Authors

G.Yeswanthi

UG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, India

I. Rajshekar

Assistant Professor, Dept., of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.), Andhra Pradesh, India

S.Vyshnavi

UG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, India

P.Snehalatha

UG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, India

P.Thanuja

UG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, India

T.M.Vasim Akram

UG Student, Dept. of E.C.E., Gates Institute of Technology, Gooty, Anantapur (Dist.) Andhra Pradesh, India

Abstract

Paralysis patients face significant challenges in mobility and communication, making continuous health monitoring essential for their safety and well-being. This project presents an AI-driven real-time paralysis patient monitoring system using Raspberry Pi-5, designed to track health parameters, detect emergencies, and enable assistive communication. The system integrates vital sign sensors, including the DHT11 sensor for temperature and humidity monitoring and a vibration sensor to detect movements or sudden impacts, ensuring early fall detection. A camera module analyse facial expressions and eye-blink detection, allowing non-verbal communication through an AI module. An LED indicator provides real-time status feedback, signaling normal conditions, warnings, or critical alerts. The system employs machine learning models using TensorFlow and OpenCV to analyse distress levels based on facial cues and motion patterns. If abnormal health conditions are detected, an AI-powered alert mechanism triggers a buzzer and instantly sends notifications via the GPRS, SMS, or email, ensuring caregivers receive real-time updates. An SD card stores patient health logs, enabling long-term tracking and analysis. By reducing dependency on caregivers and automating real-time health tracking, this system enhances patient safety and independence. Its affordability, intelligent automation, and AI-powered analytics make it a breakthrough in healthcare technology, offering a scalable and effective solution for paralysis patient monitoring. 

Keywords

AI-driven monitoring paralysis patients Raspberry Pi 5 real-time health tracking assistive communication DHT11 sensor vibration sensor facial expression analysis eye-blink detection machine learning TensorFlow OpenCV emergency alerts GPRS SMS notifications buzzer alarm SD card logging patient safety intelligent automation healthcare technology

Citation of this Article

G.Yeswanthi, I. Rajshekar, S.Vyshnavi, P.Snehalatha, P.Thanuja, & T.M.Vasim Akram. (2025). AI-Enabled Smart Monitoring System for Paralysis Patient Using Raspberry Pi-5. International Current Journal of Engineering and Science - ICJES, 4(3), 43-59. Article DOI: https://doi.org/10.47001/ICJES/2025.403006

Licence Copyright (c) 2026 International Current Journal of Engineering and Science. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

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