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Rehabilitation science has evolved significantly with the integration of technology-supported interventions, offering objective assessments, personalized programs, and real-time feedback for patients. Despite these advances, challenges remain in fully addressing the complexities of human recovery through the rehabilitation process. Over the last few years, there has been a growing interest in the application of biomimetics to inspire technological innovation. This review explores the application of biomimetic principles in rehabilitation technologies, focusing on the use of animal models to help the design of assistive devices such as robotic exoskeletons, prosthetics, and wearable sensors. Animal locomotion studies have, for example, inspired energy-efficient exoskeletons that mimic natural gait, while insights from neural plasticity research in species like zebrafish and axolotls are advancing regenerative medicine and rehabilitation techniques. Sensory systems in animals, such as the lateral line in fish, have also led to the development of wearable sensors that provide real-time feedback for motor learning. By integrating biomimetic approaches, rehabilitation technologies can better adapt to patient needs, ultimately improving functional outcomes. As the field advances, challenges related to translating animal research to human applications, ethical considerations, and technical barriers must be addressed to unlock the full potential of biomimetic rehabilitation.
Rehabilitation science has evolved significantly with the integration of technology-supported interventions, offering objective assessments, personalized programs, and real-time feedback for patients. Despite these advances, challenges remain in fully addressing the complexities of human recovery through the rehabilitation process. Over the last few years, there has been a growing interest in the application of biomimetics to inspire technological innovation. This review explores the application of biomimetic principles in rehabilitation technologies, focusing on the use of animal models to help the design of assistive devices such as robotic exoskeletons, prosthetics, and wearable sensors. Animal locomotion studies have, for example, inspired energy-efficient exoskeletons that mimic natural gait, while insights from neural plasticity research in species like zebrafish and axolotls are advancing regenerative medicine and rehabilitation techniques. Sensory systems in animals, such as the lateral line in fish, have also led to the development of wearable sensors that provide real-time feedback for motor learning. By integrating biomimetic approaches, rehabilitation technologies can better adapt to patient needs, ultimately improving functional outcomes. As the field advances, challenges related to translating animal research to human applications, ethical considerations, and technical barriers must be addressed to unlock the full potential of biomimetic rehabilitation.
Neuromorphic computing draws motivation from the human brain and presents a distinctive substitute for the traditional von Neumann architecture. Neuromorphic systems provide simultaneous data analysis, energy efficiency, and error resistance by simulating neural networks. They promote innovations in eHealth, science, education, transportation, smart city planning, and the metaverse, spurred on by deep learning and artificial intelligence. However, performance-focused thinking frequently ignores sustainability, emphasizing the need for harmony. Three primary domains comprise neuromorphic research: neuromorphic computing, which investigates biologically inspired data processing and alternative algorithms; neuromorphic devices, which utilize electronic and photonic advancements to fabricate novel nano-devices; and neuromorphic engineering, which replicates brain mechanisms using CMOS and post-CMOS technological advances. This chapter will discuss the current state of computing, the neuromorphic computing approach, established and upcoming technologies, material challenges, breakthrough computing concepts, and the advanced stage of emerging technologies. Along with software and algorithmic advancements in spike neural networks (SNNs) and neuromorphic learning algorithms, it will cover hardware improvements, such as memristors, synaptic devices, and neuromorphic processors. We will investigate applications in robotics, autonomous systems, edge computing, the Internet of Things (IoT), and sensory systems. In conclusion, the chapter will discuss future challenges and possibilities, emphasizing major findings and new research directions.
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