Key technologies in IOT based fetal movement monitoring and pregnant women’s health assessments – IOTFetMov
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. 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.
Develop an original automatic system for real time detection of fetal movements and evaluation of pregnant woman’s state of health
1) development of a new accurate and robust method for automatically detecting the well-being of fetus and interpreting fetal movements,<br />2) design of a multi-scale fetal monitoring sensor to overcome the difficult that a single sensor cannot distinguish the fetal signal from the false dynamic signals; Furthermore, by combining multi-scale sensing signals, one can also compensate the temperature effect and the environmental effects;<br />3) design of an intelligent garment for detecting fetal movements while considering aesthetic properties and wearer’s comfort in the design of the garment and layout of the embedded sensors,<br />4) development of an intelligent system permitting to integrate the techniques of advanced signal processing, artificial intelligence and decision support into the previous instrumented garment,<br />5) determination of the relations between the physiological signals captured by tiny sensors of the garment and the wearer’s well-being,<br />6) creation of a remote medical expert system by using a computing cloud connected to the intelligent garment so that distant medical experts can deliver consultations about the evolution of pregnancy, <br />7) validation of the developed system in hospitals France and China,<br />8) reinforcement of partnership between French and Chinese teams in order to develop future collaborations in the field of e-health and connected apparel/smart textiles.
1) we will develop a wearable large-range accelerometer for continuous fetal movement monitoring by combining the merits of the capacitive sensing and the piezoresistive sensing.
2) The procedure of signal processing is composed of the signal preprocessing, fetal movement signal extraction, false signal recognition. The preprocessing will be realized by using the wavelet analysis and HHT transform algorithm.
3) A mathematical model will be built for extracting rules on fetal movements and evaluating the health state of fetus. The personalized physiological indices and sensory descriptors of each pregnant woman as well as medical knowledge will be taken as input variables in the procedure of modeling.
4) A mathematical model will be built for characterizing the relation between the pregnant woman’s physiological data, such as heart rate and quantity of sweat, and her health state more particularly in terms of stress and/or anxiety.
5) The garment will permit to integrate the new sensor and classical physiological sensors without bringing about any feeling of discomfort and inconvenience. The type of fibers and garment style will be carefully selected so that the sensors are in close contact with the pregnant woman’s abdomen. We will design a knitted textile structure adapted to the morphology of pregnant women and especially the key zones in order to maintain the best contact between the human body and sensors. We will introduce conductive yarns into the knitted textile structure for connecting the sensors to a microcontroller.
6) The remote medical expert system will be composed of two parts. Basic medical diagnosis will be automatically performed in the microcontroller of the intelligent garment while advanced diagnosis related to complex symptoms will be carried out on the platform through interactions between the pregnant woman and medical experts and with the help of relevant signals emitted from the intelligent garment.
1) The newly developed sensor for fetal movements is validated by the clinical experiments on both sides and signal analysis
2) The proposed intelligent system for fetus and pregnant woman’s well-being evaluation is validated by the clinical experiments on both sides with high rates of correct diagnosis (> 95%)
3) The intelligent garment is validated by the medical experts and patients on both sides through a jointly organized evaluation in terms of accuracy, robustness, durability, human comfort and aesthetics
4) The remote medical expert system is validated by the medical services on both sides through a jointly organized evaluation in terms of efficiency and acceptability for medical experts and patients
5) At least four joint papers are published at international journals
6) At least two patents are applied
1) Creation of a new international cooperation model by organizing multidisciplinary competences of different partners around one application-oriented project, and quickly transforming cooperation results into direct economic and social benefits through experiments of remote international medical consultations.
2) Proposition of a new decentralized e-health model for continuous monitoring and remote diagnosis of fetus and pregnant women’s health and well-being. Under this model, the limited medical resources and patient’s availability can be largely optimized, the cost of medical services can be decreased, and impacts on patient’s work and psychology can be minimized. This model will be particularly beneficial for the large population of Chinese pregnant women. For French medical resources, this system will permit to create new business opportunities in this huge market.
3) Development of a new intelligent garment, permitting to perform basic diagnosis by means of its physiological sensors and automatic communications with the remote medical knowledge base and the cloud computing platform. Compared with classical reactive textiles, the level of “intelligence” of the proposed garment is enhanced. Similar prototypes can be created in other similar sectors, such as risk management, security, human protection and other medical applications.
4) Development of a remote medical expert system by making use of the advantages of cloud computing. Its capacity of data mining will permit to continuously learn new medical rules from successively integrated clinical real data and then improve the performance of the models of well-being for helping to deliver more relevant diagnosis. Medical experts can also use it as a digital library or a recommendation system for finding concerned clinical cases in the past and relevant solutions. The current model of medical organization and management on both sides can be largely changed.
At least four joint papers are published at international journals
At least two patents are applied
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.
Monsieur Xianyi ZENG (Laboratoire Génie et Matériaux Textiles)
The author of this summary is the project coordinator, who is responsible for the content of this summary. The ANR declines any responsibility as for its contents.
SEI School of Electronics and Information
SIoT School of Internet of Things Engineering
CIC-IT 1403 INSERM CIC-IT 1403 - CHRU de Lille
GEMTEX Laboratoire Génie et Matériaux Textiles
Help of the ANR 273,696 euros
Beginning and duration of the scientific project: September 2014 - 48 Months