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.