Authors : Jangam Dhanush; Dr. M. Asha Rani

Volume/Issue : Volume 10 - 2025, Issue 9 - September

Google Scholar : https://tinyurl.com/59yrdjsm

Scribd : https://tinyurl.com/3jxsn5bd

DOI : https://doi.org/10.38124/ijisrt/25sep394

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Abstract : Falls are a major safety risk for older adults and individuals with reduced mobility, making prompt detection essential to reduce the likelihood of serious outcomes. This paper presents a real-time fall detection system built around two ESP32-WROOM-32 microcontroller units (MCUs) arranged in a sender–receiver configuration. An MPU6050 inertial measurement unit (IMU) is connected to the sender via the I2C protocol to obtain motion data, which is subsequently transmitted using the ESP-NOW protocol. The receiver processes this data to perform activity inference using a pre- deployed Multilayer Perceptron (MLP) model trained and tested in Edge Impulse. Detection of a fall triggers the automatic dispatch of an SMTP email notification to caregivers. A testing accuracy of 82.53% demonstrates the system’s viability for autonomous, cloud-independent, and resource-efficient wearable health monitoring.

Keywords : Fall Detection, Wearable Health Monitoring, ESP32-WROOM-32, MPU6050, ESP-NOW, Multilayer Perceptron (MLP).

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