Research Theme: Intelligent Sensing Laboratory
The Intelligent Sensing Laboratory was built and equipped with £0.75M funding from Newcastle University Vice-Chancellor’s office as part of the University’s £30M Research Investment Fund. This laboratory was officially opened on October 13, 2016.
We built and equipped the Intelligent Sensing Laboratory with £0.75M funding from Newcastle University’s £30M Research Investment Fund. The laboratory was officially opened in October 2016.
The lab is used for cross-disciplinary work, primarily focussed on multi-sensor data acquisition and interpretation problems. Current research in the lab focusses on healthcare engineering, in particular the control of advanced upper-limb prosthetic devices.

Prosthesis control
We develop and test end-to-end upper limb prosthesis control systems.
To achieve this, we use two platforms. Our experimental platform can capture and synchronise real-time data from a range of sources and control a variety of prosthetic hands. This flexible system allows rapid iteration and testing of novel approaches in a closed-loop control environment.
We also prototype stand-alone systems for prosthesis control. These systems can control a variety of devices and can be used to collect data anywhere. Our prototypes are in regular use as research tools, providing the user feedback necessary to create devices robust enough for real world use.

Leaving the lab
To demonstrate real world applicability, prosthetics research must leave the laboratory. We have developed and deployed devices which can record and log data in the home environment and beyond.
Our ecosystem of interoperable devices can be configured flexibly, enabling interactions at various levels of user privacy and between multiple stakeholders. We can stream fully encrypted information in real-time from anywhere with a phone signal. Our Internet of Things network is entirely based on secure and scalable open-source technology.

Motor learning
Our prosthesis control work builds on neuroscience and motor learning. We have developed an approach that uses techniques originally proposed by scientists working at Newcastle University’s Institute of Neuroscience. The method starts with learning to control an on-screen cursor using muscle activity. The task creates novel patterns of muscle activation which can be mapped to prosthesis commands. We have validated this method in studies with limb-intact participants and limb different volunteers.
We have implemented this approach in a prosthesis control system and are developing and testing novel home-based training protocols which will allow people to quickly learn how to use the system.
Novel socket design
The socket is the interface between an advanced upper-limb prosthesis and the user. The reliability and quality of any prosthesis control system is entirely dependent upon the socket to deliver stable signals from the user’s residual limb. Despite this, standard clinical sockets have not changed significantly since the 1960s.
We use digital techniques, such as 3D scanning and additive manufacturing, to explore alternative methods of designing and manufacturing sockets for advanced upper-limb prostheses. To ensure real world applicability, all of our work is informed by clinical colleagues who train prosthetists for the National Health Service.

Gamification
Motor learning techniques depend on people repeating the same activity many times. This repetition changes behaviour by exploiting the brain’s plasticity. Sustained practice of any task can become tedious. We collaborate with game design experts in the School of Computing to make myogames -fun and engaging tools for learning to control upper-limb prosthetics.
Engagement alone is unlikely to help anyone control a prosthesis. Our myogame designs embed knowledge gained from our motor learning research in game-loops informed by the principles of game-design.

- 3D-Printing and upper-limb prosthetic sockets: promises and pitfalls
- J Olsen, S Day, S Dupan, K Nazarpour, M Dyson, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021
- Perception of Game-Based Rehabilitation in Upper Limb Prosthetic Training: Survey of Users and Researchers
- CA Garske, M Dyson, S Dupan, K Nazarpour JMIR Serious Games, 2021
- Learning, generalization, and scalability of abstract myoelectric control
- M Dyson, S Dupan, H Jones, K Nazarpour, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2020
- Myoelectric control with abstract decoders
- Dyson M, Barnes J, Nazarpour K. Journal of Neural Engineering, 2018
We need a steady stream of volunteers willing to help us with our research. We are always interested in hearing from individuals with upper-limb loss (hands or fingers).
Our experiments vary in duration, from one to three hours. They involve a range of non-invasive techniques such as:
- recording muscle signals
- performance of instrumented tasks
- control of advanced prosthetic hands
All procedures are safe. Most people find that it's fun to take part in our experiments.
An independent Ethical Review Committee approves all our experimental protocols. We are very flexible in relation to appointment times. We can cover UK travel and accommodation expenses.
- If you would like to take part in our experiments, send an email to Dr Matthew Dyson.
We work with many international organisations and institutions from a wide range of industry sectors.
For more information on future collaborative project opportunities, contact Dr Matthew Dyson:
- email: matthew.dyson@ncl.ac.uk
- phone: +44 (0)191 208 6682
Intelligent Sensing
E2.01 Merz Court
School of Engineering
Newcastle University
NE1 7RU
Newcastle upon Tyne
United Kingdom
email: intellsensing@ncl.ac.uk
phone: +44 (0)191 208 6682