Abstract
Human Activity Recognition (HAR) is a challenging research topic in tracking a person’s state of motion and interaction with the surroundings. HAR plays an important role in developing many applications helping improve quality of life. Applications based on HAR could be used in checking the state of health, identifying a mobile phone’s context, keeping track of user’s physical activities, etc. In this research, we applied Recursive Feature Elimination based on Linear Discrimination Analysis (RFELDA) to (http://topepo.github.io/caret/rfe.html#rfe) reduce the dimensionality of dataset before applying classification algorithms to assign subject’s activities. The experiment results on dataset showed that RFELDA improved performance and reduced processor time better than original dataset did.
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Nguyen, L.T. (2017). Enhanced Human Activity Recognition on Smartphone by Using Linear Discrimination Analysis Recursive Feature Elimination Algorithm. In: Cong Vinh, P., Tuan Anh, L., Loan, N., Vongdoiwang Siricharoen, W. (eds) Context-Aware Systems and Applications. ICCASA 2016. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 193. Springer, Cham. https://doi.org/10.1007/978-3-319-56357-2_8
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DOI: https://doi.org/10.1007/978-3-319-56357-2_8
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