Crosspoint Switch Based EMG Frontend for Pattern Recognition Myoelectric Control

Control of myoelectric prosthesis can be achieved in two ways, direct control involving measuring the amplitude of the myoelectric signal or through pattern recognition (PR). PR has the potential to provide a more intuitive control for multi-functional myoelectric prosthesis compared to direct control. Accuracy of PR systems has been shown to improve with increasing number of EMG channels. However, increasing the number of channels requires placing additional electrodes which is not feasible due to space constraints on the residual limb or incorporating multiple hardware components which poses a drawback of increased weight, cost and complexity of the prosthesis. This thesis presents the concept and design of a novel EMG acquisition system to acquire larger number of channels without increasing the number of electrodes placed or the complexity of the signal acquisition system within the prosthetic device. A prototype system was developed and tested to validate performance. Experiments were performed on able-bodied subjects to evaluate system performance in EMG pattern recognition. Subjects were requested to perform nine different hand movements while EMG data was collected into training and test groups. Test results indicate a 15% improvement in classification accuracy with the new system when compared to conventional systems.