Towards a Dataglove for Arthritis…

As rheumatologists we spend a lot of time examining the hands of people with arthritis. We examine them carefully for swelling and tenderness and use this information to assess disease activity. The effort to make this a more scientific assessment has paid off in the success of ‘treat to target’ protocols. This is fine for early arthritis, but there are other important dimensions of the problem we are only beginning to understand. Historically, rheumatologists have not been particularly interested in the scientific measurement of joint movement and function. This is a major gap in our understanding of arthritis and how it affects our patients from day to day. If we could better understand how stiffness affects joints, we could help our patients optimise their function and perhaps allow them to remain in work by testing different functional strategies in the workplace. Stiffness is also the forgotten member of the trio of symptoms that are widely regarded to reflect disease activity in rheumatoid arthritis. Technology has now reached the point where it should be possible to get accurate biometric data to record joint angles, movement and even touch. And technically it is now possible for these measurements to be recorded at home or in the workplace with minimal interruption to the normal routine. So enter stage – the ‘dataglove’. So perhaps it’s time to let our patients’ fingers do the talking…

The history of datagloves

The first wired electronic glove was patented by Thomas Zimmerman in 1982, and in 1989 Jaron Lanier patented ultrasonic and magnetic motion tracking technology to create the Powerglove. Lanier will also be remembered for coining the phrase ‘virtual reality’. The optical flex sensor used in the dataglove was invented by Young L. Harvill who scratched the fiber near the joint to make it locally sensitive to bending. The Power glove was designed for gaming but sadly for Lanier he was to lose control of the company VPL research. Interestingly, one of his VR predictions has been fulfilled: “Medical students could practice surgery on virtual cadavers that spurt virtual blood after a misplaced incision. Such uses are speculative so far, but few people doubt the technology’s potential”. Potential virtual reality applications in rheumatology include virtual homes or workplaces where the patient can explore functional problems and possible solutions. However, Lanier was well ahead of his time, and viable medical VR applications are still few and far between.

The Technology of Datagloves

The state of the art in current dataglove technology is represented by the 14-sensor 5DT dataglove ‘Ultra’ and the 22-sensor Cyberglove II. You can watch videos online of the 5DT and Cyberglove systems. At the moment these are powerful but expensive gloves which often require considerable effort to calibrate and customise. Working in conjunction with a team from the University of Ulster’s Integrated Systems Research Centre in Magee, we have done some work on programming and customising the 5DT Dataglove Ultra for patients with arthritis. Our work so far has focused on improving the repeatability and ease of calibration of the glove. We have also carried out work on the effect of using an thin inner glove – important in the healthcare setting to avoid problems with cross infection between patients. The 5DT dataglove uses optical fibre bend sensors. This technology is already fairly accurate, but has its limitations. We have therefore developed a new type of multi-functional dataglove using no fewer than 47 sensors. Our dataglove is designed to be used by people with arthritis by incorporating features to enable it to be easily put on and taken off. This will be tested in a group of patients with arthritis. We have also developed a user interface that will help patients calibrate the glove independently and use it accurately in the home setting.

project ‘digit-ease’

Our aim is to test the feasibility of using a sophisticated dataglove to take detailed measurements of joint position and movement in people with rheumatoid arthritis. We will test how closely the measurements match how our patients are feeling. We will be testing how much discomfort patients have in using the dataglove and how easy or difficult they find the visual interface. We hope that if the initial tests prove successful that we will be able to test the dataglove’s ability to detect changes in function after treatment.

For those of you who are interested in a little more detail, I have posted a copy of my recent presentation at the .med conference in Dublin on Dec 7, 2012 (#dotmed on Twitter). My co-workers James Connolly, Kevin Curran, Joan Condell and I have also recently submitted a paper for publication on the use of neural network theory to improve the accuracy of the data glove. I will provide a link if and when it is accepted for publication as this paper contains a lot more detail about our results so far.

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