Robotic therapy refers now to a diverse set of technologies and algorithms that can match or improve the clinical benefits achievable with conventional rehabilitation therapies after stroke and other neurologic injuries. However, the principles by which robotic therapy devices can be optimized are still not well understood. Here, I will first briefly overview the evolution of the technology and science of robot-assisted rehabilitation, including the range of control algorithms used. Then, I will describe recent experimental evidence that suggests three neuro-computational mechanisms that determine the effectiveness of robotic therapy: human slacking, Hebbian learning via proprioceptive stimulation, and mechanical modulation of reward. I will conclude by describing recent attempts to enhance the effectiveness of robotic therapy by combining it with neuro-regeneration, and by making it more accessible via “consumer stroke technology”.
David Reinkensmeyer is Professor in the Departments of Mechanical and Aerospace Engineering, Anatomy and Neurobiology, Biomedical Engineering, and Physical Medicine and Rehabilitation at the University of California at Irvine. He is co-director of the NIDILRR COMET Robotic Rehabilitation Engineering Center, co-director of the NIH K12 Engineering Career Development Center in Movement and Rehabilitation Sciences, and Editor-in-Chief of the Journal of Neuroengineering and Rehabilitation. He recently received the Innovator of the Year Award from the Henry Samueli School of Engineering and the Distinguished Midcareer Faculty Research Award from UC Irvine.