Multi-Event Naive Bayes Classifier for Activity Recognition in the UCAmI Cup
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This short paper presents the activity recognition results obtained from the CAR-CSIC team for the UCAmI’18 Cup. We propose a multi-event naive Bayes classifier for estimating 24 different activities in real-time. We use all the sensorial information provided for the competition, i.e., binary sensors fixed to everyday objects, proximity BLE-based tags, location-aware smart floor sensing and the wrist’s acceleration. The results using training data-sets of 7 days show accuracies (true positives) about 68%; however for the three extra data-sets of the competition we were able to reach a 60.5% accuracy.
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2010 ◽
Vol 14
(6)
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pp. 624-630
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2021 ◽
Vol 5
(4)
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pp. 389
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2020 ◽
Vol 1
(3)
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pp. 185-199
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