Abstract
Accurately recognizing the rare activities from sensor network based smart homes for monitoring the elderly person is a challenging task. Activity recognition datasets are generally imbalanced, meaning certain activities occur more frequently than others. Not incorporating this class imbalance results in an evaluation that may lead to disastrous consequences for elderly persons. To deal with this problem, we evaluate a new model OS-WSVM combining Over-Sampling (OS) with Weighted SVM (WSVM). Our experiments are carried out on real world datasets, demonstrating that OS-WSVM is able to surpass SVM, OS-SVM and WSVM in Human Activity Recognition tasks.
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Abidine, M.B., Fergani, B., Seth, S. (2019). Human Activity Recognition in Smart Home Environment Using OS-WSVM Model. In: Hajji, B., Tina, G.M., Ghoumid, K., Rabhi, A., Mellit, A. (eds) Proceedings of the 1st International Conference on Electronic Engineering and Renewable Energy. ICEERE 2018. Lecture Notes in Electrical Engineering, vol 519. Springer, Singapore. https://doi.org/10.1007/978-981-13-1405-6_15
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DOI: https://doi.org/10.1007/978-981-13-1405-6_15
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