Human Activity Recognition

Uses smartphone sensor data to classify six daily activities performed by 30 participants. Signals from a waist-mounted Samsung Galaxy S II were equipped with accelerometer and gyroscope sensors processed into 561 features, enabling machine learning models to recognize activities like walking, sitting, and laying. This work supports applications in healthcare, activity tracking, and smart environments