Sensor Reduction on EMG-based Hand Gesture Classification

This work concerns a system based on EMG sensors, signal conditioning circuitry, classification algorithm based on Artificial Neural Network, and virtual avatar representation, useful to identify hand movements within a set of five. This is to potentially make any trans-radial upper-limb amputee able to drive a virtual or real limb prosthetic hand. When using six EMG sensors, the system is able to recognize with an accuracy of 88.8% the gestures performed by a subject, and replicated by an avatar. Here we focused on differences resulting with the adoption of a different number of sensors and therefore, by means of a very simple heuristic method, we compared different subsets of features, excluding the less significant sensors. We found optimal subsets of one, two, three, four and five sensors, demonstrating a decrease of the performance of only 0.8% when using five sensors, while with three sensors the accuracy can be as high as 81.7%.