An IoT-Integrated Multi-Sensor Framework for Continuous Vital Monitoring and Fall Detection

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Authors: M Madhumitha, Lakshmi Priya S, Satheesh R
Cited by: 0

Abstract

This paper introduces a compact, real-time health monitoring system that integrates IoT technology to continuously measure heart rate, SpO₂ (blood oxygen saturation), body temperature, and also detect falls. The design incorporates a MAX30100 pulse oximeter, a thermistor-based temperature sensor, and a three-axis accelerometer, all connected to an ESP32 controller. The data collected is processed locally and sent to the Arduino IoT Cloud for remote monitoring and alert notifications. To minimize noise from ambient temperature fluctuations and variations in sensor-skin contact, a two-step thermal calibration procedure is employed. Tests show that the system provides stable signal acquisition and dependable fall detection capabilities, making it suitable for elderly care, personal health monitoring, and tracking high-risk patients. The prototype developed offers an affordable and scalable option compared to traditional medical monitoring systems.

Fields of Study & Subjects

Non-Invasive Vital Sign Monitoring Context-Aware Activity Recognition Systems Wireless Sensor Networks for Data Analysis

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
5 February 2026
Language
EN