In the last decades, people suffering from neuromuscular injuries and diseases have confronted a rapid development of technology and technological approaches to restore movement. Robotics and (neuro)prosthetics combined neuromuscular information, e.g. electromyography, controlling the mechanical devices to achieve an optimal functional behavior.
Most of the algorithms used for motor control were derived from studies on healthy individuals rather than patients with neuromuscular diseases, disorders or injuries and put in the context of neurorehabilitation. Partially successful applications required the active participation of subjects and expected supervised or unsupervised motor control learning. We, however, assume that plasticity of the central nervous system enables learning of new compensatory strategies and motion patterns to overcome the inability to perform the movement. The robotic technology enables a controlled learning curve and in combination with visual/perception tasks introduces controlled perturbations that may facilitate compensatory strategies needed to perform the functional task. This may be even more appreciated in everyday life in addition to clinical recovery.
Robots and robotic devices facilitate accurate movements and allow therapists to focus more on monitoring of movement patterns, speed, coordination, and cognitive therapy. Robots can also act as smart adaptive assistive devices enabling the participants more voluntary control and participation together with the device, e.g. cooperative human-robot. This may lead not only to physical assistance and movement replication but also to motor learning and functional recovery in stroke, TBI and spinal cord injury. Robotics devices are nowadays also combined with electromyography to achieve optimal control or with functional electrical stimulation (FES) to enable more accurate functional movements of limbs.
FES has been effective in gait restoration of the complete paraplegic person in the past, but the reverse muscle fibers recruitment and fatigue prevented a total success. However, later the electrical stimulation has been successfully applied in rehabilitation medicine, particularly in combination with robotics.
Rehabilitation robots, standalone or in combination with human-robot interfaces and/or neuro- or visual feedback are going to act as co-robots in tasks applied in physiotherapy and occupational therapy in rehabilitation medicine. So far numerous pilot and feasibility studies reported on improvements of functional movement and patterns, but less randomized control studies have indicated also clinical improvements.
The aim of this Research Topic is to deepen our current knowledge of the neurorobotic applications used in neurorehabilitation to promote further progress in the field.
We welcome articles exploring the following themes:
• assistive rehabilitation robotics
• collaborative robotics in rehabilitation medicine
• human-machine interaction
• rehabilitation robots and prostheses control
• visual interfaces for robotic tasks
• brain/nerve/muscle robot interfaces
In the last decades, people suffering from neuromuscular injuries and diseases have confronted a rapid development of technology and technological approaches to restore movement. Robotics and (neuro)prosthetics combined neuromuscular information, e.g. electromyography, controlling the mechanical devices to achieve an optimal functional behavior.
Most of the algorithms used for motor control were derived from studies on healthy individuals rather than patients with neuromuscular diseases, disorders or injuries and put in the context of neurorehabilitation. Partially successful applications required the active participation of subjects and expected supervised or unsupervised motor control learning. We, however, assume that plasticity of the central nervous system enables learning of new compensatory strategies and motion patterns to overcome the inability to perform the movement. The robotic technology enables a controlled learning curve and in combination with visual/perception tasks introduces controlled perturbations that may facilitate compensatory strategies needed to perform the functional task. This may be even more appreciated in everyday life in addition to clinical recovery.
Robots and robotic devices facilitate accurate movements and allow therapists to focus more on monitoring of movement patterns, speed, coordination, and cognitive therapy. Robots can also act as smart adaptive assistive devices enabling the participants more voluntary control and participation together with the device, e.g. cooperative human-robot. This may lead not only to physical assistance and movement replication but also to motor learning and functional recovery in stroke, TBI and spinal cord injury. Robotics devices are nowadays also combined with electromyography to achieve optimal control or with functional electrical stimulation (FES) to enable more accurate functional movements of limbs.
FES has been effective in gait restoration of the complete paraplegic person in the past, but the reverse muscle fibers recruitment and fatigue prevented a total success. However, later the electrical stimulation has been successfully applied in rehabilitation medicine, particularly in combination with robotics.
Rehabilitation robots, standalone or in combination with human-robot interfaces and/or neuro- or visual feedback are going to act as co-robots in tasks applied in physiotherapy and occupational therapy in rehabilitation medicine. So far numerous pilot and feasibility studies reported on improvements of functional movement and patterns, but less randomized control studies have indicated also clinical improvements.
The aim of this Research Topic is to deepen our current knowledge of the neurorobotic applications used in neurorehabilitation to promote further progress in the field.
We welcome articles exploring the following themes:
• assistive rehabilitation robotics
• collaborative robotics in rehabilitation medicine
• human-machine interaction
• rehabilitation robots and prostheses control
• visual interfaces for robotic tasks
• brain/nerve/muscle robot interfaces