Abstract

Recent advances in the field of neural rehabilitation, facilitated through technological innovation and improved neurophysiological knowledge of impaired motor control, have opened up new research directions. Such advances increase the relevance of existing interventions, as well as allow novel methodologies and technological synergies. New approaches attempt to partially overcome long-term disability caused by spinal cord injury, using either invasive bridging technologies or noninvasive human–machine interfaces. Muscular dystrophies benefit from electromyography and novel sensors that shed light on underlying neuromotor mechanisms in people with Duchenne. Novel wearable robotics devices are being tailored to specific patient populations, such as traumatic brain injury, stroke, and amputated individuals. In addition, developments in robot-assisted rehabilitation may enhance motor learning and generate movement repetitions by decoding the brain activity of patients during therapy. This is further facilitated by artificial intelligence algorithms coupled with faster electronics. The practical impact of integrating such technologies with neural rehabilitation treatment can be substantial. They can potentially empower nontechnically trained individuals—namely, family members and professional carers—to alter the programming of neural rehabilitation robotic setups, to actively get involved and intervene promptly at the point of care. This narrative review considers existing and emerging neural rehabilitation technologies through the perspective of replacing or restoring functions, enhancing, or improving natural neural output, as well as promoting or recruiting dormant neuroplasticity. Upon conclusion, we discuss the future directions for neural rehabilitation research, diagnosis, and treatment based on the discussed technologies and their major roadblocks. This future may eventually become possible through technological evolution and convergence of mutually beneficial technologies to create hybrid solutions.

Highlights

  • Neural plasticity is based on the hypothesis that central nervous system (CNS) and peripheral nervous system (PNS) circuits can be retrained after a lesion in order to facilitate effective rehabilitation [3]

  • The need to transmit sensory feedback from the prosthesis [187] led to the development of the Modular Prosthetic Limb (MPL) with 26 articulated and 17 controllable degrees of freedom (DOF) with bidirectional capability and the DEKA arm, which provides powered movement complemented by surgical procedures

  • In this article we have reviewed key robotic technologies that facilitate neural rehabilitation and discussed roadblocks in their development and application, as well as presented current and emerging directions in the field

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Summary

Introduction

2, we discuss the application of robotic neurorehabilitation to a number of indicative impairments These key conditions limit human motor control and present researchers with a broad range of open-ended problems capable of illustrating the aims and current clinical challenges for robotic neurorehabilitation. Given these facts and trends, it is time to discuss how such recent advances will lead to a convergence among technological and medical insights This discussion aims to create new research avenues for targeted robotic neural rehabilitation related to a multitude of impairments (see Section 4). Our aim is to highlight recent advancements in robotic rehabilitation technology and insights on impaired motor control and to offer an integrative view on how such new knowledge from diverse fields can be combined to benefit robotic neurorehabilitation. We are considering a new generation of robotic rehabilitation technologies, which will be implemented on a larger scale and result in better, faster, and less expensive clinical and functional rehabilitation outcomes

Stroke
Traumatic Brain Injury
Spinal Cord Injury
Amputation
Mental Disorders
Human–Robot Control Interfaces
Digital–Neural Interfaces
Electromyography
Brain–Computer Interfaces
Exoskeletons
Neuroprosthetics
Virtual and Augmented Reality
AI Algorithms for Human–Robot Interaction
AI Algorithms for Neural Signal Processing
Conclusions and Future Directions
Section 3.1.1
Section 3.1
Section 3.2.1
Section 3.4.1
Findings
Limitations
Full Text
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