The optimal technique for individual speed skaters remains poorly understood, due to the complex interplay of technique variables (like stroke frequency, skate trajectory and push-off mechanics). Optimization with a biomechanical model can help to identify the most efficient techniques for individual skaters. This research aimed to use a validated model of a speed skater (van der Kruk et al. 2017) within an optimization framework to investigate how the optimal speed skating techniques on the straightaways are influenced by individual characteristics and environmental conditions.

Finding the optimal technique that either minimizes effort at a target velocity or maximizes velocity, was formulated as an optimal control problem and solved using direct collocation. Across different optimizations, stroke frequency, mass, leg length, air and ice friction and limits on average and maximal power were incrementally varied.

Variations in velocity and stroke frequency most clearly influenced the optimal technique. Conditions requiring less energy (optimizations for low velocity, low ice or air friction), optimized towards energy-efficient strokes with longer gliding phases and minimal lateral forces. Conditions with higher speeds and frequencies converged to longer, forceful push-offs. These push-offs maximized leg extension by descending into a deep crouched position to emphasize a sideways push-off. Generally, optimized techniques adapted a small steer angle during the gliding phase to prioritize forward gain, and larger steering angles during the push-off to direct push-off forces forward. Optimizations for higher frequencies adopted more narrow strokes, and reached higher maximized speeds. Regarding personal characteristics, increasing the model's average and maximal power limits most significantly increased maximal velocity.

Exo devices allows users to decrease muscle effort by offloading it to the exo device. Compliance in the physical human-robot interface, originating from flexible cuffs and soft body tissue, can affect the forces transferred. The design of the cuff can alter the value of the pHEI compliance, but it is not yet known to what extent changes in pHEI compliance affect the muscle activation. Due to human variances and the difficulty of measuring objective performance metrics, it is difficult to experimentally pin point cause and effect. Therefore a novel model based method was used which combined experimentally obtained pHEI compliance data with musculoskeletal models to simulate the effect of pHEI compliance on muscle activation. A case study using this method was performed on a subject wearing a passive shoulder exotendon suit. Results indicate a large effect of cuff design on stiffness, damping and cuff migration values. Furthermore, optimising a rigid model and adding compliance afterwards resulted in an increase of total normalised muscle cost. Results from this compliant simulation let to results more closely representing EMG data from another study. Finally, optimising a compliant model results in a total normalised muscle cost equal to that of the rigid case, increased robustness of the exo to configuration errors and increased comfort. This indicates that compliance does not have a detrimental effect on optimised performance when taken into consideration during optimisation.

Introduction: The BMX start is crucial for race performance, often measured by the time to the kink at 3.15 m from the start.
Objective: This study aims to optimize the BMX SX gate start using predictive optimal control techniques, focusing on the effects of maximal crank torque and reaction time on performance.
Method: Two models were used: The ‘upper extremities’ model analysed varying crank torques (250 Nm to 350 Nm) and reaction times (0.14 s, 0.16 s, 0.18 s).
The ‘two legs’ model was assessed under a single condition to better reproduce crank torque and track experimentally measured kinematics.
Results: Higher crank torques led to more forward initial positions, reduced recoil, and increased final velocities. The velocity of the start gate was a limiting factor initially, with timing and technique being crucial until the gate is halfway open. Reaction time variations showed minor effects on performance, and no strategy adaptation was needed within the tested range. The ‘two legs’ model accurately tracked experimental kinematics with low RMSE values. The predictive simulation with the ‘two legs’ model showed an improvement in kink time. The kink time for the predictive optimal control solution was 1.15 s compared to 1.23 s in the experimental trial.
Conclusion: This framework for researching the BMX start using predictive optimal control offers a systematic basis for future research. Insights can improve training strategies focusing on technique, timing, and initial start position. Future research could explore the effects of leg strength, hip range of motion, and gear ratios or crank lengths on performance.