Prediction of the thorax/pelvis orientations and L5–S1 disc loads during various static activities using neuro-fuzzy

Spinal posture including thorax/pelvis orientations as well as loads on the intervertebral discs are crucial parameters in biomechanical models and ergonomics to evaluate the risk of low back injury. In vivo measurement of spinal posture toward estimation of spine loads requires the common motion capture techniques and laboratory instruments that are costly and time-consuming. Hence, a closed loop algorithm including an artificial neural network (ANN) and fuzzy logic is proposed here to predict the L5–S1 segment loads and thorax/pelvis orientations in various 3D reaching activities. Two parts namely a fuzzy logic strategy and an ANN from this algorithm; the former, developed based on the measured postures of 20 individuals, is to determine 3D orientations of the thorax/pelvis during the various activities while the latter, developed based on the predicted L5–S1 loads by a detailed musculoskeletal model of the spine, is to estimate compression/shear forces at the L5–S1 disc. The fuzzy logic rules are extracted based on Sugeno inference engine and the ANN is trained by LevenbergMarquardt algorithm. To evaluate the performance of the proposed strategy, the comparison between the predicted values, the target values and the presented values in the literature are reviewed. The comparison demonstrated that the proposed algorithm had a promising performance. The maximum relative error for all predictions was ~19 % and with respect to the target values while this error for the literature’s values was ~37 %. Also, the average improvement of the proposed strategy was ~17 % with respect to the presented strategy in the literature.