Ashley N Dalrymple1, David A Roszko, Richard S Sutton, Vivian K Mushahwar. 1. Neuroscience and Mental Health Institute, University of Alberta, Edmonton, AB, Canada. Sensory Motor Adaptive Rehabilitation Technology (SMART) Network, University of Alberta, Edmonton, AB, Canada.
Abstract
OBJECTIVE: Neuromodulation technologies are increasingly used for improving function after neural injury. To achieve a symbiotic relationship between device and user, the device must augment remaining function, and independently adapt to day-to-day changes in function. The goal of this study was to develop predictive control strategies to produce over-ground walking in a model of hemisection spinal cord injury (SCI) using intraspinal microstimulation (ISMS). APPROACH: Eight cats were anaesthetized and placed in a sling over a walkway. The residual function of a hemisection SCI was mimicked by manually moving one hind-limb through the walking cycle. ISMS targeted motor networks in the lumbosacral enlargement to activate muscles in the other, presumably 'paralyzed' limb, using low levels of current (<130 μA). Four people took turns to move the 'intact' limb, generating four different walking styles. Two control strategies, which used ground reaction force and angular velocity information about the manually moved 'intact' limb to control the timing of the transitions of the 'paralyzed' limb through the step cycle, were compared. The first strategy used thresholds on the raw sensor values to initiate transitions. The second strategy used reinforcement learning and Pavlovian control to learn predictions about the sensor values. Thresholds on the predictions were then used to initiate transitions. MAIN RESULTS: Both control strategies were able to produce alternating, over-ground walking. Transitions based on raw sensor values required manual tuning of thresholds for each person to produce walking, whereas Pavlovian control did not. Learning occurred quickly during walking: predictions of the sensor signals were learned rapidly, initiating correct transitions after ≤4 steps. Pavlovian control was resilient to different walking styles and different cats, and recovered from induced mistakes during walking. SIGNIFICANCE: This work demonstrates, for the first time, that Pavlovian control can augment remaining function and facilitate personalized walking with minimal tuning requirements.
OBJECTIVE: Neuromodulation technologies are increasingly used for improving function after neural injury. To achieve a symbiotic relationship between device and user, the device must augment remaining function, and independently adapt to day-to-day changes in function. The goal of this study was to develop predictive control strategies to produce over-ground walking in a model of hemisection spinal cord injury (SCI) using intraspinal microstimulation (ISMS). APPROACH: Eight cats were anaesthetized and placed in a sling over a walkway. The residual function of a hemisection SCI was mimicked by manually moving one hind-limb through the walking cycle. ISMS targeted motor networks in the lumbosacral enlargement to activate muscles in the other, presumably 'paralyzed' limb, using low levels of current (<130 μA). Four people took turns to move the 'intact' limb, generating four different walking styles. Two control strategies, which used ground reaction force and angular velocity information about the manually moved 'intact' limb to control the timing of the transitions of the 'paralyzed' limb through the step cycle, were compared. The first strategy used thresholds on the raw sensor values to initiate transitions. The second strategy used reinforcement learning and Pavlovian control to learn predictions about the sensor values. Thresholds on the predictions were then used to initiate transitions. MAIN RESULTS: Both control strategies were able to produce alternating, over-ground walking. Transitions based on raw sensor values required manual tuning of thresholds for each person to produce walking, whereas Pavlovian control did not. Learning occurred quickly during walking: predictions of the sensor signals were learned rapidly, initiating correct transitions after ≤4 steps. Pavlovian control was resilient to different walking styles and different cats, and recovered from induced mistakes during walking. SIGNIFICANCE: This work demonstrates, for the first time, that Pavlovian control can augment remaining function and facilitate personalized walking with minimal tuning requirements.
Authors: Matthew K Hogan; Sean M Barber; Zhoulyu Rao; Bethany R Kondiles; Meng Huang; William J Steele; Cunjiang Yu; Philip J Horner Journal: Sci Rep Date: 2021-07-21 Impact factor: 4.379