![]() However, an analysis of electromyographic (EMG) data shows that the linear combination of a small number of basic waveforms reproduces a large portion of the EMG patterns (Fig. The complex and redundant nature of the musculoskeletal system and various differences in the motor outcomes lead us to imagine that the motor control in the CNS is extremely complicated and that different gaits require different control strategies.Ī large number of muscles contribute to generation of human movement, and they show complex activation patterns. Although it is obvious that such redundancy plays an important role for adaptive locomotor behavior, it remains unclear how the CNS manipulates such a large number of DOF. This means that humans use numerous and redundant DOF for different gaits and speeds. Furthermore, the muscles have more DOF than the joints due to antagonistic pairs of muscles and multiarticular muscles. However, the body has more degrees of freedom (DOF) in the joints than is necessary for this propulsion. Locomotor behavior is generated by propelling the body over the ground using the legs. ![]() Such differences are motor outcomes of the complicated musculoskeletal system controlled by the central nervous system (CNS). At the kinetics level, the vertical ground reaction force shows a two-peaked shape for walking and a single-peaked shape for running 3, 4, 5. Many kinematic parameters, such as stride length and gait cycle, also change at different gaits and speeds 2. Also, the center of mass (COM) moves differently at different gaits–at the mid-stance phase, it reaches its highest position during walking and its lowest position during running 1. For example, the most notable differences are in the existence of a double-stance phase in walking, in which both feet are in contact with the ground, and a flight phase in running, in which both feet are in the air. These gaits have different characteristics at the kinematic level. Humans walk and run in accordance with the desired speed and circumstances. These findings will improve our understanding of human motor control in locomotion and provide guiding principles for the control design of wearable exoskeletons and prostheses. ![]() Furthermore, we show that the model can walk and run at different speeds by changing only the same seven parameters based on the desired speed. ![]() Specifically, we show that it produces both walking and running of a human musculoskeletal model by changing only seven key motor control parameters. Here, we provide such a demonstration by using a motor control model with 69 parameters developed based on the muscle synergy hypothesis. Demonstrating that this control scheme can generate walking and running and change the speed is critical, as bipedal locomotion is dynamically challenging. This control scheme is simple and thought to be shared between walking and running at different speeds. It has been previously proposed that muscle activations may be generated by linearly combining a small set of basic pulses produced by central pattern generators (muscle synergy hypothesis). The complex and redundant nature of the musculoskeletal system and the wide variation in locomotion characteristics lead us to imagine that the motor control strategies for these gaits, which remain unclear, are extremely complex and differ from one another. These gaits exhibit different locomotor behaviors, such as a double-stance phase in walking and flight phase in running. Humans walk and run, as well as change their gait speed, through the control of their complicated and redundant musculoskeletal system. ![]()
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