Influence of sEMG electrode matrix configuration on hand gesture recognition performance

This paper presents a study of a human-machine interface in the form of three parallel electromyographic bands placed around the user's forearm, with the influence of sEMG electrode layout on gesture recognition performance as the primary focus. Tested electrode configurations included setups ranging from 4 to 24 electrodes, with varying placement on the subject's forearm, using both monopolar and bipolar measurement methods. An artificial neural network with softmax output layer was used as the gesture classifier. The test data included three participants performing nine gestures in various sequences, over the course of two days. The work focuses on minimisation of the setup size and complexity, while simultaneously preserving best possible gesture classification performance. The most reliable results were achieved using 24-electrode configuration (F
₁
= 0.96), however, comparable efficacy was obtained using less complex designs: two-band bipolar (F
₁
= 0.95) and single-band chained differential (F
₁
= 0.94). Moreover, it has been observed that for classification of a smaller command set (5 gestures), satisfactory results can be obtained using a simple 4-electrode setup (F
₁
= 0.94).