ADVANCED ENSEMBLE CLASSIFICATION MODEL FOR HUMAN PHYSIOLOGICAL CONDITION PREDICTION - Thangapriya; Goldena, Nancy Jasmine (ShodhKosh: Journal of Visual and Performing Arts, 2024)
Human Activity Recognition (HAR) is an essential area of research, often approached through time series classification. Traditional HAR studies focus on basic behaviors, such as walking, sitting, and running. In contrast, this work seeks to predict more complex human physiological states—emotional, mental, physical, and neutral—based on sensor data obtained from healthcare devices, including ECG, TEB, and EDA. We employed three classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and k-Nearest Neighbors (k-NN)—to predict these conditions, with SVM and k-NN yielding the most accurate results. To enhance accuracy, an Optimized Ensemble Classifier (OEC) combining SVM and k-NN is proposed, achieving a 93% accuracy rate.