Estimation of Upper-Limb Joint Torques in Static and Dynamic Phases for Lifting Tasks

In this paper, we propose learning architectures to emulate the inverse dynamics step in motion analysis. Indeed, the in situ motion analysis of a work situation is often based on noisy and/or incomplete motion data (video, depth camera...), requiring the development of methods robust to these uncertainties. Our study focuses on the development and evaluation on reference data (opto-electronic motion capture) of a torque estimation tool for upper limbs. The system was trained to estimate joint torques for static and dynamic one-handed load carrying tasks, based on the estimated position of the joint centers, the mass carried and the mass of the subject. The generalizability of our learning models was tested in inter-subject and inter-task scenarios. The average RMSE $$\text {(N.m)}$$ and the average nRMSE $${(\%)}$$ metrics were computed for each type of learning architecture. In a future work, we aim at emulating noisy data as an input of the problem to emulate in situ conditions and improve the robustness of the approach.