Abstract
Human activity recognition (HAR) has mainly been directed to the recognition of static or quasi-periodic activities like sitting, walking or running, typically for fitness applications. However, activities like eating or drinking are neither static nor quasi-periodic. Instead, they are composed of sparsely occurring motions or gestures in continuous data streams. This paper presents a novel adaptive segmentation technique based on crosses of moving averages to identify potential eating or drinking gestures from accelerometer data. The novel crossings-based segmentation approach proposed is able to identify all eating and drinking gestures from continuous accelerometer data including different activities. A posteriori, potential gestures are classified as food or drink intake gestures using a combination of Dynamic Time Warping (DTW) as signal similarity measure and a k-Nearest Neighbours (KNN) classifier. An outstanding classification rate of 100% has been achieved.
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Anderez, D.O., Lotfi, A., Langensiepen, C. (2019). A Novel Crossings-Based Segmentation Approach for Gesture Recognition. In: Lotfi, A., Bouchachia, H., Gegov, A., Langensiepen, C., McGinnity, M. (eds) Advances in Computational Intelligence Systems. UKCI 2018. Advances in Intelligent Systems and Computing, vol 840. Springer, Cham. https://doi.org/10.1007/978-3-319-97982-3_32
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DOI: https://doi.org/10.1007/978-3-319-97982-3_32
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