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Human Activity Identification Using Novel Feature Extraction and Ensemble-Based Learning for Accuracy

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Artificial Intelligence Methods in Intelligent Algorithms (CSOC 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 985))

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Abstract

The area of human activity recognition is gaining momentum with the rise of smart appliances towards tracking and monitoring human behavior system. Till last decade, there have been various works being carried out towards building such a robust system that has led its way to commercial products too. However, after an in-depth investigation, it was found there is a far way to go in order to build up a true and dependable recognition system. Therefore, the proposed system introduces a novel framework meant for human activity recognition system with the sole target to enhance the precision factor in the identification process. A simplified feature extraction process has been introduced in this work that after being subjected to ensemble-training approach is found to improve the identification performance significantly. The study outcome shows better accuracy as well as good system performance.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, Ballari Institute of Technology and Management, Ballari, Karnataka, India

    Abdul Lateef Haroon P.S & U. Eranna

Authors
  1. Abdul Lateef Haroon P.S
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  2. U. Eranna
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Corresponding author

Correspondence to Abdul Lateef Haroon P.S .

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Editors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlín, Zlín, Czech Republic

    Radek Silhavy

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Lateef Haroon P.S, A., Eranna, U. (2019). Human Activity Identification Using Novel Feature Extraction and Ensemble-Based Learning for Accuracy. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_34

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