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AI control system for upper-limb prosthetic device

AI control system for upper-limb prosthetic device
上肢假肢装置人工智能控制系统
批准号:
10045926
负责人:
金额:
$3.19万
依托单位:
依托单位国家:
英国
项目类别:
Grant for R&D
财政年份:
2022
资助国家:
英国
项目状态:
已结题
起止时间:
2022 至 --

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英文摘要
Axonnr is a robotics company focussed solely on the primary goal of developing technology to improve the lives of others. Using our expertise in Artificial Intelligence and Robotics we are working tirelessly towards delivering new prosthetic technology, a technology which will offer individuals the hope that the movement they thought once lost can be replaced. Axonnr will strive to provide the technology, software, research and developments that revolutionise and advance the robotics prosthetics space. Axonnr will aim to lead the way in using robotics and AI to modernise the delivery of prosthetics and other forms of medical technology to solve issues once deemed impossible to fix. We are a small company that aim to make big waves.Our project is devoted towards the creation of a new control system that will provide clinicians with the opportunity to wield the latest developments in robotics and AI to improve current upper-limb prosthetic device functionality. Encompassing all the necessary processing hardware and software, the control system in combination with a clinicians existing device, will allow patients to utilise our bespoke brain-inspired framework to create more natural and life-like movements on their device. We are working towards making our control system compatible with prosthetic devices used by a transradial amputee (below-elbow amputation) allowing them to replicate multi-articulating movements on their prosthetic device seamlessly.Our core technology will exploit the benefits of using an AI focussed chip enabling us to bring this control system to clinicians across the UK and Ireland. The chip will function within the control system by processing the signals produced by an individuals muscles, which are non-invasively captured by our electrode sensors, and identifying patterns within these signals. The patterns would represent signals that are characteristic of typical hand movements and this is made achievable by using our underlying AI framework that will be deployed on the chip. Using this pattern recognition functionality, this will allow signals captured in real-time to be mapped to movements which can then be replicated on the prosthetic hand.The technology we will offer clinicians and to patients will be innovative as it will be the first prosthetic device leveraging the gains offered from new developments in AI and it will showcase a new methodology for using AI to advance prosthetic technology. Our technology will result in greatly improved performance and increased power efficiency yet remain affordable and available.
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