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Enhancing Motor Learning through Virtual Reality, Robotics

Although the terms motor skill and coordination bring to mind feats of elite athletes or musicians, there is a tremendous deal of skill and coordination required even to perform the most mundane tasks like picking up a cup of coffee. The exquisite coordination required to achieve such movements comes to the fore only when it is disrupted, as is often the case of movement disorders or neurological injury.

For example, in the United State there are about 800,000 individuals who suffer a stroke every year and a substantial portion of these individuals suffer chronic deficits in motor function, which in turn significantly affects their ability to independently perform activities of daily living. There is an urgent need to understand the theoretical mechanisms underlying the coordination of these multi-joint movement patterns, and finding new ways to retrain these movement patterns.

In view of this important problem, the goal of the research in the lab of Rajiv Ranganathan, assistant professor in the
Department of Kinesiology and the Department of Mechanical Engineering at Michigan State University, is two-fold:

  1. to develop a theoretical understanding of how the nervous system learns to produce coordinated movement, and how this is altered in neurological conditions like stroke, and
  2. to integrate theoretical understanding with technology (like virtual reality and robotics) to facilitate the development of novel rehabilitation strategies.

Virtual environments to study motor learning

A key challenge to studying motor learning and coordination in the real-world (for example, learning a tennis serve), is that it often takes too long to learn and there are too many confounding variables in the environment which have an influence on how the movements are performed.

Ranganathan’s approach to addressing this challenge is to use virtual environments where he and his team can both precisely control the environment and also quantify how exactly movements change with training using tools such as 3D motion analysis.

For example, participants in our experiments learn to play “virtual games” – e.g., a shuffleboard task, where they have to learn to slide a virtual puck as close as possible towards a target. However, because the tasks are virtual, we as experimenters can control which movement patterns will result in good performance on the task.

As we alter these parameters during the experiment, we can see participants go through the process of reorganizing and reshaping their coordination patterns to get better at the task. This allows us to study a process in the matter of a few hours in a perfectly controlled environment that would probably take months or years in the real world.

Virtual reality and robotics to enhance motor learning

In addition to measuring these changes with practice, Ranganathan also uses tools such as virtual reality and robotics to alter the visual and physical environments during these experiments.

For example, imagine a person has had a stroke and is having difficulty making a smooth reaching movement towards a target. Ranganathan and his team use virtual reality to alter how the patient perceives the movement of his arm (e.g., make it look smoother than the actual movement), or use a robot to actually apply forces to his arm and assist the movement.

Given that many of these tools are becoming more powerful and ubiquitous, understanding how these tools should be employed to enhance motor learning is a critical step in developing novel rehabilitation strategies.

In order to acquire data, a lot of experiments had to be run and the data collected from these experiments were very large. Ranganathan depended on Michigan State University’s High Performance Computing Center in the Institute for Cyber-Enabled Research (iCER) to provide him with a substantial amount of storage space that can easily be linked to other projects and other faculty.

 – Rajiv Ranganathan via the Institute for Cyber-Enabled Research website

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