Loss of arm and hand control is one of the most invalidating consequences of high-level injuries to the spinal cord. Limb paralysis and sensory deprivation, over time, lead to progressive and permanent structural and functional changes in the Central Nervous System that can hinder recovery of upper limb function. One way to counteract this phenomenon is to engage muscles in skilled activities and skill learning in order to promote use-dependent plasticity. However, the effectiveness and specificity of these techniques are still limited by our poor understanding of the neural mechanisms underlying the recovery of function.
The REBoT project aims at harnessing neuroplasticity by means of therapeutic interventions to promote functional recovery of the upper limb after cervical spinal cord injury (SCI). The ambitious, overarching goal is to promote a “rewiring” of the nervous system to bypass pathways interrupted by SCI through a progressive adaptation of the rehabilitative intervention.
The leading hypothesis is that engaging the upper body in the skilled control of personalized physical or virtual interfaces brings about plastic changes in the sensorimotor pathways that can be exploited to design more effective and tailored assistive devices and neuro-prostheses. These, in turn, will contribute increasing the independence and therefore the quality of life after SCI.
The work in REBoT capitalizes on the concept of personalized body-machine interfaces as tools to leverage residual arms and shoulders mobility with the dual objective of (i) strengthening residual voluntary control of the upper limb, and (ii) promoting specific functional and structural changes in the nervous system.
Results from our studies indicated that wearable personalized interfaces allow delivering a customized intervention that supports both assistive and rehabilitative goals through the integration of multimodal signals from the user’s body. Our preliminary investigations suggest that control through wearable interfaces is not limited to low dimensional systems but can easily extend to complex, multi-articulated machines. We also demonstrated that interface co-adaptation can be a tool to increase user-interface interaction efficiency and to intuitively tailor the interface to their users’ unique abilities and needs. Moreover, results suggest that human-machine integration has the potential to drive neural adaptation and advocates for more studies to help characterizing the time-course and functional relevance of the changes induced by prolonged interface use.
Overall, the results of the project call for a multidisciplinary evaluation of technologies applied to individuals with disabilities that encompasses the specific user’s impairment and the behavioral and neural factors supporting its operation.
