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@article{196454,
        author = {Pradeepa G and Sowmiya G and Thirisha C and Kiruthika M S},
        title = {Human Activity Monitoring And Assistance System With Digital Twin},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {3846-3849},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=196454},
        abstract = {Human activity monitoring plays a vital role in ensuring the safety, health, and well-being of individuals, especially elderly people and patients who require continuous supervision. This project proposes a real-time human activity monitoring and assistance system designed to detect and analyze human activities using sensor-based technology and IoT communication. The system utilizes a wearable BNO055 sensor to capture real-time motion and orientation data, which is processed using an ESP32 microcontroller for efficient activity recognition. Activities such as walking, sitting, running, and falling are identified using predefined algorithms and data processing techniques. Additionally, an IoT-based communication module enables real-time data transmission and remote monitoring through a web dashboard. In case of abnormal activities like fall detection, the system triggers instant alerts via notifications or email, ensuring timely assistance. The proposed system is cost-effective, reliable, and suitable for real-world healthcare applications. By integrating embedded systems, sensor technology, and IoT, the system enhances safety, enables continuous monitoring, and provides immediate response during critical situations.},
        keywords = {Human Activity Monitoring, Activity Recognition, ESP32 Microcontroller, BNO055 Sensor, IoT-Based System, Real-Time Monitoring, Fall Detection, Embedded Systems, Sensor Data Processing, Wireless Communication, Smart Healthcare System, Emergency Alert System},
        month = {April},
        }