Human Activity Recognition with Smartphones using Machine Learning Algorithm

Human Activity Recognition with Smartphones (HAR) leverages the ubiquitous nature of smartphones equipped with sensor technology to identify and classify daily human activities from the generated data. This paper explores the field of HAR, focusing on the use of smartphone sensors like magnetometers, gyroscopes, and accelerometers to capture movement patterns. This provides a comprehensive exploration of HAR, delving into methodologies such as feature extraction data acquisition, and the application of various machine learning algorithms for activity classification. The discussion encompasses challenges and limitations intrinsic to smartphone-based HAR, addressing issues like data variability and device heterogeneity. Promising research directions are highlighted, with an emphasis on context awareness, multi sensor fusion, and deep learning approaches, aiming to enhance the robustness and accuracy of activity recognition systems. The abstract concludes by emphasizing the potential applications of HAR in healthcare, where it could revolutionize patient monitoring, fitness monitoring, optimizing routines, and personalized user experiences, showeasing its role in shaping the future of health-related technologies. This model contributes valuable insights into the dynamic field of HAR, considering the synergy between smartphones, sensor technologies, and machine learning algorithms to propel innovative applications and personalized user experiences.