Human-robot cooperative movement training: Learning a novel sensory motor transformation during walking with robotic assistance-as-needed

BACKGROUND: A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. METHODS: Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. RESULTS: We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. CONCLUSION: The assist-as-needed algorithm proposed here can limit error during the learning of a dynamic motor task. The algorithm encourages learning by decreasing its assistance as a function of the ongoing progression of movement error. This type of algorithm is well suited for helping people learn dynamic tasks for which large kinematic errors are dangerous or discouraging, and thus may prove useful for robot-assisted movement training of walking or reaching following neurologic injury

Training and Assessment of Laparoscopic Skills

Laparoscopic surgery is gaining popularity among the surgical community. While its prevalence expands, the need for reliable training and assessment tools is becoming increasingly important. Laparoscopic skills are not an innate behavior, nor can they be easily mimicked, and can only be acquired through hands-on training. A consensus exists among physicians that establishment and evaluation of technical skill in surgical training programs are inadequate and in need of improvement. A validated, reliable bench model that could train and assess could be standardized and provide numerous benefits including determination of which medical students should consider a career in surgery, valuable feedback to residents, a tracking mechanism of resident performance, a possible certification and recertification tool, and to allow for interinstitutional comparison. To this end, several potentially successful bench models testing dexterity, hand-eye coordination, and depth perception have been developed. A few models have been proven to be both valid and reliable indicators of technical skill. Although the future remains uncertain, enough groundwork has been laid to begin incorporating technical skill training and assessment into surgical training programs

Feasibility of Manual Teach-and-Replay and Continuous Impedance Shaping for Robotic Locomotor Training Following Spinal Cord Injury

Robotic gait training is an emerging technique for retraining walking ability following spinal cord injury (SCI). A key challenge in this training is determining an appropriate stepping trajectory and level of assistance for each patient, since patients have a wide range of sizes and impairment levels. Here, we demonstrate how a lightweight yet powerful robot can record subject-specific, trainer-induced leg trajectories during manually assisted stepping, then immediately replay those trajectories. Replay of the subject-specific trajectories reduced the effort required by the trainer during manual assistance, yet still generated similar patterns of muscle activation for six subjects with a chronic SCI. We also demonstrate how the impedance of the robot can be adjusted on a step-by-step basis with an error-based, learning law. This impedance-shaping algorithm adapted the robot's impedance so that the robot assisted only in the regions of the step trajectory where the subject consistently exhibited errors. The result was that the subjects stepped with greater variability, while still maintaining a physiologic gait pattern. These results are further steps toward tailoring robotic gait training to the needs of individual patients

Training and assessment of laparoscopic skills.

Laparoscopic surgery is gaining popularity among the surgical community. While its prevalence expands, the need for reliable training and assessment tools is becoming increasingly important. Laparoscopic skills are not an innate behavior, nor can they be easily mimicked, and can only be acquired through hands-on training. A consensus exists among physicians that establishment and evaluation of technical skill in surgical training programs are inadequate and in need of improvement. A validated, reliable bench model that could train and assess could be standardized and provide numerous benefits including determination of which medical students should consider a career in surgery, valuable feedback to residents, a tracking mechanism of resident performance, a possible certification and recertification tool, and to allow for interinstitutional comparison. To this end, several potentially successful bench models testing dexterity, hand-eye coordination, and depth perception have been developed. A few models have been proven to be both valid and reliable indicators of technical skill. Although the future remains uncertain, enough groundwork has been laid to begin incorporating technical skill training and assessment into surgical training programs

Human-robot cooperative movement training: learning a novel sensory motor transformation during walking with robotic assistance-as-needed

Background A prevailing paradigm of physical rehabilitation following neurologic injury is to "assist-as-needed" in completing desired movements. Several research groups are attempting to automate this principle with robotic movement training devices and patient cooperative algorithms that encourage voluntary participation. These attempts are currently not based on computational models of motor learning. Methods Here we assume that motor recovery from a neurologic injury can be modelled as a process of learning a novel sensory motor transformation, which allows us to study a simplified experimental protocol amenable to mathematical description. Specifically, we use a robotic force field paradigm to impose a virtual impairment on the left leg of unimpaired subjects walking on a treadmill. We then derive an "assist-as-needed" robotic training algorithm to help subjects overcome the virtual impairment and walk normally. The problem is posed as an optimization of performance error and robotic assistance. The optimal robotic movement trainer becomes an error-based controller with a forgetting factor that bounds kinematic errors while systematically reducing its assistance when those errors are small. As humans have a natural range of movement variability, we introduce an error weighting function that causes the robotic trainer to disregard this variability. Results We experimentally validated the controller with ten unimpaired subjects by demonstrating how it helped the subjects learn the novel sensory motor transformation necessary to counteract the virtual impairment, while also preventing them from experiencing large kinematic errors. The addition of the error weighting function allowed the robot assistance to fade to zero even though the subjects' movements were variable. We also show that in order to assist-as-needed, the robot must relax its assistance at a rate faster than that of the learning human. Conclusion The assist-as-needed algorithm proposed here can limit error during the learning of a dynamic motor task. The algorithm encourages learning by decreasing its assistance as a function of the ongoing progression of movement error. This type of algorithm is well suited for helping people learn dynamic tasks for which large kinematic errors are dangerous or discouraging, and thus may prove useful for robot-assisted movement training of walking or reaching following neurologic injury.Peer Reviewe