Human Activity Recognition Using Deep Learning

Abstract: Human Activity Recognition (HAR) focuses on the detection and classification of human movements using data collected from various sources, including video and wearable sensors. This paper presents a deep learning-based approach for identifying daily activities such as walking, running, sitting, and standing by utilizing accelerometer and gyroscope sensor data. HAR plays a vital role in domains like healthcare, smart environments, and fitness tracking. The study employs machine learning (ML) and deep learning (DL) models to address the challenges of data variability and real-time processing. As artificial intelligence and pervasive computing continue to evolve, HAR systems are becoming more precise, efficient, and scalable for practical applications.