Over the past decade, there has been a rapid increase in applications of multibody system
dynamics to the predictive simulation of human movement. Using predictive “what-if” human …

In recent years, numerous studies have been conducted to analyze how humans
subconsciously optimize various performance criteria while performing a particular task …

Learning effective continuous control policies in high-dimensional systems, including
musculoskeletal agents, remains a significant challenge. Over the course of biological …

The ultimate goal of most neuromusculoskeletal modeling research is to improve the
treatment of movement impairments. However, even though neuromusculoskeletal models …

Muscle-actuated organisms are capable of learning an unparalleled diversity of dexterous
movements despite their vast amount of muscles. Reinforcement learning (RL) on large …

To elucidate the underlying physiological mechanisms of muscle synergies, we investigated
long-range functional connectivity by cortico-muscular (CMC), intermuscular (IMC) and …

How do muscle synergies change as motor skills are learned? The purpose of this study
was to investigate the relationship between synergy number and skill acquisition, and to …

Conventional approaches to enhance movement coordination, such as providing
instructions and visual feedback, are often inadequate in complex motor tasks with multiple …

Humans can rapidly adapt to new situations, even though they have redundant degrees of
freedom (df). Previous studies in neuroscience revealed that human movements could be …

Software packages that use optimization to predict the motion of dynamic systems are
powerful tools for studying human movement. These “predictive simulations” are gaining …