Biology subjects

Koelewijn, A. D.

Publications and source records attributed to Koelewijn, A. D..

8 recordsLinked to original sources

bioRxiv2025

From Smartphone Images to Musculoskeletal Models: Personalized Inertial Parameter Estimation

Every human body is different, however, current movement analysis does not reflect that, as it heavily relies on generic musculoskeletal models. Usually, these models are scaled to match the participants body segment lengths and body weight, but not taking individual body shape into account. This can lead to errors in the estimation of joint forces and torques, which are important to accurately estimate musculoskeletal variables. Thus, we developed a method to estimate body segment inertial parameters based on body hulls acquired via smartphone pictures. From the body hull, we infer the skeletal shape and pose, and then estimate the distribution of bone, lean, and fatty tissues. We then segment the body hull and assign each tissue type a density, which is used to calculate the body segment inertial parameters. To allow for the use of our method with existing data, we also introduce two new generic musculoskeletal models, which are based on the average standing body shapes. Validation using MRI-derived ground-truth models shows that our method creates participant-specific musculoskeletal models that are closer to the MRI-derived ground truth than scaled generic models. Additionally, we performed lab-based gait experiments to evaluate the effect of our method on residual forces and joint moments, where we found that our method leads to a reduction of residual forces of up to 14.9 % and a reduction of metabolic cost of up to 12.8 % when compared to generic musculoskeletal models. Our new generic models show similar joint moment outcomes, but less reduction of residual forces than the personalized models.

bioengineering↗

bioRxiv2025

Does reduced reactivity explain altered postural control in Parkinson's disease? A predictive simulation study.

Postural instability represents one of the cardinal symptoms of Parkinsons disease (PD). Still, internal processes leading to this instability are not fully understood. Simulations using neuromusculoskeletal human models could help understand these internal processes leading to PD-associated postural deficits. In this paper, we investigated whether reduced reactivity amplitudes resulting from impairments due to PD can explain postural instability as well as increased muscle tone as often observed in individuals with PD. To simulate reduced reactivity, we gradually decreased previously optimized gain factors within the postural control circuitry of our model performing a quiet upright standing task. After each reduction step, the model was again optimized. Simulation results were compared to experimental data collected from 31 individuals with PD and 31 age- and sex-matched healthy control participants. Analyzing our simulation results, we showed that muscle activations increased with a models reduced reactivity, as well as joint angles ranges of motion (ROMs). However, sway parameters such as center of pressure (COP) path lengths and COP ranges did not increase as observed in our experimental data. These results suggest that a reduced reactivity does not directly lead to increased sway parameters, but could cause increased muscle tone leading to subsequent postural control alterations. To further investigate postural stability using neuromusculoskeletal models, analyzing additional internal model parameters and tasks such as perturbed upright standing requiring comparable reaction patterns could provide promising results. By enhancing such models and deepening the understanding of internal processes of postural control, these models may be used to assess and evaluate rehabilitation interventions in the future. Impact StatementAltered postural control in Parkinsons disease can be partly associated to reduced reactivity. Additional changes in the neural circuitry need to be further investigated to fully explain the observed differences.

bioengineering↗

bioRxiv2024

A sensorimotor enhanced neuromusculoskeletal model for simulating postural control of upright standing

The humans upright standing is a complex control process that is not yet fully understood. Postural control models can provide insights into the bodys internal control processes of balance behaviour. Using physiologically plausible models can also help explaining pathophysiological motion behaviour. In this paper, we introduce a neuromusculoskeletal postural control model using sensor feedback consisting of somatosensory, vestibular and visual information. The sagittal plane model was restricted to effectively six degrees of freedom and consisted of nine muscles per leg. Physiological plausible neural delays were considered for balance control. We applied forward dynamic simulations and a single shooting approach to generate healthy reactive balance behaviour during quiet and perturbed upright standing. Control parameters were optimized to minimize muscle effort. We showed that our model is capable of fulfilling the applied tasks successfully. We observed joint angles and ranges of motion in physiological plausible ranges and comparable to experimental data. This model represents the starting point for subsequent simulation of pathophysiological postural control behaviour.

bioengineering↗

bioRxiv2024

Neural Networks Estimate Muscle Force in Dynamic Conditions Better than Hill-type Muscle Models

Hill-type muscle models are widely used, even though they do not accurately represent certain muscle mechanics. We explored neural networks to develop new muscle models. We trained neural networks to estimate muscle force from activation, muscle length, and muscle velocity. Training data was recorded using sonomicrometry, electromyography, and a tendon buckle on two muscles of guinea fowl. First, we compared the neural network to a Hill-type muscle model, using the same data for network training and model optimization. Second, we trained neural networks on large datasets, in a more realistic machine learning scenario. We found that neural networks generally yielded higher coefficients of determination and lower errors than Hill-type muscle models. Our neural networks performed better when estimating forces on the muscle used for training, but on another bird, than on a different muscle of the same bird, which could be explained by inaccuracies in activation and force scaling. We extracted forcelength and force-velocity relationships from the trained neural networks and found that both effects were underestimated and that both relationships were not replicated well outside of the training data distribution. We discuss suggested experimental designs to collect suitable training data and conclude that neural networks could provide an accurate alternative to Hill-type muscle models, particularly for modeling dynamic muscle behavior that is prevalent in faster movements, given a suitable training dataset, while scaling of the training data should be comparable between muscles and animals. SummaryNeural networks predict muscle forces more accurately than Hill-type muscle models, particularly under dynamic conditions. However, they struggle to replicate the force-length and force-velocity relationships well.

bioengineering↗

bioRxiv2022

Change the direction: 3D optimal control simulation by directly tracking marker and ground reaction force data

Optimal control simulations of musculoskeletal models can be used to reconstruct motions measured with optical motion capture to estimate joint and muscle kinematics and kinetics. These simulations are mutually and dynamically consistent, in contrast to traditional inverse methods. Commonly, optimal control simulations are generated by tracking generalized coordinates in combination with ground reaction forces. The generalized coordinates are estimated from marker positions using, for example, inverse kinematics. Hence, inaccuracies in the estimated coordinates are tracked in the simulation. We developed an approach to reconstruct arbitrary motions, such as change of direction motions, using optimal control simulations of 3D full-body musculoskeletal models by directly tracking marker and ground reaction force data. For evaluation, we recorded three trials each of straight running, curved running, and a v-cut for 10 participants. We reconstructed the recordings with marker tracking simulations, coordinate tracking simulations, and inverse kinematics and dynamics. First, we analyzed the convergence of the simulations and found that the wall time increased three to four times when using marker tracking compared to coordinate tracking. Then, we compared the marker trajectories, ground reaction forces, pelvis translations, joint angles, and joint moments between the three reconstruction methods. Root mean squared deviations between measured and estimated marker positions were smallest for inverse kinematics (e.g., 7.6 {+/-} 5.1 mm for v-cut). However, measurement noise and soft tissue artifacts are likely also tracked in inverse kinematics, meaning that this approach does not reflect a gold standard. Marker tracking simulations resulted in slightly higher root mean squared marker deviations (e.g., 9.5 {+/-} 6.2 mm for v-cut) than inverse kinematics. In contrast, coordinate tracking resulted in deviations that were nearly twice as high (e.g., 16.8 {+/-} 10.5 mm for v-cut). Joint angles from coordinate tracking followed the estimated joint angles from inverse kinematics more closely than marker tracking (e.g., root mean squared deviation of 1.4 {+/-} 1.8 deg vs. 3.5 {+/-} 4.0 deg for v-cut). However, we did not have a gold standard measurement of the joint angles, so it is unknown if this larger deviation means the solution is less accurate. In conclusion, we showed that optimal control simulations of change of direction running motions can be created by tracking marker and ground reaction force data. Marker tracking considerably improved marker accuracy compared to coordinate tracking. Therefore, we recommend reconstructing movements by directly tracking marker data in the optimal control simulation when precise marker tracking is required.

bioengineering↗

bioRxiv2021

Predictive Simulations of Gait with Exoskeletons that Alter Energetics

Robotic exoskeletons, designed to augment human locomotion, have the potential to restore function in those with mobility impairments and enhance it in able-bodied individuals. However, optimally controlling these devices, to work in concert with complex and diverse human users, is a challenge. Accurate model simulations of the interaction between exoskeletons and walking humans may expedite the design process and improve control. Here, we use predictive gait simulations to investigate the effect of an exoskeleton that alters the energetic consequences of walking. To validate our approach, we re-created an past experimental paradigm where robotic exoskeletons were used to shift peoples energetically optimal step frequency to frequencies higher and lower than normally preferred. To match the experimental controller, we modelled a knee-worn exoskeleton that applied resistive torques that were either proportional or inversely proportional to step frequency--decreasing or increasing the energy optimal step frequency, respectively. We were able to replicate the experiment, finding higher and lower optimal step frequencies than in natural walking under each respective condition. Our simulated resistive torques and objective landscapes resembled the measured experimental resistive torque and energy landscapes. Individual muscle energetics revealed distinct coordination strategies consistent with each exoskeleton controller condition. Predicted step frequency and energetic outcomes were best achieved by increasing the number of virtual participants (varying whole-body anthropometrics), rather than number of muscle parameter sets (varying muscle anthropometrics). In future, our approach can be used to design controllers in advance of human testing, to help identify reasonable solution spaces or tailor design to individual users.

bioengineering↗

bioRxiv2019

A Solution Method for Predictive Simulations in a Stochastic Environment

This short communication describes a method to solve predictive simulations of human movements in a stochastic environment using a collocation method. The optimization is performed over multiple noisy episodes of the trajectory, instead of a single episode in a deterministic environment. Each episode used the same control parameters. The method was verified on a torque-driven pendulum swing-up problem. A different optimal trajectory was found in a stochastic environment than in the deterministic environment. Secondly, it was applied to gait to show its application in human movements. We show that nonzero minimum foot clearance during swing is energetically optimal in a stochastic environment. The amount of clearance increased with the noise amplitude.

bioengineering↗

bioRxiv2019

Metabolic cost calculations of gait using musculoskeletal energy models, a comparison study

This paper compares predictions of metabolic energy expenditure in gait using seven metabolic energy expenditure models to assess their correlation with experimental data. Ground reaction forces, marker data, and pulmonary gas exchange data were recorded for six walking trials at combinations of two speeds, 0.8 m/s and 1.3 m/s, and three inclines, -8% (downhill), level, and 8% (uphill). The metabolic cost, calculated with the metabolic energy models was compared to the metabolic cost from the pulmonary gas exchange rates. A repeated measures correlation showed that all models correlated well with experimental data, with correlations of at least 0.9. The model by Bhargava et al. [7] and the model by Lichtwark and Wilson [21] had the highest correlation, 0.96. The model by Margaria [23] predicted the increase in metabolic cost following a change in dynamics best in absolute terms.

physiology↗