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Human Activity Recognition Based on Wavelet-Based Features along with Feature Prioritization

Conference Paper


Abstract


  • Activity recognition from human action data is quite a challenging task in the biomedical data science community. The main challenge in dealing with human activity recognition (HAR) datasets is their high cardinality. Therefore, reducing cardinality is a cardinal area of research in the HAR field. In this research, reducing the data dimensionality by utilizing future selection methods has been used. This research work has extracted features using wavelet packet transform (WPT) and the cardinality of the feature set has been reduced by using the Genetic Algorithm (GA) technique. The selected features also have been ranked according to their importance based on their SHAP values. In the venture, an interesting inspection has been found. That is in HAR datasets, signal values lay into lower frequency regions mostly. The highest accuracy and f1-score which have been got are 94.74%, 94.73%, and 89.98%, 89.67% for the feature extracted and feature selected dataset respectively.

Publication Date


  • 2021

Citation


  • Abid, M. H., Nahid, A. A., Islam, M. R., & Parvez Mahmud, M. A. (2021). Human Activity Recognition Based on Wavelet-Based Features along with Feature Prioritization. In 2021 IEEE 6th International Conference on Computing, Communication and Automation, ICCCA 2021 (pp. 933-939). doi:10.1109/ICCCA52192.2021.9666294

Scopus Eid


  • 2-s2.0-85124809754

Web Of Science Accession Number


Start Page


  • 933

End Page


  • 939

Abstract


  • Activity recognition from human action data is quite a challenging task in the biomedical data science community. The main challenge in dealing with human activity recognition (HAR) datasets is their high cardinality. Therefore, reducing cardinality is a cardinal area of research in the HAR field. In this research, reducing the data dimensionality by utilizing future selection methods has been used. This research work has extracted features using wavelet packet transform (WPT) and the cardinality of the feature set has been reduced by using the Genetic Algorithm (GA) technique. The selected features also have been ranked according to their importance based on their SHAP values. In the venture, an interesting inspection has been found. That is in HAR datasets, signal values lay into lower frequency regions mostly. The highest accuracy and f1-score which have been got are 94.74%, 94.73%, and 89.98%, 89.67% for the feature extracted and feature selected dataset respectively.

Publication Date


  • 2021

Citation


  • Abid, M. H., Nahid, A. A., Islam, M. R., & Parvez Mahmud, M. A. (2021). Human Activity Recognition Based on Wavelet-Based Features along with Feature Prioritization. In 2021 IEEE 6th International Conference on Computing, Communication and Automation, ICCCA 2021 (pp. 933-939). doi:10.1109/ICCCA52192.2021.9666294

Scopus Eid


  • 2-s2.0-85124809754

Web Of Science Accession Number


Start Page


  • 933

End Page


  • 939