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.

包括胸部/骨盆方向以及椎间盘负荷在内的脊柱姿势是生物力学模型和人体工程学中评估下背部受伤风险的关键参数。体内测量脊柱姿势以估算脊柱负荷需要昂贵且耗时的通用运动捕捉技术和实验室仪器。因此，这里提出了一种包含人工神经网络（ANN）和模糊逻辑的闭环算法，以预测在各种3D到达活动中L5–S1段的载荷和胸部/骨盆的方向。该算法分为两部分，即模糊逻辑策略和人工神经网络。前者是根据20个人的测量姿势开发的，用于确定各种活动期间胸腔/骨盆的3D方向，而后者则是 根据详细的脊柱肌肉骨骼模型，基于预测的L5–S1负荷开发的方法是估计L5–S1椎间盘的压缩/剪切力。基于Sugeno推理机提取模糊逻辑规则，并用LevenbergMarquardt算法训练ANN。为了评估所提出策略的性能，回顾了文献中预测值，目标值和给出值之间的比较。比较表明，该算法具有良好的性能。所有预测的最大相对误差约为19％，相对于目标值，而文献值的最大相对误差约为37％。同样，相对于文献中提出的策略，提出的策略的平均改进为〜17％。