Abstract
Among the major challenges in the realization of practical health monitoring systems is the identification of short-duration events from larger signals. Time-series segmentation refers to the challenge of subdividing a continuous stream of data into discrete windows, which are individually processed using statistical classifiers to recognize various activities or events. In this paper, we propose a probabilistic algorithm for segmenting time-series signals, in which window boundaries are dynamically adjusted when the probability of correct classification is low. Our proposed scheme is benchmarked using an audio-based nutrition-monitoring case-study. Our evaluation shows that the algorithm improves the number of correctly classified instances from a baseline of 75%–94% using the RandomForest classifier.
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