Mining human activity using dimensionality reduction and pattern recognition

Human activity recognition (HAR) is an emerging research topic in pattern recognition, especially in computer vision. The main objective of human activity recognition is to automatically detect and analyze human activities from the information acquired from different sensors. Human activity prediction using big data remains a challengingly open problem. Several approaches have recently been developed in order to find practical ways to solve high dimensionality of data problems. The aim of this study is to attempt, using data mining techniques, to deal with HAR modeling involving a significant number of variables in order to identify relevant parameters from data and thus to maximize the classification accuracy while minimizing the number of features. The proposed framework has 1032 Ismail El Moudden et al. been tested on a publicly HAR available dataset and the results have been interpreted and discussed.

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