Optimization analysis of intelligent myoelectric control

Myoelectric control refers lo the use of processed electromyogram (EMG) signals in the operation of devices external to the human body. It finds numerous applications requiring good precision, quick response, design flexibility and ease in control. A variety of myoelectric controller algorithms exist, but there is immense scope for optimization. This work proposes an intelligent system which is capable of optimizing the system speed and the number of actions that can be selected. Such an optimization involves rigorous mathematical analysis of the characteristics and the interdependence of these two parameters. The intelligent systems employ a technique to continuously monitor the actions that are performed. Accordingly, they allocate the least selection duration to those actions that are executed the most number of times. This process has been termed as self-preferential time allocation and is achieved by a combination of spatial and temporal coding. Here, the temporal priorities are reassigned preferentially to spatially coded signals. Such systems that employ self-preferential time allocation eliminate the need for constant reprogramming and the design of individually programmed myoelectric controllers. The introduction of a scaling factor also induces intelligence to the system, resulting in fast, flexible and self-regulating myoelectric controllers.