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The nervous systems of animals evolved to exert dynamic control of behavior in response to the needs of the animal and changing signals from the environment. To understand the mechanisms of dynamic control, we need a means of predicting how individual neural and body elements will interact to produce the performance of the entire system. We have developed a neuromechanical application named AnimatLab that addresses this problem through simulation. A computational model of a body and nervous system can be constructed from simple components and situated in a virtual world for testing. Simulations and live experiments were used to investigate questions about locust jumping. The neural circuitry and biomechanics of kicking in locusts have been extensively studied. It has been hypothesized that the same neural circuit and biomechanics governed both behaviors, but this hypothesis was not testable with current technology. We built a neuromechanical model to test this and to gain a better understanding of the role of the semi-lunar process (SLP) in jump dynamics. The SLP are bands of cuticle that store energy for use during jumping. The results of the model were compared to a variety of published data and were similar. The SLP significantly increased jump distance, power, total energy, and duration of the jump impulse. Locust can jump precisely to a target, but also exhibit tumbling. We proposed two mechanisms for controlling tumbling during the jump. The first was that locusts adjust the pitch of their body prior to the jump to move the center of mass closer to the thrust vector. The second was that contraction of the abdominal muscles during the jump produced torques that countered the torque due to thrust. There was a strong correlation relating increased pitch and takeoff angle. In simulations there was an optimal pitch-takeoff combination that minimized tumbling that was similar to the live data. The direction and magnitude of tumbling could be controlled by adjusting abdominal tension. Tumbling also influenced jump elevation. Neuromechanical simulation addressed problems that would be difficult to examine using traditional physiological approaches. It is a powerful tool for understanding the neural basis of behavior.

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A digital twin of the human neuromuscular system can substantially improve the prediction of injury risks and the evaluation of the readiness to return to sport. Reinforcement learning (RL) algorithms already learn physical quantities unmeasurable in biomechanics, and hence can contribute to the development of the digital twin. Our preliminary results confirm the potential of RL algorithms to estimate the muscle activations of an athlete’s moves. / Ein digitaler Zwilling des menschlichen neuromuskulären Systems kann die Vorhersage von Verletzungsrisiken und die Bewertung der Bereitschaft zur Rückkehr in den Sport erheblich verbessern. Algorithmen des bestärkenden Lernens (Reinforcement Learning, RL) lernen bereits physikalische Größen, die in der Biomechanik nicht messbar sind, und können daher zur Entwicklung des digitalen Zwillings beitragen. Unsere vorläufigen Ergebnisse bestätigen das Potenzial von RL-Algorithmen zur Schätzung der Muskelaktivierung bei den Bewegungen eines Sportlers.