Section: New Results


Ergonomics has become an important application domains in MimeTIC: being able to capture, analyze, and model human performance at work. In this domain, key challenge consists in using limited equipment to capture the physical activity of workers in real conditions. Hence, in 2019, we have designed a new approach to predict external forces using mainly motion capture data, and to personnalize the biomechanical capabilities (maximum feasible force/torque) of specific population.

Motion-based Prediction of External Forces

Participants : Charles Pontonnier [contact] , Georges Dumont, Claire Livet, Anthony Sorel, Nicolas Bideau.

We proposed [21] a method to predict the external efforts exerted on a subject during handling tasks, only with a measure of his motion. These efforts are the contacts forces and moments on the ground and on the load carried by the subject. The method is based on a contact model initially developed to predict the ground reaction forces and moments. Discrete contact points are defined on the biomechanical model at the feet and the hands. An optimization technique computes the minimal forces at each of these points satisfying the dynamic equations of the biomechanical model and the load. The method was tested on a set of asymmetric handling tasks performed by 13 subjects and validated using force platforms and an instrumented load. For each task, predictions of the vertical forces obtained a RMSE of about 0.25 N/kg for the feet contacts and below 1 N/kg for the hands contacts. This method enables to quantitatively assess asymmetric handling tasks on the basis of kinetics variables without additional instrumentation such as force sensors and thus improve the ecological aspect of the studied tasks. We evaluated this method [23] on manual material handling (MMH) tasks. From a set of hypothesized contact points between the subject and the environment (ground and load), external forces were calculated as the minimal forces at each contact point while ensuring the dynamics equilibrium. Ground reaction forces and moments (GRF&M) and load contact forces and moments (LCF&M) were computed from motion data alone. With an inverse dynamics method, the predicted data were then used to compute kinetic variables such as back loading. On a cohort of 65 subjects performing MMH tasks, the mean correlation coefficients between predicted and experimentally measured GRF for the vertical, antero-posterior and medio-lateral components were 0.91 (0.08), 0.95 (0.03) and 0.94 (0.08), respectively. The associated RMSE were 0.51 N/kg, 0.22 N/kg and 0.19 N/kg. The correlation coefficient between L5/S1 joint moments computed from predicted and measured data was 0.95 with a RMSE of 14 Nm for the flexion / extension component. This method thus allows the assessment of MMH tasks without force platforms, which increases the ecological aspect of the tasks studied and enables performance of dynamic analyses in real settings outside the laboratory.

This method was successfully applied [24] on lunge motion that is a fundamental attack of modern fencing, asking for a high level of coordination, speed and accuracy. It consists in an explosive extension of the front leg accompanying an extension of the sword arm. In such motions, the direction of action and the way feet are oriented – guard position - are particularly challenging for a GRF&M prediction method. These methods are avalaible in CusToM software [22].

Biomechanics for Motion Analysis-Synthesis and Analysis of Torque Generation Capacities

Participants : Charles Pontonnier [contact] , Georges Dumont, Nicolas Bideau, Guillaume Nicolas, Pierre Puchaud.

Characterization of muscle mechanism through the torque-angle and torque-velocity relationships [17] is critical for human movement evaluation and simulation. In-vivo determination of these relationships through dynamometric measurements and modelling is based on physiological and mathematical aspects. However, no investigation regarding the effects of the mathematical model and the physiological parameters underneath these models was found. The purpose of the current study was to compare the capacity of various torque-angle and torque-velocity models to fit experimental dynamometric measurement of the elbow and provide meaningful mechanical and physiological information. Therefore, varying mathematical function and physiological muscle parameters from the literature were tested. While a quadratic torque-angle model seemed to increase predicted to measured elbow torque fitting, a new power-based torque-velocity parametric model gave meaningful physiological values with similar fitting results to a classical torque-velocity model. This model is of interest to extract modelling and clinical knowledge characterizing the mechanical behavior the joint. Based on the same kind of methods, we proposed [25] to analyse torque generation capacities of a human knee. The torque generation capacities are often assessed for human performance, as well as for prediction of internal forces through musculoskeletal modelling. Scaling individual strength generation capacities is challenging but can provide physiologically meaningful perspectives. We propose to fit the models to isokinetic measurements of joint torques in different angle and angular velocity conditions. Assuming muscles are viscoelastic actuators, their entire architectures contribute to Joint Torque-Angle and Torque-Velocity Relationships (JTAR and JTVR respectively, and their coupling JTAVR) at the joint level. Experimental observation at different scales (muscle sarcomere, muscle fibre and joint) resulted in various JTAR models available in the literature. On the other side, JVTR models are often modelled without obvious physiological consistency. The above mentionned JTVR model was shown to increase physiological transparency of the elbow JTAVR. As those results might be joint-specific, we extended it to evaluate five JTAR and two JTVR models on the knee flexion and extension.