Analysis of optimal control problem formulations in skeletal movement predictions

Postprint (published version

A factorization-based algorithm to predict EMG data using only kinematics information

EMG analyses have several applications, such as identifying muscle excitation patterns during rehabilitation or training plans, or controlling EMG-driven devices. However, experimental measurements can be time consuming or difficult to obtain. This study presents a simple algorithm to predict EMG signals that can be applied in real time during running, given only the instantaneous vector of kinematics. We hypothesize that the factorization of the kinematics of the skeleton together with the EMG data of calibration subjects could be used to predict EMG data of another subject only using the kinematic information. The results showed that EMG signals of lower-limb muscles can be predicted accurately in less than a second using this method. Correlation coefficients between predicted and experimental EMG signals were higher than 0.7 in ten out of eleven muscles for most prediction trials and subjects, and their overall median value was higher than 0.8. These values confirm that this method could be used to accurately predict EMG signals in real time when only kinematics are measured.Peer ReviewedPostprint (author's final draft

Influence of coordinate and dynamic formulations on solving biomechanical optimal control problems

Postprint (published version

Influence of musculoskeletal model parameter values on prediction of accurate knee contact forces during walking

Treatment design for musculoskeletal disorders using in silico patient-specific dynamic simulations is becoming a clinical possibility. However, these simulations are sensitive to model parameter values that are difficult to measure experimentally, and the influence of uncertainties in these parameter values on the accuracy of estimated knee contact forces remains unknown. This study evaluates which musculoskeletal model parameters have the greatest influence on estimating accurate knee contact forces during walking. We performed the evaluation using a two-level optimization algorithm where musculoskeletal model parameter values were adjusted in the outer level and muscle activations were estimated in the inner level. We tested the algorithm with different sets of design variables (combinations of optimal muscle fiber lengths, tendon slack lengths, and muscle moment arm offsets) resulting in nine different optimization problems. The most accurate lateral knee contact force predictions were obtained when tendon slack lengths and moment arm offsets were adjusted simultaneously, and the most accurate medial knee contact force estimations were obtained when all three types of parameters were adjusted together. Inclusion of moment arm offsets as design variables was more important than including either tendon slack lengths or optimal muscle fiber lengths alone to obtain accurate medial and lateral knee contact force predictions. These results provide guidance on which musculoskeletal model parameter values should be calibrated when seeking to predict in vivo knee contact forces accurately.Postprint (updated version

Analysis of muscle synergies and activation–deactivation patterns in subjects with anterior cruciate ligament deficiency during walking

The knowledge of muscle activation patterns when doing a certain task in subjects with anterior cruciate ligament deficiency could help to improve their rehabilitation treatment. The goal of this study is to identify differences in such patterns between anterior cruciate ligament–deficient and healthy subjects during walking. Methods Electromyographic data for eight muscles were measured in a sample of eighteen subjects with anterior cruciate ligament deficiency, in both injured (ipsilateral group) and non-injured (contralateral group) legs, and a sample of ten healthy subjects (control group). The analysis was carried out at two levels: activation-–deactivation patterns and muscle synergies. Muscle synergy components were calculated using a non-negative matrix factorization algorithm. Findings The results showed that there was a higher co-contraction in injured than in healthy subjects. Although all muscles were activated similarly since all subjects developed the same task (walking), some differences could be observed among the analyzed groups. Interpretation The observed differences in the synergy components of injured subjects suggested that those individuals alter muscle activation patterns to stabilize the knee joint. This analysis could provide valuable information for the physiotherapist to identify alterations in muscle activation patterns during the follow-up of the subject’s rehabilitation.Postprint (author's final draft

Validity of neural networks to determine body position on the bicycle

Peer ReviewedPostprint (author's final draft

Criterion validity of neural networks to assess lower limb motion during cycling

The use of marker-less methods to automatically obtain kinematics of movement is expanding but validity to high-velocity tasks such as cycling with the presence of the bicycle on the field of view is needed when standard video footage is obtained. The purpose of this study was to assess if pre-trained neural networks are valid for calculations of lower limb joint kinematics during cycling. Motion of twenty-six cyclists pedalling on a cycle trainer was captured by a video camera capturing frames from the sagittal plane whilst reflective markers were attached to their lower limb. The marker-tracking method was compared to two established deep learning-based approaches (Microsoft Research Asia-MSRA and OpenPose) to estimate hip, knee and ankle joint angles. Poor to moderate agreement was found for both methods, with OpenPose differing from the criterion by 4–8° for the hip and knee joints. Larger errors were observed for the ankle joint (15–22°) but no significant differences between methods throughout the crank cycle when assessed using Statistical Parametric Mapping were observed for any of the joints. OpenPose presented stronger agreement with marker-tracking (criterion) than the MSRA for the hip and knee joints but resulted in poor agreement for the ankle joint.Peer ReviewedPostprint (author's final draft

Modifications to the net knee moments lead to the greatest improvements in accelerative sprinting performance: a predictive simulation study

The current body of sprinting biomechanics literature together with the front-side mechanics coaching framework provide various technique recommendations for improving performance. However, few studies have attempted to systematically explore technique modifications from a performance enhancement perspective. The aims of this investigation were therefore to explore how hypothetical technique modifications affect accelerative sprinting performance and assess whether the hypothetical modifications support the front-side mechanics coaching framework. A three-dimensional musculoskeletal model scaled to an international male sprinter was used in combination with direct collocation optimal control to perform (data-tracking and predictive) simulations of the preliminary steps of accelerative sprinting. The predictive simulations differed in the net joint moments that were left ‘free’ to change. It was found that the ‘knee-free’ and ‘knee-hip-free’ simulations resulted in the greatest performance improvements (13.8% and 21.9%, respectively), due to a greater knee flexor moment around touchdown (e.g., 141.2 vs. 70.5 Nm) and a delayed and greater knee extensor moment during stance (e.g., 188.5 vs. 137.5 Nm). Lastly, the predictive simulations which led to the greatest improvements were also found to not exhibit clear and noticeable front-side mechanics technique, thus the underpinning principles of the coaching framework may not be the only key aspect governing accelerative sprinting.Peer ReviewedPostprint (published version

The Influence of Neuromusculoskeletal Model Calibration Method on Predicted Knee Contact Forces during Walking

This study explored the influence of three model calibration methods on predicted knee contact and leg muscle forces during walking. Static optimization was used to calculate muscle activations for all three methods. Approach A used muscle-tendon model parameter values (i.e., optimal muscle fiber lengths and tendon slack lengths) taken directly from literature. Approach B used a simple algorithm to calibrate muscle-tendon model parameter values such that each muscle operated within the ascending region of its normalized force-length curve. Approach C used a novel two-level optimization procedure to calibrate muscle-tendon, moment arm, and neural control model parameter values while simultaneously predicting muscle activations

Synergy-Based Two-Level Optimization for Predicting Knee Contact Forces during Walking

Musculoskeletal models and optimization methods are combined to calculate muscle forces. Some model parameters cannot be experimentally measured due to the invasiveness, such as the muscle moment arms or the muscle and tendon lengths. Moreover, other parameters used in the optimization, such as the muscle synergy components, can be also unknown. The estimation of all these parameters needs to be validated to obtain physiologically consistent results. In this study, a two-step optimization problem was formulated to predict both muscle and knee contact forces of a subject wearing an instrumented knee prosthesis. In the outer level, muscle parameters were calibrated, whereas in the inner level, muscle activations were predicted. Two approaches are presented. In Approach A, contact forces were used when calibrating the parameters, whereas in Approach B, no contact force information was used as input. The optimization formulation is validated comparing the model and the experimental knee contact forces. The goal was to evaluate whether we can predict the contact forces when in-vivo contact forces are not available