Research InterestsMark's core research focuses on human movement coordination. His research covers two main areas:
1). Measuring movement, using signal processing and machine learning methods from data gathered from motion sensing and wearable devices.
2). Understanding movement behaviour, using experimental psychology & neuroscience approaches to model movement behaviour.
The applications of Mark's research cover a broad range of subjects in digital healthcare, including:
1). Technology to promote self-management of rehabilitation and physiotherapy. The recent availability of low cost motion capture technologies and virtual reality systems makes it possible to produce systems that will provide guidance and feedback of exercises done outside of the clinic. Mark's team is currently investigating the use of virtual partners to provide guidance to patients in the home/community as well as low-cost wearable technologies for monitoring exercise adherence.
2). Clinical evaluation. Often degeneration of motor function caused by disease or the recovery of function following surgery is tracked subjectively using simple self-report questionnaires. The availability of low-cost sensors provides the opportunity to accurately and objectively track changes in motor function. This can subsequently be used to predict early onset of disease and optimise recovery and rehabilitation.
3). Intelligent healthcare systems. Many current healthcare technologies are simple and hence unreliable in their function. A recent project lead by Mark, involved working with a local SME company to develop a predictive alarm algorithm for a novel bed-exit sensor (used in hospitals/care-homes to alert staff if a frail person attempts to leave the bed unaided). The proof of concept developed used a real-time algorithm based on machine learning to raise the alarm of attempted bed-exit before the individual had left the bed.
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BiographyMark is Assistant Professor of Healthcare Technology and Behaviour Change at the Institute of Digital Healthcare.
Mark's main research interests are centred on human movement coordination. He combines his skills in engineering and behavioural neuroscience to produce novel methods and models, describing how people use information across the senses to co-ordinate their movements. He has published a number of papers modelling how the information we receive through the senses influences our movement. In particular, Mark is interested in the timing properties of movements and more recently has been involved in projects investigating the use of rhythmic timing cues for movement rehabilitation following neurological disease. He is currently investigating the use of motion tracking technology and machine learning for healthcare and rehabilitation applications.
Prior to his current position, Mark was a Research Fellow in the Sensory Motor Neuroscience (SyMoN) lab at the University of Birmingham. He completed his PhD at Aston University, developing intelligent systems to discriminate between different walking patterns. Before completing his PhD, Mark qualified with an MEng in Electronic Systems Engineering (Aston University) and worked for 3 years as a Design Engineer in the telecommunications industry.
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