The interplay of fatigue dynamics and task achievement using optimal control predictive simulation.

Predictive simulation of human motion could provide insight into optimal techniques. In repetitive or long-duration tasks, these simulations must predict fatigue-induced adaptation. However, most studies minimize cost function terms related to actuator activations, assuming it minimizes fatigue. An additional modeling layer is needed to consider the previous use of muscles to reveal adaptive strategies to the decreased force production capability. Here, we propose interfacing Xia's three-compartment fatigue dynamics model with rigid-body dynamics. A stabilization invariant was added to Xia's model. We simulated the maximum repetition of dumbbell biceps curls as an optimal control problem (OCP) using direct multiple shooting. We explored three cost functions (minimizing torque, fatigue, or both) and two OCP formulations (full-horizon and sliding-horizon approaches). We adapted Xia's model by adding a stabilization invariant coefficients S=105 for direct multiple shooting. Sliding-horizon OCPs achieved 20 to 21 repetitions. The kinematic strategy slowly deviated from a plausible dumbbell lifting task to a swinging strategy as fatigue onset increasingly compromised the humerus to remain vertical. In full-horizon OCPs, the latter kinematic strategy was used over the whole motion, resulting in 32 repetitions. We showed that sliding-horizon OCPs revealed a reactive strategy to fatigue when only torque was included in the cost function, whereas an anticipatory strategy was revealed when the fatigue term was included in the cost function. Overall, the proposed approach has the potential to be a valuable tool in optimizing performance and helping reduce fatigue-related injuries in a variety of fields.

Accuracy and kinematics consistency of marker-based scaling approaches on a lower limb model: a comparative study with imagery data.

Medical images are not typically included in protocol of motion laboratories. Thus, accurate scaling of musculoskeletal models from optoelectronic data are important for any biomechanical analysis. The aim of the current study was to identify a scaling method based on optoelectronic data, inspired from literature, which could offer the best trade-off between accurate geometrical parameters (segment lengths, orientation of joint axes, marker coordinates) and consistent inverse kinematics outputs (kinematic error, joint angles). The methods were applied on 26 subjects and assessed with medical imagery building EOS-based models, considered as a reference. The main contribution of this paper is to show that the marker-based scaling followed by an optimisation of orientation joint axes and markers local coordinates, gives the most consistent scaling and joint angles with EOS-based models. Thus, when a non-invasive mean with an optoelectronic system is considered, a marker-based scaling is preliminary needed to get accurate segment lengths and to optimise joint axes and marker local coordinates to reduce kinematic errors.AbbrevationsAJCAnkle joint centreCKEcumulative kinematic errorDoFdegree of freedomEBEOS-basedHBheight-basedHJChip joint centreKJCknee joint centreMBmarker-basedMSMmusculoskeletal modelsSPMstatistical parametric mappingSTAsoft tissue artifactEBa.m∗EOS-based with optimised joint axes, and all model markers coordinatesMBa.m∗marker-based with optimised joint axes, and all model markers coordinatesMBl.a.mmarker-based with optimised segment lengths, joint axes, and selected model markers coordinatesASISanterior superior illiac spinePSISposterior superior illiac spine.