Man-machine interfacing based on ultrasound wearable technology for controlling upper limb prostheses

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Current state-of-the-art control methods for upper limb prostheses are based on muscle electrical activity detected with non-invasive electrodes (surface EMG). However, surface EMG has poor spatial resolution, is influenced by the skin-electrode interface, has a detection volume limited to few millimetres up to 1 centimetre depth (therefore not accessing deep muscles), and varies its characteristics as a consequence of fatigue and other physiological factors. These limitations pose important issues in conventional myo-control strategies for active prostheses, associated to an abandonment rate of approximately 50%. This project aims to investigate the usage of an alternative, clinically-viable and non-invasive approach -muscle ultrasound sensing and imaging --for detecting the motion of musculoskeletal structure sand establishing a neural interface between patients and prosthetic arms/hands. For this purpose, we will develop a wearable ultrasound system to detect and characterize the activity and elastic status of muscle structures and machine learning methods to map the ultrasound signals into commands for bionic limbs. Ultrasound techniques can penetrate deep in soft tissue (a few cm to15cm) with scalable spatial resolution (tens of microns to ~1mm), and have shown excellent sensitivity in detecting subtle local tissue motion. Recent advances in ultrafast ultrasound have enabled the detection of deep tissue motion at micrometre level to be detected at a temporal resolution <1 millisecond.Hence ultrasound can provide a direct measure of muscle movement by image monitoring the entire muscle cross-sectional are a with very high temporal resolution, and is not influenced by the confounding factors affecting EMG recordings. Moreover, ultrasound systems can be miniaturized and therefore mounted as wearable devices. The project will develop for the first time ultrasound and signal processing technology for wearable man-machine interfacing and will translate these developments into clinical usability tests.Ongoing work in the research group and the PhD aims. The Neuromechanics and Rehabilitation Technology group at the Department of Bioengineering of Imperial College London, led by Prof. Dario Farina, focuses on electrophysiology techniques for the study of neural control of movement, bioelectrodes and biosignal processing, neurorehabilitation, active prostheses, human-machine interfaces, and motor neuron recordings in vivo. There search of the group has made major contributions to muscle electrophysiology and human-machine interfacing by developing concepts and techniques to fill the gap between the neural and biomechanical investigation of human movement. The Ultrasound Laboratory for Imaging and Sensing (ULIS), led by Professor Mengxing Tang, mainly focuses on developing new ultrasound imaging and sensing techniques for a wide range of biomedical applications.