted of re-stacking a stack of 3 boxes. A high-end lab-based OMC-system was used as the gold standard (GS). Fifteen healthy participants, 9 males and 6 females (age 21-30) participated in this study. R2, RMSE, and peak-difference with the GS ML5S1 estimate were compared between models with a repeated-measures ANOVA. The SM including arm sensors performed similar or better than the regression models (r > 0.9 and RMSE < 15 % of average peak moment). However, from the perspective of practical feasibility and minimizing the required number of sensors during work, the best approach would be using one of the two regression model approaches.
Keywords: Low back pain (LBP); Mechanical loading; Regression models.
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