Research Article
Enhanced Human Activity Recognition on Smartphone by Using Linear Discrimination Analysis Recursive Feature Elimination Algorithm
@INPROCEEDINGS{10.1007/978-3-319-56357-2_8, author={Loc Nguyen}, title={Enhanced Human Activity Recognition on Smartphone by Using Linear Discrimination Analysis Recursive Feature Elimination Algorithm}, proceedings={Context-Aware Systems and Applications. 5th International Conference, ICCASA 2016, Thu Dau Mot, Vietnam, November 24-25, 2016, Proceedings}, proceedings_a={ICCASA}, year={2017}, month={6}, keywords={Feature Selection Smartphones Human Activity Recognition}, doi={10.1007/978-3-319-56357-2_8} }- Loc Nguyen
Year: 2017
Enhanced Human Activity Recognition on Smartphone by Using Linear Discrimination Analysis Recursive Feature Elimination Algorithm
ICCASA
Springer
DOI: 10.1007/978-3-319-56357-2_8
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 () 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.
