@article{marques_iot-based_2021, title = {{IoT}-{Based} {Smart} {Health} {System} for {Ambulatory} {Maternal} and {Fetal} {Monitoring}}, volume = {8}, issn = {2327-4662}, doi = {10.1109/JIOT.2020.3037759}, abstract = {The adoption of IoT for smart health applications is a relevant tool for distributed and intelligent automatic diagnostic systems. This work proposes the development of an integrated solution to monitor maternal and fetal signals for high-risk pregnancies based on IoT sensors, feature extraction based on data analytics, and an intelligent diagnostic aid system based on a 1-D convolutional neural network (CNN) classifier. The fetal heart rate and a group of maternal clinical indicators, such as the uterine tonus activity, blood pressure, heart rate, temperature, and oxygen saturation are monitored. Multiple data sources generate a significant amount of data in different formats and rates. An emergency diagnostic subsystem is proposed based on a fog computing layer and the best accuracy was 92.59\% for both maternal and fetal emergency. A smart health analytics system is proposed for multiple feature extraction and the calculation of linear and nonlinear measures. Finally, a classification technique is proposed as a prediction system for maternal, fetal, and simultaneous health status classification, considering six possible outputs. Different classifiers are evaluated and a proposed CNN presented the best results, with the F1-score ranging from 0.74 to 0.91. The results are validated based on the diagnosis provided by two specialists. The results show that the proposed system is a viable solution for maternal and fetal ambulatory monitoring based on IoT.}, number = {23}, journal = {IEEE Internet of Things Journal}, author = {Marques, João Alexandre Lobo and Han, Tao and Wu, Wanqing and Madeiro, João Paulo do Vale and Neto, Aloísio Vieira Lira and Gravina, Raffaele and Fortino, Giancarlo and de Albuquerque, Victor Hugo C.}, month = dec, year = {2021}, note = {9 citations (Crossref) [2022-09-21] Conference Name: IEEE Internet of Things Journal}, keywords = {Artificial intelligence (AI), Biomedical monitoring, Cloud computing, Feature extraction, Fetal heart rate, Internet of Things, Medical diagnostic imaging, Monitoring, convolutional neural networks (CNNs), feature extraction, fetal monitoring, health analytics, maternal monitoring}, pages = {16814--16824}, }