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Rehabilitation of human motor function is an issue of growing significance, and human-interactive robots offer promising potential to meet the need. For the lower extremity, however, robot-aided therapy has proven challenging. To inform effective approaches to robotic gait therapy, it is important to better understand unimpaired locomotor control: its sensitivity to different mechanical contexts and its response to perturbations. The present study evaluated the behavior of 14 healthy subjects who walked on a motorized treadmill and overground while wearing an exoskeletal ankle robot. Their response to a periodic series of ankle plantar flexion torque pulses, delivered at periods different from, but sufficiently close to, their preferred stride cadence, was assessed to determine whether gait entrainment occurred, how it differed across conditions, and if the adapted motor behavior persisted after perturbation. Certain aspects of locomotor control were exquisitely sensitive to walking context, while others were not. Gaits entrained more often and more rapidly during overground walking, yet, in all cases, entrained gaits synchronized the torque pulses with ankle push-off, where they provided assistance with propulsion. Furthermore, subjects entrained to perturbation periods that required an adaption toward slower cadence, even though the pulses acted to accelerate gait, indicating a neural adaptation of locomotor control. Lastly, during 15 post-perturbation strides, the entrained gait period was observed to persist more frequently during overground walking. This persistence was correlated with the number of strides walked at the entrained gait period (i.e., longer exposure), which also indicated a neural adaptation. NEW & NOTEWORTHY We show that the response of human locomotion to physical interaction differs between treadmill and overground walking. Subjects entrained to a periodic series of ankle plantar flexion torque pulses that shifted their gait cadence, synchronizing ankle push-off with the pulses (so that they assisted propulsion) even when gait cadence slowed. Entrainment was faster overground and, on removal of torque pulses, the entrained gait period persisted more prominently overground, indicating a neural adaptation of locomotor control.
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Predictive Modeling of Periodic Behavior for Human-Robot Symbiotic Walking
We propose in this paper Periodic Interaction Primitives - a probabilistic framework that can be used to learn compact models of periodic behavior. Our approach extends existing formulations of Interaction Primitives to periodic movement regimes, i.e., walking. We show that this model is particularly well-suited for learning data-driven, customized models of human walking, which can then be used for generating predictions over future states or for inferring latent, biomechanical variables. We also demonstrate how the same framework can be used to learn controllers for a robotic prosthesis using an imitation learning approach. Results in experiments with human participants indicate that Periodic Interaction Primitives efficiently generate predictions and ankle angle control signals for a robotic prosthetic ankle, with MAE of 2.21 degrees in 0.0008s per inference. Performance degrades gracefully in the presence of noise or sensor fall outs. Compared to alternatives, this algorithm functions 20 times faster and performed 4.5 times more accurately on test subjects.
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- Award ID(s):
- 1749783
- PAR ID:
- 10168883
- Date Published:
- Journal Name:
- 2020 IEEE International Conference on Robotics and Automation
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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