Comparison of Feature Selection and Classification for Human Activity and Fall Recognition using Smartphone Sensors
Chanvichet Vong, Thitiphoom Theptit, Virinya Watcharakonpipat, Passara Chanchotisatien, Seksan Laitrakun · 2021
Human activity and fall recognition have been applied into many applications, especially, daily activity and healthcare systems. The recognition performance is up to the features used in the classification. Applying all features might degrade the performance since irrelevant features are used. In this paper, we study and compare four filter methods of feature selection: Chi square, mutual information, ANOVA, and Pearson's coefficient. Six supervised classifiers are used to evaluate the classification performance according to the sets of features obtained from these methods. The dataset UniMiB SHAR, which consists of nine basic daily activities and eight types of falls, is investigated in the experiment. As a result, among all of the considered feature-selection methods and classifiers, the XGBoost incorporating with the mutual information method gives the best performance whose accuracy, precision, recall, and F1 scores are 0.9122, 0.8727, 0.8629, and 0.8640, respectively.