Research Article
Smart Labor Pain Management: An IoT-Based Approach for Expecting Mothers
@INPROCEEDINGS{10.4108/eai.28-4-2025.2357934, author={B. Sowmya and Gopinath D}, title={Smart Labor Pain Management: An IoT-Based Approach for Expecting Mothers}, proceedings={Proceedings of the 4th International Conference on Information Technology, Civil Innovation, Science, and Management, ICITSM 2025, 28-29 April 2025, Tiruchengode, Tamil Nadu, India, Part I}, publisher={EAI}, proceedings_a={ICITSM PART I}, year={2025}, month={10}, keywords={iot labour pain real-time observation wearable sensor maternal healthcare pain management psi}, doi={10.4108/eai.28-4-2025.2357934} }- B. Sowmya
Gopinath D
Year: 2025
Smart Labor Pain Management: An IoT-Based Approach for Expecting Mothers
ICITSM PART I
EAI
DOI: 10.4108/eai.28-4-2025.2357934
Abstract
Labor pain is a serious physiological event that needs continuous surveillance and prompt medical intervention to guarantee the safety and health of both the mother and child. Conventional labor monitoring systems tend to be based on manual observations and delayed responses, which can lead to less-than-optimal care, particularly in rural or low-resource environments. This study presents a smart labor pain management system based on Internet of Things (IoT) technology for real-time, continuous monitoring and individualized pain management assistance. The system combines wearable sensors to monitor vital parameters like heart rate, temperature, oxygen saturation, and uterine contraction pressure. Processing is done through an ESP32 microcontroller and wireless data transmission through MQTT to a Firebase cloud backend. A mobile app is the user interface, providing live visualization of data, emergency notification, remote consultation, and interactive pain relief aids. An analytics engine that is intelligent calculates a Pain Severity Index (PSI) and gives personalized suggestions based on real-time data and user feedback. Experimental outcomes of the simulated and actual tests showed 90% reduced response time, enhanced detection rates, and maximum user satisfaction versus conventional approaches. The system outlined here is shown to be efficient, scalable, and patient-focussed solution that enhances labour pain management as well as evidence-based medical decision-making.
