In the context of activity recognition, wearable devices are nowadays the preferable hardware thanks to their usability, user experience and performances; at the same time, these devices present limitations in terms of computational capability and memory, which force the algorithm design to be at the same time efficient and simple. In this work, we adopt Symbolic Aggregate Approximation (SAX), a symbolic approach for information retrieval in time series data that allows dimensionality and numerosity reduction; SAX is employed here, in combination with 1-Nearest Neighbor classifier, to identify activity phases in continuous repetitive activities from inertial time-series data. The proposed approach is validated on a cross-country skiing dataset and on a daily living activities dataset.
Human Activity Recognition with Wearable Devices: A Symbolic Approach
CENEDESE, ANGELO;SUSTO, GIAN ANTONIO;TERZI, MATTEO
2016
Abstract
In the context of activity recognition, wearable devices are nowadays the preferable hardware thanks to their usability, user experience and performances; at the same time, these devices present limitations in terms of computational capability and memory, which force the algorithm design to be at the same time efficient and simple. In this work, we adopt Symbolic Aggregate Approximation (SAX), a symbolic approach for information retrieval in time series data that allows dimensionality and numerosity reduction; SAX is employed here, in combination with 1-Nearest Neighbor classifier, to identify activity phases in continuous repetitive activities from inertial time-series data. The proposed approach is validated on a cross-country skiing dataset and on a daily living activities dataset.Pubblicazioni consigliate
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