Functional hand and wrist motion control using functional electrical stimulation electrode arrays

使用功能性电刺激电极阵列进行功能性手部和腕部运动控制

• 批准号: 2891052
• 金额: --
• 依托单位: University of Southampton
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2891052
• 关键词: Functional; hand; wrist; motion; control

This research programme will develop wearable technology that assists stroke patients in their activities of daily living. In recent years functional electrical stimulation (FES) electrode arrays have been developed by several institutions, but control systems have failed to capitalise on their potential to assist the complex hand movements that are needed to perform everyday tasks. This presents an enormous unmet need since 80% of stroke survivors suffer from upper limb impairment and stroke prevalence in the UK is due to rise to more than 2 million people. Effective control strategies for FES arrays can therefore substantially improve the rehabilitation of patients suffering from upper-limb impairmentsThis project will develop the theory of multiple model switched adaptive control (MMSAC) and apply it to control FES arrays. This potentially will enable a highly accurate model based controller to be used, while eliminating the need for identification and manual tuning. An additional extension to the framework will automatically capture the user's voluntary intension by predicting the way in which a patient wishes to carry out a particular motion and provide assistance as required. This project will firstly develop a design framework enabling MMSAC to be applied to arrays, and then investigate approaches to reduce the underlying set of elements (the subspace). It will then carry out trials at with patients at Berkshire Hospital to test the effectiveness.

这项研究计划将开发可穿戴技术，帮助中风患者的日常生活活动。近年来，几个机构开发了功能性电刺激(FES)电极阵列，但控制系统未能充分利用它们的潜力来辅助执行日常任务所需的复杂手部运动。这是一个巨大的未得到满足的需求，因为80%的中风幸存者患有上肢损伤，英国的中风患病率将上升到200多万人。因此，有效的FES阵列控制策略可以显著改善上肢损伤患者的康复。本项目将发展多模型切换自适应控制(MMSAC)理论，并将其应用于FES阵列的控制。这可能会使用高度精确的基于模型的控制器，同时消除识别和手动调整的需要。框架的额外扩展将通过预测患者希望执行特定运动并根据需要提供帮助的方式来自动捕获用户的自愿意图。这个项目将首先开发一个设计框架，使MMSAC能够应用于数组，然后研究减少底层元素集(子空间)的方法。然后，它将在伯克希尔医院的患者中进行试验，以测试有效性。