Permutation importance based modified guided regularized random forest in human activity recognition with smartphone

Human Activity Recognition (HAR) is a burgeoning field of study due to its real-life applications in the medical field, the e-health system, and elder care or care of physically impaired people in a smart healthcare environment. Using sensors built into wearable devices, such as smartphones, HAR provides an opportunity to identify human behavior and better understand an individual's health. Improving the classification performance of human activities is an academic and industrial focus. Feature selection can affect classification performance: redundant and irrelevant features increase the learning difficulty of the classification model, cause overfitting, reduce classification performance, decrease interpretability, and reduce generalizability. Many preceding studies showed the defectiveness of feature selection results, which causes difficulties for professionals in a variety of fields (e.g., medical practitioners) to analyze and interpret the obtained feature subsets. Random Forest (RF) based feature selection methods select more interpretable features than other methods. However, RF-based feature selection methods are highly biased. Herein, we propose a novel RF-based feature selection method, namely modified Guided Regularized Random Forest (mGRRF), using permutation importance to overcome this. To prove the effectiveness of the proposed feature selection method, we conduct experiments using a public standard HAR dataset. Five classifiers, such as random forest, k-nearest neighbors, logistic regression, support vector machine, and xgboost, are used to recognize human activities after selecting the relevant and vital features using mGRRF. Experimental results indicate that with mGRRF-based features, the recognition accuracy is generally improved to 98% or 7% better than when all the extracted statistical features are used.