Supervised by Dr Adel Al-Jumaily and Dr Ahmed Al-Ani of the School of Electrical, Mechanical and Mechatronic Systems, Khushaba, a PhD student with the University of Technology, Sydney is developing the mathematical basis for identifying what biosignals relate to particular arm movements and where electrodes should be placed to achieve the optimum result.
Their project presents novel 'swarm intelligence' based algorithms to tackle many of the problems associated with the current myoelectric control strategies.
It has been known for some time that human muscle activity, known as the Electromyogram (EMG), carries the distinct signature of the voluntary intent of the central nervous system.
These myoelectric signals are already being used to control prosthetic devices, but there is a lot of refining to do before a robotic arm will respond instantaneously and accurately to the intention to move.
Right now the best that can be done is a few simple tasks with rather unsatisfactory performance, due to poor signal recognition and the high computational cost that leads to extra time delays.
Improvement in analysing the myoelectric signals will spur improvement of the hardware, and that's where our work is directed, says Rami.
He notes, "The way the members of a colony of ants will interact to achieve goals like finding food is metaphor that can be expressed in algorithms that are powerful tools for pattern recognition.
"Current methods for capturing biosignals on the forearm can involve mounting up to 16 electrodes on the skin, generating a vast quantity of data to be processed. We have already demonstrated that applying swarm logic will both simplify that set-up and achieve significantly better results.
"Applying the algorithms on 16-channel EMG datasets from six people found patterns that made it clear only three surface electrodes were actually needed.
"These few electrode positions achieved 97 per cent accuracy in capturing the crucial biosignals for movement. This significantly reduced the number of channels to be used for a real time problem, thus reducing the computational cost and enhancing the system's performance.
"The result was confirmed on a second dataset consisting of eight channels of EMG data collected from the right arm of thirty normally limbed subjects (twelve males and eighteen females).
"We hope one accuracy will lead to another and it will be the very near future when amputees, who can still imagine moving a lost limb, will have access to a device that can truly respond to their intentions."
And so the crossover from science fiction to science reality is that far away now, hopes Rami Khushaba.