Application of Probabilistic Sensor Networks to Intent Detection in Prosthetics and Other Wearable Technology

概率传感器网络在假肢和其他可穿戴技术中的意图检测中的应用

• 批准号: 2284234
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2284234
• 关键词: Application; Probabilistic; Sensor; Networks; Intent

Project Context and Potential ImpactWith over 60,000 patients with an amputation or congenital limb deficiency attending specialist rehabilitation service centres in the UK, prosthetic technology is a significant industry with the potential to impact a great many lives. However, it is clear that there is much room for improvement in prosthetic control. The typical, healthy human does not decide to activate each muscle individually in a sequence to perform an action - they simply "will" it, and the limb obeys automatically. In contrast, operating a prosthetic can be challenging, requiring considerable practice to perform any kind of complex operation, which is a limiting factor in prosthetic design and can contribute towards device abandonment. This project will aim to develop techniques to move away from "manual" prosthetic control to a computer-assisted system more reminiscent of healthy biological limb control. Project Aims and ObjectivesThe objective of the research will be to design, develop and test a system for probabilistically combining readings from multiple sensor environments (such as EMG, motion-tracking, kinematic sensing, smart home and smart phone-based sensing) in order to produce a measure of user intent when operating a wearable device such as a prosthetic limb, which can then be used to make a decision as to how the device should respond. The goal is that this will be a "drop in/drop out" network, taking advantage of whatever sensors are available at any given time, focusing on low-cost, unobtrusive sensors. This concept will be proved over a number of "test scenarios" for specific, repeatable actions, and expanded into a set of general principles which can be applied over a wide range of situations. Proposed Research MethodologyAn extensive literature review has been carried out, assessing all existing intent-sensing research and determining the merit of all viable sensor methods that could be used in the network. Probabilistic sensor network techniques have been reviewed, carrying forward and expanding upon the results of my fourth year project to produce a set of theoretical models of probabilistic intent-sensing systems. Using the models, a method has been developed to provide a "sanity-check" for machine learning systems to identify inappropriate methods (published and presented at UEMCON 2019 and EMBC 2020). These models and methods are being used to develop the intent-sensing algorithm and will be applied to test scenarios such as sitting down, beginning climbing stairs, and picking up a cup, which will initially be artificially simulated. Once the system is proven to work with the simulation, experimental data will be gathered from practical sensor networks to test it in the real world. Some of this will be gathered from healthy volunteers, and additional data may be provided by Blatchford (a leading UK prosthetics company) from patients, which can be used to verify the system and test how its success may vary from user to user. With the concept proven to work in multiple test scenarios, the procedure will be formalised into a general set of techniques which can be used for a wide range of applications by inputting various data parameters, employing machine learning techniques to train the system to associate sensor data with intent, regardless of the situation. This framework will be designed to make industrial application straightforward, and cooperation will take place with Blatchford to ensure the principles are viable in a product-focused environment.Alignment to EPSRC's Strategies and Research AreasThis project falls within the EPSRC Engineering and Healthcare Technologies research areas, with potential applications not only in the fields of prosthetic control and patient rehabilitation, but also in the wider fields of wearable technology and human-machine interfacing as a whole.

项目背景和潜在影响英国有超过 60,000 名截肢或先天性肢体缺陷患者到专业康复服务中心就诊，假肢技术是一个重要的行业，有可能影响许多人的生命。然而，很明显，假肢控制方面还有很大的改进空间。典型的健康人不会决定按顺序单独激活每块肌肉来执行某个动作 - 他们只是“愿意”它，而肢体会自动服从。相比之下，操作假肢可能具有挑战性，需要大量的练习才能执行任何类型的复杂操作，这是假肢设计的限制因素，并可能导致装置的废弃。该项目旨在开发技术，从“手动”假肢控制转向更类似于健康生物肢体控制的计算机辅助系统。项目目的和目标该研究的目标是设计、开发和测试一个系统，用于概率性地组合来自多个传感器环境（例如肌电图、运动跟踪、运动学传感、智能家居和基于智能手机的传感）的读数，以便在操作假肢等可穿戴设备时产生用户意图的测量，然后可用于决定设备应如何响应。目标是，这将是一个“插入/退出”网络，利用任何给定时间可用的任何传感器，重点关注低成本、不引人注目的传感器。这一概念将通过大量针对具体、可重复操作的“测试场景”得到证明，并扩展为一组可应用于各种情况的一般原则。拟议的研究方法已经进行了广泛的文献综述，评估了所有现有的意图感知研究，并确定了可在网络中使用的所有可行传感器方法的优点。对概率传感器网络技术进行了回顾，继承并扩展了我第四年项目的成果，以产生一套概率意图感知系统的理论模型。使用这些模型，开发了一种方法来为机器学习系统提供“健全性检查”，以识别不适当的方法（在 UEMCON 2019 和 EMBC 2020 上发布和展示）。这些模型和方法正在用于开发意图感知算法，并将应用于测试场景，例如坐下、开始爬楼梯、拿起杯子，这些场景最初将进行人工模拟。一旦系统被证明可以与模拟一起工作，将从实际传感器网络收集实验数据，以在现实世界中对其进行测试。其中一些数据将从健康志愿者那里收集，Blatchford（英国领先的假肢公司）可能会从患者那里提供额外的数据，这些数据可用于验证系统并测试其成功程度如何因用户而异。随着这一概念被证明可以在多个测试场景中发挥作用，该过程将被形式化为一组通用技术，通过输入各种数据参数，采用机器学习技术来训练系统将传感器数据与意图相关联，无论情况如何，该技术都可用于广泛的应用。该框架的设计旨在使工业应用变得简单，并将与 Blatchford 合作，以确保这些原理在以产品为中心的环境中可行。与 EPSRC 的战略和研究领域保持一致该项目属于 EPSRC 工程和医疗保健技术研究领域，其潜在应用不仅在假肢控制和患者康复领域，而且还适用于更广泛的可穿戴技术和人机领域 接口作为一个整体。