Abstract-Robotics and related technologies have begun to realize their promise to improve the delivery of rehabilitation therapy. However, the mechanism by which they enhance recovery remains unclear. Ultimately, recovery depends on biology, yet the details of the recovery process remain largely unknown; a deeper understanding is important to accelerate refinements of robotic therapy or suggest new approaches. Fortunately, robots provide an excellent instrument platform from which to study recovery at the behavioral level. This article reviews some initial insights about the process of upper-limb behavioral recovery that have emerged from our work. Evidence to date suggests that the form of therapy may be more important than its intensity: muscle strengthening offers no advantage over movement training. Passive movement is insufficient; active participation is required. Progressive training based on measures of movement coordination yields substantially improved outcomes. Together these results indicate that movement coordination rather than muscle activation may be the most appropriate focus for robotic therapy.

Thirty patients with chronic stroke received 6 weeks of sensorimotor robotic training in a pilot study that targeted motor function of the affected shoulder and elbow. The impairment and disability scores were stable during a 2-month observation/measurement period, improved significantly by program completion, and remained robust in the 3-month follow-up. Task-specific motor training attenuated a chronic neurologic deficit well beyond the expected period for improvement after stroke.

A system for electromyographic (EMG) triggering of robot-assisted therapy (dubbed the EMG game) for stroke patients is presented. The onset of a patient's attempt to move is detected by monitoring EMG in selected muscles, whereupon the robot assists her or him to perform point-to-point movements in a horizontal plane. Besides delivering customized robot-assisted therapy, the system can record signals that may be useful to better understand the process of recovery from stroke. Preliminary experiments aimed at testing the proposed system and gaining insight into the potential of EMG-triggered, robot-assisted therapy are reported.

This paper presents a stochastic method to estimate the multijoint mechanical impedance of the human arm suitable for use in a clinical setting, e.g., with persons with stroke undergoing robotic rehabilitation for a paralyzed arm. In this context, special circumstances such as hypertonicity and tissue atrophy due to disuse of the hemiplegic limb must be considered. A low-impedance robot was used to bring the upper limb of a stroke patient to a test location, generate force perturbations, and measure the resulting motion. Methods were developed to compensate for input signal coupling at low frequencies apparently due to human-machine interaction dynamics. Data was analyzed by spectral procedures that make no assumption about model structure. The method was validated by measuring simple mechanical hardware and results from a patient's hemiplegic arm are presented.

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