Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Sobinov, Anton; Boots, Matthew T.; Gritsenko, Valeriya; Fisher, Lee E.; Gaunt, Robert A.; Yakovenko, Sergiy

doi:10.1101/759878

Cited by 3 publications

(11 citation statements)

References 53 publications

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“…The goal of this development was to bypass costly calculations of geometrical transformations with high-quality approximations. Previously, we have demonstrated high-fidelity of these approximations with kinematic errors below 1% (Sobinov et al, 2019). These polynomial models of muscle posture-dependent state were used to develop an ANN-based approximation method for the musculoskeletal dynamics in this study.…”

Section: Methodsmentioning

confidence: 93%

“…We have previously developed the method of autogenerated polynomial models (Sobinov et al, 2019). In these polynomials, the composition of terms was expanded using objective information measurements, i.e., the corrected Akaike Information Criterion.…”

Section: Methodsmentioning

confidence: 99%

“…We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets were generated by our previous phenomenological model based on the autogenerated polynomial structures (Sobinov et al, 2019). Both models achieved a similar level of errors: ANN model errors were 0.08±0.05% for muscle lengths and 0.53±0.29% for moment arms, and LGB model made similar errors—0.18±0.06% and 0.13±0.07%, respectively.…”

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confidence: 99%

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Solving musculoskeletal biomechanics with machine learning

Smirnov

Попов

et al. 2020

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Deep learning is a relatively new computational technique for the description of the musculoskeletal dynamics. The experimental relationships of muscle geometry in different postures are the high-dimensional spatial transformations that can be approximated by relatively simple functions, which opens the opportunity for machine learning applications. In this study, we challenged general machine learning algorithms with the problem of approximating the posture-dependent moment arm and muscle length relationships of the human arm and hand muscles. We used two types of algorithms, light gradient boosting machine (LGB) and fully connected artificial neural network (ANN) solving the wrapping kinematics of 33 muscles spanning up to six degrees of freedom (DOF) each for the arm and hand model with 18 DOFs. The input-output training and testing datasets were generated by our previous phenomenological model based on the autogenerated polynomial structures (Sobinov et al., 2019). Both models achieved a similar level of errors: ANN model errors were 0.08±0.05% for muscle lengths and 0.53±0.29% for moment arms, and LGB model made similar errors--0.18±0.06% and 0.13±0.07%, respectively. LGB model reached the training goal with only 10^3 samples, while ANN required 10^6 samples; however, LGB models were about 39 slower than ANN models in the evaluation. The sufficient performance of developed models demonstrates the future applicability of machine learning for musculoskeletal transformations in a variety of applications, such as in advanced powered prosthetics.

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Section: Methodsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

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Solving musculoskeletal biomechanics with machine learning

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Попов

et al. 2020

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“…"# values, pennation angles, or values. The moment arm and muscle length values from the validated model can also be extracted for real-time applications (Sobinov et al, 2019), which provides a valuable resource for human-machine interfaces.…”

Section: Discussionmentioning

confidence: 99%

“…The simulated muscle moment arm and length values were acquired from the OpenSim model in a uniform grid with 9 points spanning the maximal range of each DOF (Sobinov et al, 2019). This created 9 d unique postures per muscle, where d is the number of DOFs a muscle spans.…”

Section: Dataset 3 Simulated Muscle Moment Measurementsmentioning

confidence: 99%

Functional and Structural Moment Arm Validation for Musculoskeletal Models: A Study of the Human Forearm and Hand

Hardesty

Sobinov

et al. 2020

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The development of realistic musculoskeletal models is a fundamental goal for the theoretical progress in sensorimotor control and its engineering applications, e.g., in the biomimetic control of artificial limbs. Yet, accurate models require extensive experimental measures to validate structural and functional parameters describing muscle state over the full physiological range of motion. However, available experimental measurements of, for example, muscle moment arms are sparse and often disparate. Validation of these models is not trivial because of the highly complex anatomy and behavior of human limbs. In this study, we developed a method to validate and scale kinematic muscle parameters using posture-dependent moment arm profiles, isometric force measurements, and a computational detection of assembly errors. We used a previously published model with 18 degrees of freedom (DOFs) and 32 musculotendon actuators with force generated from a Hill-type muscle model. The muscle path from origin to insertion with wrapping geometry was used to model the muscle lengths and moment arms. We simulated moment arm profiles across the full physiological range of motion and compared them to an assembled dataset of published and merged experimental profiles. The muscle paths were adjusted using custom metrics based on root-mean-square and correlation coefficient of the difference between simulated and experimental values. Since the available measurements were sparse and the examination of high-dimensional muscles is challenging, we developed analyses to identify common failures, i.e., moment arm functional flipping due to the sign reversal in simulated moments and the imbalance of force generation between antagonistic groups in postural extremes. The validated model was used to evaluate the expected errors in torque generation with the assumption of constant instead of the posture-dependent moment arms at the wrist flexion-extension DOF, which is the critical joint in our model with the largest number of crossing muscles. We found that there was a reduction of joint torques by about 35% in the extreme quartiles of the wrist DOF. The use of realistic musculoskeletal models is essential for the reconstruction of hand dynamics. These models may improve the understanding of muscle actions and help in the design and control of artificial limbs in future applications. New & NoteworthyRealistic models of human limbs are a development goal required for the understanding of motor control and its applications in biomedical fields. However, developing accurate models is restrained by the lack of accurate and reliable musculoskeletal measurements in humans. Here, we have overcome this challenge by using multi-stage validation of simulated structures using both experimental data and the identification of structural failures in the high-dimensional muscle paths. We demonstrate that the rigorous structural and functional validation method is essential for the understanding of force generation at the wrist.

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Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

Bahdasariants

Barela

Gritsenko

et al. 2023

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The nervous system predicts and executes complex motion of body segments actuated by the coordinated action of muscles. When a stroke or other traumatic injury disrupts neural processing, the impeded behavior has not only kinematic but also kinetic attributes that require interpretation. Biomechanical models could allow medical specialists to observe these dynamic variables and instantaneously diagnose mobility issues that may otherwise remain unnoticed. However, the real-time and subject-specific dynamic computations necessitate the optimization these simulations. In this study, we explored the effects of intrinsic viscoelasticity, choice of numerical integration method, and decrease in sampling frequency on the accuracy and stability of the simulation. The bipedal model with 17 rotational degrees of freedom (DOF)—describing hip, knee, ankle, and standing foot contact—was instrumented with viscoelastic elements with a resting length in the middle of the DOF range of motion. The accumulation of numerical errors was evaluated in dynamic simulations using swing-phase experimental kinematics. The relationship between viscoelasticity, sampling rates, and the integrator type was evaluated. The optimal selection of these three factors resulted in an accurate reconstruction of joint kinematics (err < 1%) and kinetics (err < 5%) with increased simulation time steps. Notably, joint viscoelasticity reduced the integration errors of explicit methods and had minimal to no additional benefit for implicit methods. Gained insights have the potential to improve diagnostic tools and accurize real-time feedback simulations used in the functional recovery of neuromuscular diseases and intuitive control of modern prosthetic solutions.

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Approximating complex musculoskeletal biomechanics using multidimensional autogenerating polynomials

Cited by 3 publications

References 53 publications

Solving musculoskeletal biomechanics with machine learning

Solving musculoskeletal biomechanics with machine learning

Functional and Structural Moment Arm Validation for Musculoskeletal Models: A Study of the Human Forearm and Hand

Does joint impedance improve dynamic leg simulations with explicit and implicit solvers?

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