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@article{207427,
        author = {Samrith Shetty and Sidhanth Naik and Om Patole and Dr. Narendra Bhagat},
        title = {Smart Wheelchair Monitoring System: A Real-Time IoT-Based Health and Safety Assistive Platform},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {3},
        pages = {1075-1092},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207427},
        abstract = {This paper presents the design, implementation, and evaluation of an Internet of Things (IoT)-based Smart Wheelchair Monitoring System designed to enhance the autonomy, safety, and healthcare delivery for individuals with physical and mobility impairments. The proposed assistive platform integrates a comprehensive sensor suite—including the MAX30102 for heart rate and blood oxygen saturation (SpO2) monitoring, an analog Galvanic Skin Response (GSR) stress sensor, and a non-invasive Electrocardiogram (ECG) cardiac monitor—onto an ESP32-C3 microcontroller plat- form. For navigation safety, the system incorporates an MPU-6050 6-axis inertial sensor and a tilt sensor for posture, orientation, and fall detection, alongside ultrasonic sensors for active obstacle avoidance. Telemetry data is prepro- cessed at the edge and securely transmitted via Bluetooth Low Energy (BLE) and the Message Queuing Telemetry Transport (MQTT) protocol to a FastAPI backend. Real-time vital signs and alerts are visualized on a centralized doctor-patient web portal implemented via Streamlit. A supervised deep learning 1D Convolutional Neural Network (1D-CNN) is integrated into the backend to classify ECG signals and detect cardiac arrhythmias. Experimental re- sults validate the real-time tracking, alerting capability, low latency, and low cost of the integrated assistive ecosystem, demonstrating high feasibility for clinical and home care environments},
        keywords = {Internet of Things (IoT), Smart Wheelchair, Health Monitoring, ESP32-C3, MAX30102, ECG Classification, 1D-CNN, Stress Detection, Fall Prevention},
        month = {August},
        }