Key technologies in IOT based fetal movement monitoring and pregnant women’s health assessments
Fetal movement perceived by mothers is one important index of fetal well-being. Absence of maternal perception on fetal movements is one symptom of fetal death, and a reduction in fetal movements is an alarming sign of fetal compromise. In clinical practice, a mother is often requested to count the fetal movements by herself. However, the maternal counting is not easy to perform because it is a long-term monitoring (> 1 hour) and a subjective and uncertain procedure due to her personal habits and customs, family factors and time availability. A pregnant woman usually has no time to perform the material counting or she can not be fully concentrated on it. In practice, for a specific individual, fetal movements are quite personalized and different from any others. It is difficult for a pregnant woman to subjectively obtain the accurate number of fetal movements. In this situation, development of an automatic system extracting rules on fetal movements in a specific time interval is the most efficient method for detecting fetal well-being. This system should be accurate, robust, durable and fully integrated into the mother’s living and working environment without causing any damage and discomfort.
In the frame of IOTFetMov, we propose to develop an original automatic system for real time detection of fetal movements and evaluation of pregnant woman’s state of health. This system will be fully integrated into an intelligent garment such as a T-shirt of tight style, which is capable of maintaining direct and close contact with the pregnant woman’s abdomen without causing any discomfort. The garment will integrate a number of multi-scale sensors in order to acquire signals of fetal movements. However, in practice, these signals are usually mixed with false signals caused by mother’s body movements, breath and hiccup. The proposed system will then perform the preprocessing and classification of these signals in order to filter noises, set up a mathematical model characterizing the relation between the measures and fetus’s position, and extract from the model the rules describing fetal movements for a specific time interval. In this way, a basic diagnosis on state of fetal well-being can be realized.
Moreover, the proposed system will be linked with a cloud computing platform for providing advanced diagnosis and consultations from medical experts in order to take actions before suffering irreversible harm. In the same time, the classical sensors of the intelligent garment can also be used to monitor the pregnant woman’s physiological indices, including skin temperature, sweat quantity and heart rate, and perform a general evaluation on her psychological and health states. According to these data, a remote personalized medical consultation will be provided to the concerned pregnant woman.
The proposed Internet Of Things (IOT) based system will permit to realize the concept of e-health home care. It can be used by a large population of pregnant women for their daily health evaluation without causing important impacts on their work and life. It will be composed of two main components: the monitoring unit and general health evaluation unit. The monitoring unit will include: 1) the basic structure of the software and hardware system, 2) a set of tiny low power consumption multi-scale and single-chip sensors, 3) the signal processing unit for data fusion, signal filtering and relevant information extraction, 4) the mathematical model, permitting to extract rules from recorded fetal movements and predict the fetal health state, 5) the intelligent garment integrating the sensors monitoring fetal movements and pregnant women’s psychological and health state. The general health evaluation unit will include 1) an IOT-based and cloud computing system for online advanced diagnosis, 2) a specialized knowledge base for remote medical consultations.
Period : 2014 – 2018 +
Keywords : IOT – Fetal movement detection – intelligent garment – sensors – mathematical model
Website : IotFetMov
Partners : CIC-IT 1403 INSERM CIC-IT 1403 – CHRU de Lille, GEMTEX Laboratoire Génie et Matériaux Textiles, SEI School of Electronics and Information, SIoT School of Internet of Things Engineering
Ensait contact : Xianyi ZENG