Non-invasive optical sensing of muscle activity.

用于肌肉活动的非侵入式感测的现场标准技术是肌电图（EMG）。肌电传感器有许多局限性，包括对电噪声的敏感性和传感器与肢体表面的稳定机械耦合的要求。本项目将研究使用红外线传感肌肉活动的新技术的可行性。第一阶段的工作将涉及设计一个由模块化组件组成的光学传感器。初步测试将使用不同的电路组件和设计收集经验数据。将进行光通过组织传播的生物物理分析。经验数据和数学模型将确定最佳电路元件和参数。第二阶段将专注于将多个光学传感器组合成一个阵列，用于数据采集。参与者将在佩戴阵列和EMG传感器的同时进行抓握和手指运动。简单的线性分类方法将光学数据与EMG数据进行基准测试。该项目的第三阶段将侧重于对用作控制信号的光学数据进行实时信号处理。这项工作可能会集中在光信号的非平稳性质和从这些类型的信号中提取有意义的特征的最佳方法。黑盒方法，即数据训练黑盒分类器，并分析所得分类器结构，将确定哪些光学特征最相关。进一步分析将包括传感器位移后对读数的影响。这对于假肢使用者来说是特别有意义的，他们可能会发现他们的设备在进行日常活动时发生小碰撞后不可预测。最终的传感器将是一种电池供电的无线可穿戴设备，能够进行远程数据记录，并在临床假肢接受腔中进行测试。

The field-standard technology for non-invasive sensing of muscle activity is Electromyography (EMG). Electromyography sensors have a number of limitations, including susceptibility to electrical noise and a requirement that the sensors have a stable mechanical coupling to the limb surface.This project will study the feasibility of using a novel technique for sensing muscle activity using infrared light. The first stage of the work will involve designing an optical sensor comprised of modular components. Preliminary tests will collect empirical data using varying circuit components and designs. A biophysical analysis of light propagation through tissue will be undertaken. Empirical data and mathematical models will determine optimal circuit components and parameters. The second stage will focus on combining multiple optical sensors to form an array, used for data acquisition. Participants will perform grasps and finger movements while wearing the array and EMG sensors. Simple linear classification methods will benchmark optical data against EMG data. The third phase of the project will focus on real-time signal processing of optical data for use as a control signal. This work is likely to focus on the non-stationary nature of optical signals and the best methods for extracting meaningful features from these types of signals. A black-box approach whereby data trains black box classifiers and the resulting classifier structures are analysed will determine what optical features are most relevant.Further analysis will comprise the impact on readings following a sensor displacement. This is of particular interest for prosthesis users who may find their devices unpredictable after small collisions while performing daily activities. The final sensor will be a battery-powered, wireless wearable device capable of remote datalogging, tested within clinical prosthetic sockets.