Regressing dexterous finger flexions using machine learning and multi-channel single element ultrasound transducers

Abstract

Human Machine Interfaces or "HMI's" come in many shapes and sizes. The mouse and keyboard is a typical and familiar HMI. In applications such as Virtual Reality or Music performance, a precise HMI for tracking finger movement is often required. Ultrasound, a safe and non-invasive imaging technique, has shown great promise as an alternative HMI interface that addresses the shortcomings of vision-based and glove-based sensors. This thesis develops a first-in-class system enabling real-time regression of individual and simultaneous finger flexions using single element ultrasound transducers. A comprehensive dataset of ultrasound signals is collected is collected from a study of 10 users. A series of machine learning experiments using this dataset demonstrate promising results supporting the use of single element transducers as a HMI device.