Optimizing Time Distribution of Training Sessions for Robotic Exoskeletons Use in Individuals with Spinal Cord Injury
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Regaining the ability to walk independently in the home and the community has been identified as a high priority objective for individuals with paraplegia resulting from a spinal cord injury (SCI). Until recently, the main source of mobility for individuals with paraplegic has been the use of a wheelchair or walking minimally with long-leg braces. However, wheelchair use is not physically or emotionally equivalent to walking, and is often thought to limit community participation and thus to exacerbate social isolation, while long-leg braces impose extreme demands metabolically and on the musculoskeletal system, resulting in ability to walk very short distances before experiencing extreme fatigue and fall risk. Recently, a new generation of robotic exoskeletons provides the opportunity to individuals with paraplegia to stand up and walk, thus enabling them to potentially reintegrate into society. Although several different exoskeletons are currently available in the market and over a 150 already been sold worldwide, product commercialization has preceded clinical research; little or no information is available on training strategies to enable the safe use of these devices for supporting mobility in individuals with SCI.
A few studies have been published on exoskeleton use, however participant expertise is extremely variable, and each clinical site chooses to space its training sessions differently resulting in inconsistent training parameters and protocols. Hence, objective evaluation of the optimal timing distribution and training strategies for these exoskeletons to advance the expertise in exoskeleton use for independent walking devices is thus a priority.
Accordingly, the long-term goal of our research is to facilitate independent walking at home and in the community for individuals with paraplegia using robotic exoskeletons. To achieve this goal, our objective is to test a range of timing distribution models of exoskeleton training to enable us to develop optimal training strategies to help individuals ambulate independently with these devices. Our central hypothesis is that quantitative mathematical models of therapy timing distribution built on mapping the time course of learning decay after single and multiple sessions of therapy will help plan optimal training strategies with limited training sessions.