Controlling Intelligent Prostheses Using ETSI MEC

Abstract

Currently, algorithms required to operate intelligent prostheses are usually executed locally, using the embedded system integrated into prostheses. However, the limited computational capabilities of such devices usually constrain the complexity and thus the accuracy of the algorithms that determine the intention of the user, resulting in a sub-optimal user experience. Modern networking technologies in combination with edge cloud solutions offer significantly more raw computational power (in comparison with embedded solutions). With predictable latency and low overhead, more robust and accurate algorithms could be applied for processing data collected by sensors, thus providing more accurate control, at the cost of the latency introduced by the network. The goal of my research project is to determine whether 5G networks and edge cloud solutions are viable as the background for offloading parts of a control system to edge cloud applications and address the challenges of offloading control in time-critical settings with a relatively high-frequency input such as electromyography (EMG) sensors. This paper describes a framework for offloading near real-time computations using an IP network along with a set of tools for benchmarking networks and the protocol itself in different network conditions. The control of intelligent prostheses is an application the framework is specifically tailored to. EMG data is collected and used to determine the intended action, with the accuracy depending on the complexity of a classifier algorithm. Other applications related to control systems with similar requirements were also kept in mind during the design phase. The capabilities of different networking technologies in combination with the framework are shown through the results of various benchmarks and experiments.