About this Research Topic
Over the last decade, the developments in science and technology have greatly enhanced the scope and outcome of neurorehabilitation processes. Technologies are being used in neurorehabilitation to provide innovative solutions for patients with nervous system issues. Neurological diseases usually reduce the patient’s quality of life considerably. In such cases, physiological data and processing can be applied to improve the rehabilitation process. This field has attracted great interest in using multimodal physiological data.
A neurorehabilitation system must interface with a large number of sensors from multiple locations and must perform complex processing on the acquired data. Various algorithms are typically tested for accurate classification and prediction of neurological complications. In the actual processing of multiple physiological signals, a set of different signal processing and algorithms must be applied to the incoming data stream in real-time. Depending on the type of data, it can be computationally expensive and should be done with minimal latency. Recent advances in parallel processing hardware, such as graphical processing unit (GPU) and Field-programmable gate array (FPGA), have attracted huge attention in the development of hardware-software co-design for time-critical applications. The use of systems on chip (SoC) that combines microprocessor and programmable logic in a single chip solution has enabled the development of high mobility, power-efficient, and high-performance processing. Co-design implies simultaneous design and optimization of several aspects of the system, including hardware and software, to achieve a set target for a given system metrics, such as throughput, latency, power, size, or any combination thereof. Deep learning has been particularly amenable to such co-design processes across various parts of the software and hardware stack, leading to a variety of novel algorithms, numerical optimizations, and AI hardware.
This Research Topic aims to bring together state-of-the-art interdisciplinary research in hardware and software co-design implementation in the field of Neurorehabilitation and Neuroprosthetics. All aspects of neurological disabilities including stroke, spinal cord injury, traumatic brain injury, neuromuscular disease, and other neurological disorders, and associated neurorehabilitation systems and brain-machine interfaces studies devoted to Neuroprosthetics will be the streamline of this article collection. The main objective of this Research Topic is to report on aspects of hardware and software co-design for neurorehabilitation, leading to clinical decisions and real-time applications. We welcome research papers that explain the methods, techniques, and machine learning models for the analysis and hardware implementation of brain signals for various application like chronic electrode implant, neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain-machine interface (BMI), deep brain stimulation, neuromodulator, etc. Suggested topics can include, but are not limited to:
• Robotic rehabilitation system
• Brain-Computer Interfaces
• Robotics for Assisting Children with Physical and Cognitive
Disabilities
• Emerging Technologies for Neurorehabilitation after Stroke
• Assistive system for the workplace
• Unobtrusive Assistive Technology
• Processing in-memory hardware architectures for efficient and
scalable machine learning
• Hardware efficiency-aware neural architecture search
• Graph-based recommender systems with implications on
hardware
• End-to-end hardware/software co-design automation for deep
learning
A neurorehabilitation system must interface with a large number of sensors from multiple locations and must perform complex processing on the acquired data. Various algorithms are typically tested for accurate classification and prediction of neurological complications. In the actual processing of multiple physiological signals, a set of different signal processing and algorithms must be applied to the incoming data stream in real-time. Depending on the type of data, it can be computationally expensive and should be done with minimal latency. Recent advances in parallel processing hardware, such as graphical processing unit (GPU) and Field-programmable gate array (FPGA), have attracted huge attention in the development of hardware-software co-design for time-critical applications. The use of systems on chip (SoC) that combines microprocessor and programmable logic in a single chip solution has enabled the development of high mobility, power-efficient, and high-performance processing. Co-design implies simultaneous design and optimization of several aspects of the system, including hardware and software, to achieve a set target for a given system metrics, such as throughput, latency, power, size, or any combination thereof. Deep learning has been particularly amenable to such co-design processes across various parts of the software and hardware stack, leading to a variety of novel algorithms, numerical optimizations, and AI hardware.
This Research Topic aims to bring together state-of-the-art interdisciplinary research in hardware and software co-design implementation in the field of Neurorehabilitation and Neuroprosthetics. All aspects of neurological disabilities including stroke, spinal cord injury, traumatic brain injury, neuromuscular disease, and other neurological disorders, and associated neurorehabilitation systems and brain-machine interfaces studies devoted to Neuroprosthetics will be the streamline of this article collection. The main objective of this Research Topic is to report on aspects of hardware and software co-design for neurorehabilitation, leading to clinical decisions and real-time applications. We welcome research papers that explain the methods, techniques, and machine learning models for the analysis and hardware implementation of brain signals for various application like chronic electrode implant, neural-control interface (NCI), mind-machine interface (MMI), direct neural interface (DNI), or brain-machine interface (BMI), deep brain stimulation, neuromodulator, etc. Suggested topics can include, but are not limited to:
• Robotic rehabilitation system
• Brain-Computer Interfaces
• Robotics for Assisting Children with Physical and Cognitive
Disabilities
• Emerging Technologies for Neurorehabilitation after Stroke
• Assistive system for the workplace
• Unobtrusive Assistive Technology
• Processing in-memory hardware architectures for efficient and
scalable machine learning
• Hardware efficiency-aware neural architecture search
• Graph-based recommender systems with implications on
hardware
• End-to-end hardware/software co-design automation for deep
learning
Keywords: Neurorehabilitation, FPGA, System on Chip, Neurological Disorder, Signal Processing, Brain-Computer Interface, Embedded System
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