Jeremy L. Emken

Robot-enhanced motor learning: accelerating internal model formation during locomotion by transient dynamic amplification

When adapting to novel dynamic environments the nervous system learns to anticipate the imposed forces by forming an internal model of the environmental dynamics in a process driven by movement error reduction. Here, we tested the hypothesis that motor learning could be accelerated by transiently amplifying the environmental dynamics. A novel dynamic environment was created during treadmill stepping by applying a perpendicular viscous force field to the leg through a robotic device. The environmental dynamics were amplified by an amount determined by a computational learning model fit on a per-subject basis. On average, subjects significantly reduced the time required to predict the applied force field by approximately 26% when the field was transiently amplified. However, this reduction was not as great as that predicted by the model, likely due to nonstationarities in the learning parameters. We conclude that motor learning of a novel dynamic environment can be accelerated by exploiting the error-based learning mechanism of internal model formation, but that nonlinearities in adaptive response may limit the feasible acceleration. These results support an approach to movement training devices that amplify rather than reduce movement errors, and provide a computational framework for both implementing the approach and understanding its limitations.

Tools for understanding and optimizing robotic gait training

This article reviews several tools we have developed to improve the understanding of locomotor training following spinal cord injury (SCI), with a view toward implementing locomotor training with robotic devices. We have developed (1) a small-scale robotic device that allows testing of locomotor training techniques in rodent models, (2) an instrumentation system that measures the forces and motions used by experienced human therapists as they manually assist leg movement during locomotor training, (3) a powerful, lightweight leg robot that allows investigation of motor adaptation during stepping in response to force-field perturbations, and (4) computational models for locomotor training. Results from the initial use of these tools suggest that an optimal gait-training robot will minimize disruptive sensory input, facilitate appropriate sensory input and gait mechanics, and intelligently grade and time its assistance. Currently, we are developing a pneumatic robot designed to meet these specifications as it assists leg and pelvic motion of people with SCI.

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 robots 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.

A robotic device for manipulating human stepping

This paper provides a detailed design description and technical testing results of a device for studying motor learning and rehabilitation of human locomotion. The device makes use of linear motors and a parallel mechanism to achieve a wide dynamic force bandwidth as it interacts with the leg during treadmill stepping.

Second generation robotic systems for studying rodent locomotion following spinal cord injury

This paper describes the development of two generations of robotic systems to assist in quantifying and training treadmill locomotion in spinal-injured rodents. Design principles identified with the first generation systems and their incorporation into second-generation systems are described.

Accelerating motor adaptation by influencing neural computations

When people learn to reach or step in a novel dynamic environment, they initially exhibit a large trajectory error, which they gradually reduce with practice. The error evolution is well modeled by a process in which the motor command on the next movement is adjusted in proportion to the previous movements trajectory error. We hypothesized that we could accelerate motor adaptation by transiently increasing trajectory error. We tested this hypothesis by quantifying adaptation to a viscous force field applied during the swing phase of stepping in two conditions. In the first condition, we applied then removed the field for 75 steps each, for four iterations. Subjects adapted to each field exposure with a mean time constant of 3.4 steps. In the second condition, we repeated this experiment, but increased the strength of the field for only the first step in each field exposure. We predicted the field strength increase needed by solving a finite difference equation that described the error evolution. Adaptation was significantly faster when the field was transiently amplified (mean time constant = 2 trials). These results demonstrate that it is possible to increase the rate of adaptation to a novel dynamic environment based on knowledge of the computational mechanisms that underlie adaptation.

Evidence for an internal model dedicated to locomotor control

We investigated the formation and transfer of internal models during locomotion and a visual motor pointing task performed with the leg using a novel backdrivable robot. Subjects adapted to a force field applied by a robot that pushed their leg upward during walking, and then immediately pointed with their lower shank to a small target in the same force field or with the field removed. This paradigm was designed to test whether the internal model formed during walking is available during pointing and vice versa. Subjects exhibited direct effects when pointing in the force field following walking in the same field suggesting that separate models are used in each task. The same was true for the reverse transition. This separate model paradigm was further supported by the presence of aftereffects when pointing with the field removed following a period of walking with the field removed. Evidence of aftereffects when the field was removed for both walking and pointing following adaptation to force fields in the other task indicates, however a partial sharing of internal models. The results provide evidence that the internal model formed during locomotion is largely dedicated to locomotion.

Robotic Enhancement of Human Motor Learning Based on Computational Modeling of Neural Adaptation

Robotic devices could potentially retrain movement following neurologic injuries such as stroke and spinal cord injury, or train surgeons or athletes to make skillful movements. However, the optimal forms of robot assistance for enhancing human motor learning remain unknown. Here we present a model of motor learning in which the nervous system learns to move by adjusting motor commands in proportion to trajectory errors. We then provide experimental evidence that motor adaptation can be accelerated by transiently increasing trajectory errors, based on identification of such a motor learning model. We also demonstrate how a robotic training algorithm that mimics the adaptive features of human motor learning could theoretically improve movement recovery following a neurologic injury. Such a robotic training algorithm can limit movement errors while allowing the nervous system to learn an internal model of its altered dynamic environment.Copyright