Pattern classification with meta-cognition and online sequential learning algorithm

An integral part of modem day health-care is monitoring the physical activities of human beings. In this paper, we deal with automatic recognition of some daily activities based on signals measured using easily-available smart phones. We present a neural-network based methodology to classify these signals. In contrast to typical conventional techniques we use sequential processing of signals and circumvent pre-processing and feature extraction. In addition, we introduce meta-cognition to reduce the computations required during the training stage. We demonstrate that our approach yields satisfactory recognition accuracies.