Research

Theme

Data- and theory-driven human movement science

Tool

Behavioral Experiment, Machine Learning, Computational Modeling, App Development

Research purpose

We can achieve desired movements in our daily life, e.g., we can grasp a cup on a table, professional baseball pitchers can throw a ball toward an aimed location, or pianist can play sounds like he/she images. Contrasting to those sophisticated movements, no one can repeat an identical movement because of uncertainty and redundancy inherent in our motor systems (i.e., brain, spinal cord, muscles, bones, etc.,). Uncertainty means, for instance, the noise of neural activity in motor planning and that of muscle activity in motor execution. Redundancy means that the degree of freedom (i.e., the number of joints and muscles) is more than necessary to achieve desired movements. This uncertainty and redundancy result in an intractable situation in our body movements, i.e., an infinite number of movement repertory enables to achieve an identical result. In other words, even if we can not repeat an identical movement, we can achieve desired movements by somehow managing the uncertainty and redundancy.

A fundamental question in motor neuroscience is how we tame the uncertainty and redundancy in the motor control process. Another question can arise: how we manage the uncertainty and redundancy in the motor learning process. Our research group tackles the questions based on behavioral experiment, mathematical modeling, machine learning, and application development. Our final goals are to answer the above-mentioned questions. These goals naturally lead to propose flexible control theory applicable to a complicated system, efficient rehabilitation method, and effective sport or music training methods.