The neuromechanics of human balance and walking

Neuromechanics considers how the nervous and musculoskeletal systems interact to plan and perform body movements. Understanding human neuromechanics is integral to advancing multiple aspects of assistive technology and rehabilitation-for example, the design and use of brain-computer interfaces to control prosthetics and orthotics. This PhD will use cutting-edge technologies to advance understanding of the neuromechanics of human balance and walking.

Transcranial magnetic stimulation (TMS) is non-invasive brain stimulation that can probe the connection between brain and muscles. Dr Davies (lead supervisor) has developed a highly novel system to deliver TMS during walking, allowing study of how the brain is involved in controlling muscle activity during walking.

Peripheral nerve stimulation (PNS) can probe transmission of information through networks of neurones in the spinal cord. When delivered while a person is walking, it allows study of how these networks are involved in controlling muscle activity during walking.

The systems to deliver TMS and PNS during walking are combined with an instrumented dual-belt treadmill, an optoelectronic motion capture system and a virtual reality environment that are integrated into a real-time feedback loop.

The response to TMS and PNS is a muscle twitch, which is measured using surface electromyography (EMG). In this PhD the student will record these muscle responses using cutting-edge high-density EMG, which will allow you to study the response of individual spinal motor neurones as well as the spatial distribution of the response across a muscle.

The student will be supported to codevelop their own research questions to apply these highly novel, internationally unique capabilities to probe the neuromechanics of human balance and walking. This will be aligned to their own interests and expertise, and may include the effects of environment on neuromechanics (such as walking on unstable ground, or while distracted) and/or using blind source separation and deep learning methods to decompose high-density EMG signals into individual neurone firing trains and studying the effects of stimulation at the level of the spinal motor neurone.

This project will take advantage of a phenomenal array of opportunities to study how the nervous and musculoskeletal systems interact to plan and perform body movements, including I. studying the cortical and spinal control of muscle activity in the same experiment;

II. gaining a window into the spinal cord by decomposing high-density EMG signals using blind source separation and deep learning methods to reveal the firing patterns of individual motoneurones in response to TMS and PNS; III. combining neurophysiological information with biomechanics, linking neural control and movement;

IV. using virtual reality environments to study the effect of environment on neuromechanics.

Ultimately, this will provide rich information that can be used to inform the design and use of brain-computer interfaces to control prosthetics and orthotics.

The student will be supported to work in line with the principles of responsible innovation, including exploring and reflecting on possible impacts and implications and engaging in debate around these issues. This will include incorporating patient and public involvement in your work, and engaging with stakeholders within and outside the discipline.