IoT Enabled Patient Monitoring System with Fall Detection

Title

IoT Enabled Patient Monitoring System with Fall Detection

Creator

Ganachar, Sanjana R; Sharon Roji Priya, C.

Description

The growth in demand for remote and prolonged healthcare monitoring has led to a strong growth in adoption of wearable technologies and Internet of Things -based solutions. These technologies are meant to help solve real-time health supervision challenges, particularly for older adults and those suffering from long-term conditions, through reduced need for constant onsite medical care. Here, we describe the design and deployment of an intelligent health monitoring system that utilizes low-cost sensors and wireless communication to enable real-time, continuous monitoring of important physiological and environmental parameters. The suggested system combines an array of sensors: the DHT11 sensor to measure environmental temperature and humidity, the MAX30102 sensor for heart rate and SpO2 monitoring in real-time, and the MPU6050 sensor to sense body posture, orientation, and motion. The sensors are connected to an ESP8266 Wi-Fi MCU, which serves as the hub node for data sensing and transmission. Sensor data that is aggregated is processed locally and then sent to a cloud-based platform for analysis, storage, and visualization. To improve the functionality of the system, the system utilizes a cloud-hosted rule-based Artificial Intelligence (AI) engine for interpreting physiological patterns, identifying signs of abnormal health conditions at an early stage, and offering context-aware, personalized health suggestions. The platform provides dual output modes for added accessibility and reliability: an OLED display for local feedback, and an offdevice cloud dashboard for caregivers and health workers to view patients in real time. This study demonstrates the potential for the integration of embedded systems, cloud computing, and light AI methods to make predictive health analytics and facilitate proactive healthcare interventions possible. Through emphasizing modular design, low power, and scalability, the system is particularly suitable for deployment in elderly care, post-surgery recovery, and chronic disease management. Experimental assessments suggest that the system offers a credible, cost-efficient alternative. 2025 IEEE.

Source

Proceedings of 2025 6th International Conference on Communication, Computing and Industry 6.0, C2I6 2025;

Date

01-01-2025

Publisher

Institute of Electrical and Electronics Engineers Inc.

Subject

chronic disease management; cloud computing; DHT11; elderly care; ESP8266; Internet of Things (IoT); MAX30102; MPU6050; real-time health analytics; remote patient monitoring; rule-based AI; wearable health monitoring

Coverage

Ganachar S.R., Christ University, School of Engineering and Technology, Department of CSE, Bengaluru, India; Sharon Roji Priya C., Christ University, School of Engineering and Technology, Department of CSE, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISBN: 979-833150240-9;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1109/C2I666499.2025.11366952

https://www.scopus.com/pages/publications/105034385519?origin=resultslist