Ultrawideband On-Body Area Network for Navigational Support

The number of patients with blindness and partial sightedness has grown over time. These vision impairments significantly affect the quality of life, and navigation is one of the main challenges these patients face. Although assistive devices were introduced in the past, they have a low acceptance rate due to their limited usability, cost, portability, battery life and higher cost. This paper proposes an on-body area network based on ultra-wideband (UWB) technology and a novel algorithm to detect and classify the obstacles. The on-body radar network captures the backscattered UWB pulses and publishes them to an MQTT network at a rate of 5 frames per second. Then the algorithms unit obtains the UWB radar frame and generates a detection image. This image presents the position and features of the obstacles. At the final stage, detection images are fed to machine learning classifiers to identify the obstacles. The proposal system was experimentally validated and obtained a classification accuracy of 93% with the support vector machine utilising the radial basis function kernel.