Refereed article in conference proceedings (A4)

IoT-Based Healthcare System for Real-Time Maternal Stress Monitoring




List of AuthorsOlugbenga Oti, Iman Azimi, Arman Anzanpour, Amir M. Rahmani, Anna Axelin, Pasi Liljeberg

EditorsKewei Sha

Conference nameIEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies

Publication year2018

Book title *2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)

Start page57

End page62

Number of pages6

ISBN978-1-5386-7207-5

eISBN978-1-5386-7206-8

DOIhttp://dx.doi.org/10.1145/3278576.3278596

URLhttps://ieeexplore.ieee.org/document/8648673

Self-archived copy’s web addresshttps://research.utu.fi/converis/portal/detail/Publication/39386026


Abstract

Excessive stress during pregnancy could cause adverse effects for the mother and her unborn baby, disrupting the normal maternal adaptation throughout pregnancy. Such conditions could be tackled to some degree via traditional clinical techniques, although an automated healthcare system is required for providing a continuous stress management system. Internet of Things (IoT) systems are promising alternatives for such real-time stress monitoring. In conventional IoT-based stress monitoring, stress-related data is collected, and the stress level is determined using a pre-defined model. However, these systems are insufficient for pregnant women whose physiological data are changing over the course of their pregnancy. Therefore, an adaptive monitoring system is needed to estimate stress levels, considering the maternal adaptation such as heart rate elevation in pregnancy. In this paper, we propose a stress-level estimation algorithm based on heart rate and heart rate variations during pregnancy. The algorithm is distributed in an edge-enabled IoT system. We test the performance of our algorithm using supervised and unsupervised learning via an unlabelled set of data from a 7-month monitoring. The monitoring was fulfilled for 20 pregnant women using wearable smart wristbands. Our results show a 97.9% accuracy with 10-fold cross validation using Random Forests.


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Last updated on 2022-07-04 at 17:15