Non-invasive optical sensing of muscle activity.

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.