The study explores the application of smart glasses and smartphones to study human behavior. Through ensemble and deep learning methodologies, the study seeks to autonomously scrutinize data from each device to improve accuracy and resilience in activity identification. The methodology adopted entails the utilization of distinct models for data derived from smartphones and smart glasses, as opposed to amalgamating attributes, to acquire distinctive insights into user activities. The study outcomes demonstrated promising results, showcasing elevated precision in activity recognition across various machine learning models. Comparative analyses with prior research work reveal enhancements in algorithmic efficacy.