Functional Electronic Stimulation (FES)
Functional electrical stimulation and other rehabilitation technologies
Within this theme we are focusing on the design, development and evaluation of new functional electrical stimulation (FES) and other rehabilitation technologies aimed at assisting or evaluating functional movement.
At present we consider “Other Technologies” to cover prosthetics and measurement technologies. Salford’s research in this area is one of the core activities of the Centre and has developed through a series of externally funded projects dating back to the late 1990s, bringing in £2.7 million to the University. We currently hold grants totalling ~£900k from NIHR and the Stroke Association, as well as a number of externally funded studentships, have published our work in leading journals and have demonstrable impact from a series of projects and other activities, dating back over 10 years.
Focus areas
Functional electrical stimulation
Functional electrical stimulation has been shown to improve function after stroke, both as an active orthosis and as a tool to modulate the functioning of the motor control system. There is significant evidence of effectiveness in a range of applications. For example, NICE Guidelines now support the use of FES for foot drop and there is a growing body of evidence of the effectiveness of FES in the restoration of upper limb function.
More recently, new FES application areas have emerged, including gait retraining through FES cycling. However, uptake of FES and hence clinical benefit remains patchy, at best. There are many factors contributing to the limited uptake, including cost, technical limitations of current systems, difficulties in applying the technologies and, in certain cases, a lack of large scale trials to support its use. In our group we address three of these challenges:
- Development of new and improved FES technologies
- Development of new methods to evaluate FES systems
- Clinical trials of new FES technologies
The ultimate aim of these strands of work is to improve the uptake of demonstrably useful technologies. The work capitalises on the range of expertise at Salford spanning both technical and clinical aspects, and collaborating with external clinical and academic partners, and stimulator manufacturers, as required.
Prosthetics
Despite significant effort over decades, the complex relationships between lower limb prosthesis mechanical properties and gait are not fully understood. This means that there is limited information upon which to base prosthesis prescription and setup and the rate of progress in device development is slow.
At Salford we are focusing on the characterisation of the mechanical properties of prostheses. Through our improved understanding of these properties, we are developing new designs of passive prostheses and better understanding the relationships between these key parameters and gait. This work integrates directly with ongoing work in foot and ankle biomechanics (FootPrint) and gait modelling (GaitWay).
One of the major problems with upper limb prostheses remains the limited extent to which they restore functionality. Our work on upper limb prostheses is focused on the development of better tools for control of upper limb prostheses and work using new and emerging measurement technologies to develop improved methods of evaluating the functionality of upper limb prostheses.
Measurement technologies
As the measurement of human movement in real world settings is central to both the work on FES and on prosthetics, we also have a strand of research addressing the use of wearable sensors, either as inputs to device control systems, or as sources of information on activity. The interpretation and exploitation of human movement data is complex, as it often deals with data of high dimensionality exhibiting high inter and intra-subject variability and subject numbers within studies are often small.
Key to addressing these challenges is an in-depth understanding of the statistical properties of data and how advanced statistical tools can be sensibly applied to characterise the data. Therefore, much of this work is carried out in close collaboration with Dr Yannis Goulermas at the University of Liverpool, one of the UK’s leading specialists in pattern recognition, image/signal processing, and mathematical modelling and optimisation.
