Optimized Human Activity Recognition Using ANOVA-Driven CatBoost Modeling

Abstract

Human Activity Recognition (HAR) has become a pivotal area in domains such as healthcare, sports, and human-computer interaction, requiring advanced models capable of processing intricate datasets. This study introduces an innovative framework that integrates CatBoost, a gradient-boosting decision tree algorithm, with ANOVA-based feature selection to enhance both accuracy and computational efficiency in HAR systems. CatBoost's distinctive ability to manage categorical features and mitigate overfitting makes it ideal for handling the diverse and high-dimensional data characteristic of HAR tasks. The algorithm’s ordered boosting mechanism and effective handling of missing values ensure robust performance in dynamic scenarios. In conjunction, the ANOVA (Analysis of Variance) feature selection technique systematically identifies statistically significant features, reducing dimensionality, diminishing noise, and improving interpretability, thereby optimizing model inputs.

The proposed approach employs ANOVA to refine the feature space, supplying optimized datasets to CatBoost for training and prediction. Experimental evaluations conducted on benchmark HAR datasets reveal substantial improvements in classification accuracy and computational efficiency over traditional methods. The synergy between CatBoost and ANOVA accelerates decision-making while maintaining adaptability to variations in human activity patterns. This research underscores the promise of combining advanced machine learning algorithms with statistical feature selection techniques, fostering the development of accessible, accurate, and efficient HAR systems. Future work will focus on extending this framework to real-time applications, further expanding its applicability across diverse environments. The synergy between CatBoost and ANOVA not only accelerates the decision-making process but also ensures that the model remains adaptable to variations in human activity patterns. This research highlights the potential of combining advanced machine learning algorithms with statistical feature selection techniques, paving the way for more accessible, accurate, and efficient HAR systems. Future work aims to expand this framework to real-time applications, further enhancing its utility across diverse environments.