Increasing performance of a pattern recognition system using a sEMG signal by setting multi-references

This paper proposes a special technique for pattern classification problems using the sEMG signal from human forearm muscles. For improvement of classification accuracy, a multi-reference is set for each class so that the classifier can cover a wide range of obtained signals for training. The results of classification accuracy through an off-line simulation were analyzed to validate the proposed concept.