Review of control strategies for robotic movement training after neurologic injury

• Published in: Journal of neuroengineering and rehabilitation Vol. 6; no. 1; p. 20
• Main Authors: Marchal-Crespo, Laura, Reinkensmeyer, David J
• Format: Journal Article
• Language: English
• Published: London BioMed Central 16.06.2009; BioMed Central Ltd; BMC
• Subjects: Algorithms; Biomedical and Life Sciences; Biomedical Engineering and Bioengineering; Biomedicine; Care and treatment; Electromyography - methods; Health aspects; Humans; Medicine, Physical; Methods; Musculoskeletal Manipulations - methods; Neurology; Neurosciences; Patient outcomes; Rehabilitation Medicine; Review; Robotic Lower Limb Exoskeletons; Robotics; Robotics - methods; Spinal cord injuries; Trauma, Nervous System - rehabilitation; United States; Virtual Channel; Chronic Stroke; Robotic Device; Adaptive Controller; Motor Learning
• ISSN: 1743-0003; 1743-0003
• DOI: 10.1186/1743-0003-6-20

There is increasing interest in using robotic devices to assist in movement training following neurologic injuries such as stroke and spinal cord injury. This paper reviews control strategies for robotic therapy devices. Several categories of strategies have been proposed, including, assistive, challenge-based, haptic simulation, and coaching. The greatest amount of work has been done on developing assistive strategies, and thus the majority of this review summarizes techniques for implementing assistive strategies, including impedance-, counterbalance-, and EMG- based controllers, as well as adaptive controllers that modify control parameters based on ongoing participant performance. Clinical evidence regarding the relative effectiveness of different types of robotic therapy controllers is limited, but there is initial evidence that some control strategies are more effective than others. It is also now apparent there may be mechanisms by which some robotic control approaches might actually decrease the recovery possible with comparable, non-robotic forms of training. In future research, there is a need for head-to-head comparison of control algorithms in randomized, controlled clinical trials, and for improved models of human motor recovery to provide a more rational framework for designing robotic therapy control strategies.