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@article{197101,
        author = {Keerthana P T and Anupriya P and Radhika D and Diddi Sreekavya},
        title = {AI - BASED SMART HELMET FOR WORKER SAFETY},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {4625-4630},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=197101},
        abstract = {Industrial mining environments present significant hazards to worker health and safety, necessitating proactive monitoring solutions. This research presents the design and implementation of an AI-integrated Smart Helmet developed to provide real-time tracking of both physiological and environmental parameters. The system utilizes a NodeMCU (ESP8266) microcontroller to interface with a MAX30100 pulse oximeter for monitoring heart rate and SpO_2 levels, alongside DHT11 and MQ-series sensors for detecting ambient temperature, humidity, and hazardous gas concentrations. To address physical safety, an MPU6050 inertial measurement unit is employed to detect fall directions and impact anomalies through edge-based processing. Data is transmitted via IoT connectivity to a centralized IP-based server for continuous surveillance and historical analysis. The system is programmed to trigger autonomous buzzer alarms and web-based alerts when safety thresholds are breached, ensuring immediate emergency response. Experimental results validate the system’s ability to reduce latency in hazard detection and enhance PPE compliance. This smart helmet offers a scalable, cost-effective framework for safety management in underground mining and other high-risk industrial sectors.},
        keywords = {Smart Helmet, NodeMCU, IoT, Worker Safety, Fall Detection, MAX30100, Environmental Monitoring.},
        month = {April},
        }