A movement generation algorithm for FE Human Body Models

Martynenko, Oleksandr; Schmitt, Syn; Bayer, A.; Blaschke, J.; Mayer, Christian

doi:10.1002/pamm.201710070

Cited by 2 publications

(1 citation statement)

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“…As we see significant force changes in the dynamic perturbations originating from the muscle's passive characteristics (Figure 9), these new findings may also influence the response. Ultimately, for the study of internal contact forces, finite-element models may allow a more detailed analysis (Röhrle et al, 2016) but significantly increase the complexity of finding an adequate controller (Martynenko et al, 2017).…”

Section: Model Assumptions and Limitationsmentioning

confidence: 99%

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Stollenmaier

Ilg

Haeufle

2020

Front. Bioeng. Biotechnol.

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Human movement is generated by a dynamic interplay between the nervous system, the biomechanical structures, and the environment. To investigate this interaction, we propose a neuro-musculoskeletal model of human goal-directed arm movements. Using this model, we simulated static perturbations of the inertia and damping properties of the arm, as well as dynamic torque perturbations for one-degree-of freedom movements around the elbow joint. The controller consists of a feed-forward motor command and feedback based on muscle fiber length and contraction velocity representing short-latency (25 ms) or long-latency (50 ms) stretch reflexes as the first neuronal responses elicited by an external perturbation. To determine the open-loop control signal, we parameterized the control signal resulting in a piecewise constant stimulation over time for each muscle. Interestingly, such an intermittent open-loop signal results in a smooth movement that is close to experimental observations. So, our model can generate the unperturbed point-to-point movement solely by the feed-forward command. The feedback only contributed to the stimulation in perturbed movements. We found that the relative contribution of this feedback is small compared to the feed-forward control and that the characteristics of the musculoskeletal system create an immediate and beneficial reaction to the investigated perturbations. The novelty of these findings is (1) the reproduction of static as well as dynamic perturbation experiments in one neuro-musculoskeletal model with only one set of basic parameters. This allows to investigate the model's neuro-muscular response to the perturbations that-at least to some degree-represent stereotypical interactions with the environment; (2) the demonstration that in feed-forward driven movements the muscle characteristics generate a mechanical response with zero-time delay which helps to compensate for the perturbations; (3) that this model provides enough biomechanical detail to allow for the prediction of internal forces, including joint loads and muscle-bone contact forces which are relevant in ergonomics and for the development of assistive devices but cannot be observed in experiments.

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Section: Model Assumptions and Limitationsmentioning

confidence: 99%

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Stollenmaier

Ilg

Haeufle

2020

Front. Bioeng. Biotechnol.

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Upper limb movement simulation and biomechanical characteristics during human movement

Wang

2022

International Journal of Emerging Electric Power Systems

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The movement process of the human body is not the movement process of a single limb, but the movement process of skeletal muscles that coordinate multiple adjacent limbs with joints as the hub. Human body movement has different actions and links. When observing the human body movement mechanism, introducing the body movement chain can maintain the integrity and independence of the movement system. The upper limb of the human body is a kinematic chain with multiple limbs and multiple degrees of freedom, which can perform various complex movements. This article mainly introduces the upper limb movement simulation and biomechanical characteristics analysis during human movement, and intends to provide some ideas and directions for the upper limb movement simulation and biomechanical characteristics research during human movement. This paper proposes the research methods of upper limb motion simulation and biomechanical characteristics analysis during human movement, summarizes the human upper limb physiological structure and the relevant theoretical knowledge of human body biomechanics, and proposes the human upper limb motion capture and the human upper limb posture description algorithm for the human body Simulation experiment of upper limb movement during exercise. The experimental results of this paper show that the overall prediction time of simulation using MSCNN is only 0.0065 s, which ensures the real-time prediction.

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A movement generation algorithm for FE Human Body Models

Cited by 2 publications

References 9 publications

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Predicting Perturbed Human Arm Movements in a Neuro-Musculoskeletal Model to Investigate the Muscular Force Response

Upper limb movement simulation and biomechanical characteristics during human movement

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