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Soft Robotics for Parkinson’s Disease Supported by Functional Materials and Artificial Intelligence
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作者 Hirak Mazumdar Kamil Reza Khondakar +1 位作者 Suparna Das Ajeet Kaushik 《Biomedical Engineering Frontiers》 2025年第1期241-263,共23页
Progressive neurodegenerative disease known as Parkinson’s disease(PD)is characterized by both motor and nonmotor symptoms that severely reduce the quality of life.Recent developments in soft robotics provide customi... Progressive neurodegenerative disease known as Parkinson’s disease(PD)is characterized by both motor and nonmotor symptoms that severely reduce the quality of life.Recent developments in soft robotics provide customizable,cozy,and less intrusive assistive devices,which provide promising answers to these problems.To develop an enhanced support system for people with PD,this article explores the potential of next-generation soft robotics,specifically hydrogel materials,integrated with artificial intelligence(AI)and augmented reality(AR)to provide an innovative solution for PD management.The integration of an AI copilot allows for remote monitoring and real-time adjustments,ensuring optimal performance and personalized care.The use of AR enhances human–computer interactions,offering an intuitive and immersive experience for both patients and healthcare providers.By leveraging these advanced technologies,our approach aims to substantially improve motor function,reduce symptoms,and enhance the overall quality of life for PD patients.This review outlines the key components,benefits,and potential impact of this novel approach,highlighting the transformative potential of combining wearable robotics,AI,and AR in the treatment of PD.The potential for creating novel healthcare solutions by combining soft robotics,functional materials,the Internet-of-Things(IoT),and machine learning(ML)is highlighted by this multidisciplinary approach. 展开更多
关键词 hydrogel materialsintegrated progressive neurodegenerative disease assistive deviceswhich artificial intelligence ai soft robotics soft roboticsspecifically parkinson s disease pd hydrogel materials
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A facile photonics reconfigurable memristor with dynamically allocated neurons and synapses functions
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作者 Zhenyu Zhou Lulu Wang +10 位作者 Gongjie Liu Yuchen Li Zhiyuan Guan Zixuan Zhang Pengfei Li Yifei Pei Jianhui Zhao Jiameng Sun Yahong Wang Yiduo Shao Xiaobing Yan 《Light: Science & Applications》 2025年第10期2904-2914,共11页
The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to th... The dynamic neural network function realized by reconfigurable memristors to implement artificial neurons and synapses is an effective method to complete the next generation of neuromorphic computing.However,due to the limitation of reconfiguration conditions,there are inconsistencies in the turn-on voltage and operating current before and after the reconfiguration of neuromorphic devices,which leads to huge difficulties in hardware application development and is an urgent problem to be solved.In this work,we introduced light as a regulatory means in the memristor and achieved the reconfiguration of volatile(endurance~10^(6) cycles)and non-volatile(retention~10^(4 )s)characteristics with a unified working parameter through the photoelectric coupling mode.The switching voltage of the device can be controlled 100%by this method without any limiting current.This will allow neurons and synapses to be dynamically allocated on demand.We completed the verification such as Morse code decoding,Poisson coded image recognition,denoising in the image recognition process,and intelligent traffic signal recognition hardware system under different work modes.It is verified that the device can dynamically adjust the neuromorphic according to needs,providing a new idea for the further integration of neuromorphic computing in the future. 展开更多
关键词 dynamically allocated neurons hardware application development neuromorphic deviceswhich dynamic neural network function reconfigurable memristors PHOTONICS neuromorphic computinghoweverdue artificial neurons synapses
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