Smart mobile system for pregnancy care using body sensors

Abstract

Hypertensive disorders are the most common problems during pregnancy. They cause about 10% of maternal deaths. The world mortality rate has decreased but many women are still dying every day from pregnancy complications. Various technic resources are being used in an integrated manner in order to minimize even more the death of both mothers and babies. Mobile devices with Internet access have a great potential to expand actions of health professionals. These devices facilitate care with people that are living in remote areas, assisting in patient monitoring. Information exchange anywhere and anytime between experts and patients could be an important way to improve the pregnancy monitoring. This paper presents a mobile monitoring solution using body sensors to identify worsens in the health status of pregnant women suffering hypertensive disorders. This mobile application uses Naïve Bayes classifier to better identify hypertension severity helping experts in decisionmaking process. Results show that the proposed mobile system is promising for monitoring blood pressure disorders in pregnancy.

Long-Term IoT-Based Maternal Monitoring: System Design and Evaluation

“…There is a large body of literature introducing remote monitoring services in pregnancy. Most of these studies use questionnaires to track mother’s health condition [ 32 ] or investigate certain issues or health problems during pregnancy such as hypertension [ 23 , 33 ], preterm birth [ 14 ], gestational diabetes [ 34 ] and sleep disturbances [ 21 ]. Few works have exploited long-term IoT-based health monitoring in pregnancy and postpartum.…”

“…They aimed to reduce preterm birth by collecting uterine contractions through a body sensor and informing women via a mobile application if the collected information was above some personalized thresholds. In [ 23 ], the authors used a smartphone-based system enabled by a Naive Bayes Classifier, performing real-time decision-making.…”

“…These works are also bounded to limited data collection [ 19 , 20 ]. In addition, mobile applications and wearable electronics are utilized to continuously collect health parameters during and after pregnancy, targeting specific pregnancy-related issues such as sleep disturbances, physical activity and hypertension [ 21 , 22 , 23 ].…”

Efficient EER-LEACH Protocol for Monitoring the Activities of Pregnant Women Using Wearable Body Sensor Network

“…We assessed our proposed framework as far as limits false positive rate, CPU and Memory execution, and power utilization. Mário W. L. Moreira, [2016] talked about the utilization of a mobile application to help caregivers in pregnancy checking. This framework gets information from body sensors to quantify the circulatory strain, continuously, and together with information gathered by a health care through a proteinuria testing makes the deduction utilizing Bayesian classifier Naïve Bayes [5].…”

“…Mário W. L. Moreira, [2016] talked about the utilization of a mobile application to help caregivers in pregnancy checking. This framework gets information from body sensors to quantify the circulatory strain, continuously, and together with information gathered by a health care through a proteinuria testing makes the deduction utilizing Bayesian classifier Naïve Bayes [5]. Performance evaluation of this proposed strategy demonstrated that this classifier performed well in forecast with an exactness of 0.8.…”