This paper proposes a novel sequential pattern recognition method enabling calculation of a posteriori probability for learned and unlearned classes. In this approach, probability density functions of unlearned classes are incorporated in a hidden Markov model to classify undefined classes via model parameter estimation using given learning samples. The technique can be applied to various pattern recognition problems such as motion classification with electromyogram (EMG) signals and in support for disease diagnosis. In the experiments conducted, motion classification from EMG signals was implemented with three subjects for eight learned/unlearned forearm motions. The proposed method produced higher levels of classification performance (learned motions: 90.13%; unlearned motions: 91.25%) than previous approaches. The results demonstrated the effectiveness of the technique.
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