Coupled artificial neural networks to estimate 3D whole-body posture, lumbosacral moments, and spinal loads during load-handling activities.

Biomechanical modeling approaches require body posture to evaluate the risk of spine injury during manual material handling. The procedure to measure body posture via motion-analysis techniques as well as the subsequent calculations of lumbosacral moments and spine loads by, respectively, inverse-dynamic and musculoskeletal models are complex and time-consuming. We aim to develop easy-to-use yet accurate artificial neural networks (ANNs) that predict 3D whole-body posture (ANNposture), segmental orientations (ANNangle), and lumbosacral moments (ANNmoment) based on our measurements during load-handling activities. Fifteen individuals each performed 135 load-handling activities by reaching (0 kg) or handling (5 and 10 kg) weights located at nine different horizontal and five vertical (0, 30, 60, 90, and 120 cm from the floor) locations. Whole-body posture was measured via a motion capture system and lumbosacral moments were calculated via a 3D top-down eight link-segment inverse-dynamic model. ANNposture, ANNangle, and ANNmoment were trained (RMSEs = 6.7 cm, 29.8°, and 16.2 Nm, respectively) and their generalization capability was tested (RMSE = 7.0 cm and R2 = 0.97, RMSE = 29.9° and R2 = 0.85, and RMSE = 16.5 Nm and R2 = 0.97, respectively). These ANNs were subsequently coupled to our previously-developed/validated ANNload, which predicts spinal loads during 3D load-handling activities. The results showed outputs of the coupled ANNs for L4-L5 intradiscal pressure (IDPs) during a number of activities were in agreement with measured IDPs (RMSE = 0.37 MPa and R2 = 0.89). Hence, coupled ANNs were found to be robust tools to evaluate posture, lumbosacral moments, spinal loads, and thus risk of injury during load-handling activities.