Abstract
Human Activity Recognition (HAR) is a crucial task in numerous applications, including healthcare, smart homes, security, and fitness tracking. This study explores the effectiveness of Long Short-Term Memory (LSTM) networks in accurately recognizing and classifying human activities from sensor data. Leveraging the ability of LSTM to capture temporal dependencies and long-term patterns, we employ a deep learning approach that processes sequential data collected from accelerometers and gyroscopes. Our proposed model demonstrates significant improvements in recognition accuracy. We validate the performance of our approach on benchmark datasets, achieving an accuracy of over 95%. The findings underscore the potential of LSTM networks in advancing HAR systems, offering reliable and precise activity classification that can be integrated into various real-world applications.