Abstract
BackgroundResearch efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. However, despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had only modest clinical impact. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals. This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery.MethodsWe have developed a patient-specific computational model of the human musculoskeletal system controlled via neural surrogates, i.e., electromyography-derived neural activations to muscles. The electromyography-driven musculoskeletal model was synthesized into a human-machine interface (HMI) that enabled poststroke and incomplete spinal cord injury patients to voluntarily control multiple joints in a multifunctional robotic exoskeleton in real time.ResultsWe demonstrated patients’ control accuracy across a wide range of lower-extremity motor tasks. Remarkably, an increased level of exoskeleton assistance always resulted in a reduction in both amplitude and variability in muscle activations as well as in the mechanical moments required to perform a motor task. Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton would potentially increase human neuromuscular effort, these results demonstrate that the developed HMI precisely synchronizes the device actuation with residual voluntary muscle contraction capacity in neurologically impaired patients.ConclusionsContinuous voluntary control of robotic exoskeletons (i.e. event-free and task-independent) has never been demonstrated before in populations with paretic and spastic-like muscle activity, such as those investigated in this study. Our proposed methodology may open new avenues for harnessing residual neuromuscular function in neurologically impaired individuals via symbiotic wearable robots.
Highlights
Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals
Despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had limited performance when tested in healthy individuals [1] and have achieved only modest clinical impact in neurologically impaired patients [2], e.g., stroke [3, 4], spinal cord injury (SCI) patients [5]
Since small discrepancies in onset time between human limb movement and that of the parallel exoskeleton can significantly increase human muscle effort [1], our results demonstrate that the proposed approach can precisely synchronize device actuation with human muscle contraction, which is especially challenging in pathological populations with paretic and spastic-like muscle activity
Summary
Research efforts in neurorehabilitation technologies have been directed towards creating robotic exoskeletons to restore motor function in impaired individuals. A major limitation is the inability to enable exoskeleton voluntary control in neurologically impaired individuals This hinders the possibility of optimally inducing the activity-driven neuroplastic changes that are required for recovery. Despite advances in mechatronics and bioelectrical signal processing, current robotic exoskeletons have had limited performance when tested in healthy individuals [1] and have achieved only modest clinical impact in neurologically impaired patients [2], e.g., stroke [3, 4], SCI patients [5]. The first is the inability of current systems to enable an individual patient to voluntarily control the robotic device while inducing positive modulation of neuromuscular activity This prevents wearable robots from facilitating the activity-driven neuroplastic changes that are required for recovery [6, 7]. The second is an incomplete understanding of how lesions in the central nervous system (CNS) impact musculoskeletal system function, which impedes understanding how patients’ motor intentions should be best supported by a robotic device
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