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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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Smart Wearable for Vital Tracking and Alerts

Author(s):Ms Saranya S1, Gurpreet Singh2, Sahil Kumar3, Gagandeep Singh4, Digantik Mukherjee5

Affiliation: 1Assistant Professor Department of Computer Science and Engineering Bengaluru, India 2,3,4,5Department of Computer Science and Engineering, New Horizon College of Engineering Bengaluru, India

Page No: 40-48

Volume issue & Publishing Year: Volume 2 Issue 11,Nov-2025

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.17753654

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Abstract:
The increasing demand for ongoing, remote health monitoring for people with chronic illnesses and aging populations calls for a change from passive data collecting to intelligent, proactive systems [1], [2]. Real-time, on-device predictive analytics, reliable fall detection, and an integrated, closed-loop emergency response system that connects users to emergency medical assistance are frequently absent from current commercial wearables. We introduce an integrated wearable system with an ADXL345 accelerometer for fall detection and a MAX30102 sensor for heart rate and SpO2 monitoring, all based on an ESP32 microcontroller. The solution uses an on-device AI model that is lightweight and tuned with TensorFlow Lite to detect anomalies in physiological data in real time. The system reliably detects abnormalities in vital signs and shows great efficacy in differentiating falls from activities of daily living (ADLs). Importantly, it automatically retrieves the user’s GPS coordinates and uses the Google Maps API to find local medical institutions, achieving an end-to-end emergency alert latency of less than 5 seconds. Real-time, on-device predictive analytics, reliable fall detection, and an integrated, closed-loop emergency response system that connects users to emergency medical assistance are freq By bridging the crucial gap between health anomaly detection and practical emergency intervention, our work offers a low-latency, energy-efficient, privacy-preserving approach that improves patient safety and autonomy

Keywords: Internet of Things (IoT), Wearable Sensors, Health Monitoring, Anomaly Detection, Fall Detection, Edge AI, TensorFlow Lite, Emergency Response System

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