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@article{170689,
        author = {M.Yashwanthi and M.Blessi and G.Shivaji and G.Bharadhwaj and Mrs.Surekha},
        title = {Human Activity Recognition Using Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2024},
        volume = {11},
        number = {7},
        pages = {1592-1601},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=170689},
        abstract = {The project titled "Human Activity Recognition Using Machine Learning," focuses on developing an intelligent system capable of accurately classifying and recognizing  human activities based on sensor data. Human Activity Recognition (HAR) is crucial for various applications, including healthcare monitoring, fitness tracking, and smart home automation. The team employed machine learning algorithms to analyze and classify different physical activities, such as walking, sitting, standing, and running, using data collected from wearable sensors. A comprehensive data analysis was conducted to preprocess the sensor data, extract relevant features, and optimize the model's performance. The project explored various classification techniques, including decision trees, support vector machines (SVM), and deep learning models, to determine the most effective approach for accurate activity recognition.A key aspect of this project was the emphasis on data analysis, ensuring that the models were trained on high-quality, clean data, which significantly improved the system's accuracy and reliability. The team's efforts resulted in a system that not only accurately recognizes human activities but also provides insights into patterns and trends in physical behavior. This project demonstrates the effectiveness of combining machine learning with thorough data analysis to solve complex real-world problems. The work has potential applications in enhancing user experiences in wearable technology, improving health monitoring systems, and contributing to advancements in ambient intelligence.},
        keywords = {Human Activity Recognition (HAR), Deep Learning, Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Spatiotemporal Data, Video Classification, Sequential Modeling, Hybrid Models, Action Recognition, Temporal Feature Extraction.},
        month = {December},
        }