Enabling Technologies for Sensory Feedback in Next-Generation Assistive Devices

下一代辅助设备中的感官反馈支持技术

• 批准号: EP/M025977/1
• 负责人: Kianoush Nazarpour
• 金额: $ 184.03万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2015
• 资助国家: 英国
• 起止时间: 2015 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FM025977%2F1
• 关键词: Enabling; Technologies; Sensory; Feedback; Next

An artificial arm, or prosthesis, is an example of technology that can be used to help somebody perform essential activities of daily living after a serious injury that results in the loss of their arm. Such activities might include eating, washing, opening doors, or shaking hands with a friend. Many artificial arms on the market these days are highly sophisticated, offering individual finger movement, and even movement of segments within a finger, that resemble the natural arm and hand. These prosthetic arms are often controlled by sensing the contractions in the muscles of the remaining arm to which the prosthesis is attached, allowing the user to operate the arm by flexing their muscles. However, one key aspect of artificial arms that is currently missing is the sense of feedback. In other words, the user does not know where the arm is or how wide open the hand is without looking at it, and if a delicate object is picked up, there is no sense of how hard it is being gripped. This leads to slow and awkward use of the artificial arm and prevents its use from becoming truly natural.The goal of this project is to develop technologies that will enable the next generation of assistive devices to provide truly natural control through enhanced sensory feedback. Our long-term vision is for artificial arms that provide the user with a sense of feedback that recreates the natural feedback associated with a real arm. To enable this level of feedback, we must meet two clear objectives: to generate artificial signals that mimic those of the natural arm and hand, and to provide a means of delivering those signals to the nervous system of a prosthesis user. These objectives will be achieved by: building new fingertip sensors to give the prosthesis a realistic sense of touch, including pressure, shear and temperature; developing a 'virtual hand' that mimics the nerve impulses that would be produced by a real hand, giving the user a sense of position of an artificial hand; and designing electrodes and a stimulation system that can deliver the simulated nerve impulses directly to the individual's nervous system.Building this level of feedback into prosthetic devices will enable much higher levels of function to be achieved than is currently possible. Device users would be able to naturally reach out and pick up a glass, for example, whilst maintaining eye contact in a conversation, or pick up an apple without bruising it. This will advance the field of prosthetics, provide enhanced function to prosthesis users and decrease the learning time involved when acquiring a new device.

假臂，或假肢，是一种技术的例子，可以用来帮助一个人在严重受伤后进行必要的日常生活活动，导致他们失去手臂。这样的活动可能包括吃饭、洗澡、开门或与朋友握手。如今市场上的许多人造手臂都非常复杂，可以单独移动手指，甚至可以移动手指内部的部分，就像天然的手臂和手一样。这些假肢通常是通过感知假肢所连接的剩余手臂肌肉的收缩来控制的，允许使用者通过伸展肌肉来操作手臂。然而，人工手臂目前缺乏的一个关键方面是反馈感觉。换句话说，使用者不看一眼就不知道手臂在哪里，手张开得有多大，如果拿起一个精致的物体，也感觉不到它被抓住的力度有多大。这导致了人工手臂的使用缓慢和笨拙，并阻止了它的使用变得真正自然。该项目的目标是开发技术，使下一代辅助设备能够通过增强的感觉反馈提供真正的自然控制。我们的长期愿景是为用户提供反馈感觉的人造手臂，重建与真实手臂相关的自然反馈。为了实现这种水平的反馈，我们必须达到两个明确的目标：产生模仿自然手臂和手的人造信号，以及提供一种将这些信号传递到假肢使用者的神经系统的方法。这些目标将通过以下方式实现：构建新的指尖传感器，使假肢具有逼真的触感，包括压力、剪切力和温度；开发一种模仿真手产生的神经脉冲的“虚拟手”，让使用者感觉到假手的位置；设计电极和刺激系统，可以将模拟的神经脉冲直接传递到个体的神经系统。将这种级别的反馈构建到假肢设备中，将能够实现比目前可能实现的更高水平的功能。例如，设备用户将能够自然地伸手拿起玻璃杯，同时在对话中保持眼神交流，或者拿起苹果而不会擦伤它。这将推动假肢领域的发展，为假肢用户提供增强的功能，并减少获得新设备时涉及的学习时间。

项目成果

Processing occlusions using elastic-net hierarchical MAX model of the visual cortex

使用视觉皮层的弹性网络分层 MAX 模型处理遮挡

• DOI: 10.1109/inista.2017.8001150
• 发表时间: 2017
• 作者: Alameer A

An elastic net-regularized HMAX model of visual processing

• DOI: 10.1049/cp.2015.1753
• 发表时间: 2015
• 作者: Ali Alameer;G. Ghazaeil;P. Degenaar;K. Nazarpour

Object Recognition With an Elastic Net-Regularized Hierarchical MAX Model of the Visual Cortex

• DOI: 10.1109/lsp.2016.2582541
• 发表时间: 2016-06
• 期刊: IEEE Signal Processing Letters
• 影响因子: 3.9
• 作者: Ali Alameer;Ghazal Ghazaei;P. Degenaar;J. Chambers;K. Nazarpour

Objects and scenes classification with selective use of central and peripheral image content

• DOI: 10.1016/j.jvcir.2019.102698
• 发表时间: 2020-01-01
• 期刊: JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
• 影响因子: 2.6
• 作者: Alameer, Ali;Degenaar, Patrick;Nazarpour, Kianoush
• 通讯作者: Nazarpour, Kianoush

Improvement in modelling of physiological tremor by inclusion of grip force in quaternion weighted Fourier linear combiner

• DOI: 10.1049/cp.2015.1782
• 发表时间: 2015
• 作者: K. Adhikari;S. Tatinati;K. Veluvolu;K. Nazarpour