Stroke is one of the leading causes of long-term disability today; therefore, many research efforts are focused on designing maximally effective and efficient treatment methods. In particular, robotic stroke rehabilitation has received significant attention for upper-limb therapy due to its ability to provide high-intensity repetitive movement therapy with less effort than would be required for traditional methods. Recent research has focused on increasing patient engagement in therapy, which has been shown to be important for inducing neural plasticity to facilitate recovery. Robotic therapy devices enable unique methods for promoting patient engagement by providing assistance only as needed and by detecting patient movement intent to drive to the device. Use of these methods has demonstrated improvements in functional outcomes, but careful comparisons between methods remain to be done. Future work should include controlled clinical trials and comparisons of effectiveness of different methods for patients with different abilities and needs in order to inform future development of patient-specific therapeutic protocols.

No abstract

Eighty percent of medical robotics papers have been published in the past decade—What has been accomplished?

This paper presents a shared-control interaction paradigm for haptic interface systems, with experimental data from two user studies. Shared control, evolved from its initial telerobotics applications, is adapted as a form of haptic assistance in that the haptic device contributes to execution of a dynamic manual target-hitting task via force commands from an automatic controller. Compared to haptic virtual environments, which merely display the physics of the virtual system, or to passive methods of haptic assistance for performance enhancement based on virtual fixtures, the shared-control approach offers a method for actively demonstrating desired motions during virtual environment interactions. The paper presents a thorough review of the literature related to haptic assistance. In addition, two experiments were conducted to independently verify the efficacy of the shared-control approach for performance enhancement and improved training effectiveness of the task. In the first experiment, shared control is found to be as effective as virtual fixtures for performance enhancement, with both methods resulting in significantly better performance in terms of time between target hits for the manual target-hitting task than sessions where subjects feel only the forces arising from the mass-spring-damper system dynamics. Since shared control is more general than virtual fixtures, this approach may be extremely beneficial for performance enhancement in virtual environments. In terms of training enhancement, shared control and virtual fixtures were no better than practice in an unassisted mode. For manual control tasks, such as the one described in this paper, shared control is beneficial for performance enhancement, but may not be viable for enhancing training effectiveness.