Abstract
The characteristic of the data stream is continuous, non-stationary, and very large or infinite size. Data stream classification requires the algorithm that able to classify data instance and learn from data incrementally. In this paper, the algorithm with Weighted Dimension is proposed and applied for the Kinect bodily posture recognition. The human body portions, as the input features, are calculated from Skeleton Joint data. The proposed algorithm successes in recognizing three human postures: stand, sit_on_chair, and sit_on_floor. The result of classification is 99.02% on average and 100% on moving accuracy. Moreover, the algorithm always learns from the data instances and some labels so the algorithm is able to learn whether the data instances are changed. In the other words, the algorithm could handle the concept drift in the data stream.
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Jariyavajee, C., Sirinaovakul, B., Polvichai, J. (2018). Bodily Posture Recognition with Weighted Dimension on Kinect Data Stream. In: Meesad, P., Sodsee, S., Unger, H. (eds) Recent Advances in Information and Communication Technology 2017. IC2IT 2017. Advances in Intelligent Systems and Computing, vol 566. Springer, Cham. https://doi.org/10.1007/978-3-319-60663-7_14
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DOI: https://doi.org/10.1007/978-3-319-60663-7_14
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