The Design and Implementation of Intelligent Labor Contraction Monitoring System based on Wearable Internet of Things

In current clinical practice, pregnant women who have entered 37 weeks cannot correctly judge whether they are in labor based on their subjective feelings. Wrong judgment of labor contraction can lead to adverse pregnancy outcomes and endanger the safety of mothers and babies. It will also increase the healthcare pressure in the hospital and the healthcare efficiency is reduced. Therefore, it is very meaningful to be able to design a system for monitoring labor contraction based on objective data to assist pregnant women who have entered 37 weeks in deciding the suitable time to go to hospital. For the above requirements, this thesis designs and implements an intelligent labor contraction monitoring system based on wearable Internet of Things. The system combines the Internet of Things technology, wearable technology and machine learning technology to collect contraction data through wearable sensing device. It uses the Long Short-Term Memory (LSTM) neural network to classify and identify the collected contraction data and realize real-time processing. It improves the accuracy of model recognition to 93.75%. And the recognition results are fed back to the WeChat applet so that pregnant women can view them in real time. The prototype of the wearable sensing device has been integrated by 3D printing and the proof-of-concept system has been demonstrated. Pregnant women can use this system to detect the contraction status and view the contractions in real time through the WeChat applet results. They can judge whether it is suitable for labor, and this system assists in making decisions about the best time to go to hospital

Immunochromatographic diagnostic test analysis using Google Glass.

We demonstrate a Google Glass-based rapid diagnostic test (RDT) reader platform capable of qualitative and quantitative measurements of various lateral flow immunochromatographic assays and similar biomedical diagnostics tests. Using a custom-written Glass application and without any external hardware attachments, one or more RDTs labeled with Quick Response (QR) code identifiers are simultaneously imaged using the built-in camera of the Google Glass that is based on a hands-free and voice-controlled interface and digitally transmitted to a server for digital processing. The acquired JPEG images are automatically processed to locate all the RDTs and, for each RDT, to produce a quantitative diagnostic result, which is returned to the Google Glass (i.e., the user) and also stored on a central server along with the RDT image, QR code, and other related information (e.g., demographic data). The same server also provides a dynamic spatiotemporal map and real-time statistics for uploaded RDT results accessible through Internet browsers. We tested this Google Glass-based diagnostic platform using qualitative (i.e., yes/no) human immunodeficiency virus (HIV) and quantitative prostate-specific antigen (PSA) tests. For the quantitative RDTs, we measured activated tests at various concentrations ranging from 0 to 200 ng/mL for free and total PSA. This wearable RDT reader platform running on Google Glass combines a hands-free sensing and image capture interface with powerful servers running our custom image processing codes, and it can be quite useful for real-time spatiotemporal tracking of various diseases and personal medical conditions, providing a valuable tool for epidemiology and mobile health

Healthcare Monitoring System

The proposed model enables users to improve health related risks and reduce healthcare costs by collecting, recording, analyzing and sharing large data streams in real time and efficiently. In a hospital health care monitoring system it is necessary to constantly monitor the patient’s physiological parameters. For example a pregnant woman parameters such as blood pressure (BP) and heart rate of the woman and heart rate and movements of fetal to control their health condition. The idea of this project came so to reduce the headache of patient to visit to doctor every time he need to check his blood pressure, heart beat rate, temperature etc. With the help of this proposal the time of both patients and doctors are saved and doctors can also help in emergency scenario as much as possible. This system can detect the abnormal conditions, issue an alarm to the patient and send a information to the physician. The proposed outcome of the project is to give proper and efficient medical services to patients by connecting and collecting data information through health status monitors which would include patient’s heart rate, blood pressure and sends an emergency alert to patient’s doctor with his current status and full medical information

A Smart Remote Monitoring System for Prenatal Care in Rural Areas

The complications in maternity especially the women lives in rural sector can be reduced through regular monitoring of their vitals like blood pressure, SpO2 and fetal growth. The internet of things (IoT) is the modern technology bridges the gap between the traditional clinical setting with its consumers as well promotes the telemedicine industry into great levels of accessing proactive healthcare facilities. The predominant aim of this work is to bring a remote monitoring device which assesses the significant health indicators of the pregnant women and their fetus status cost effectively. In order to build such kit, the biosensors like heart rate, SpO2, pressure, temperature and load cell which gives the weight of the fetus are integrated into Arudino board. The sensor readings are processed through ThingSpeak. The timely medical attention is proposed upon observing abnormal physiological vitals of the women which is implemented through a buzzer system in this device. Like such devices in realism help to predict the pregnancy risk and decrease the mortality rate

Antepartum Fetal Monitoring through a Wearable System and a Mobile Application

Prenatal monitoring of Fetal Heart Rate (FHR) is crucial for the prevention of fetal pathologies and unfavorable deliveries. However, the most commonly used Cardiotocographic exam can be performed only in hospital-like structures and requires the supervision of expert personnel. For this reason, a wearable system able to continuously monitor FHR would be a noticeable step towards a personalized and remote pregnancy care. Thanks to textile electrodes, miniaturized electronics, and smart devices like smartphones and tablets, we developed a wearable integrated system for everyday fetal monitoring during the last weeks of pregnancy. Pregnant women at home can use it without the need for any external support by clinicians. The transmission of FHR to a specialized medical center allows its remote analysis, exploiting advanced algorithms running on high-performance hardware able to obtain the best classification of the fetal condition. The system has been tested on a limited set of pregnant women whose fetal electrocardiogram recordings were acquired and classified, yielding an overall score for both accuracy and sensitivity over 90%. This novel approach can open a new perspective on the continuous monitoring of fetus development by enhancing the performance of regular examinations, making treatments really personalized, and reducing hospitalization or ambulatory visits. Keywords: tele-monitoring; wearable devices; fetal heart rate; telemedicin

ECG Signal Reconstruction on the IoT-Gateway and Efficacy of Compressive Sensing Under Real-time Constraints

Remote health monitoring is becoming indispensable, though, Internet of Things (IoTs)-based solutions have many implementation challenges, including energy consumption at the sensing node, and delay and instability due to cloud computing. Compressive sensing (CS) has been explored as a method to extend the battery lifetime of medical wearable devices. However, it is usually associated with computational complexity at the decoding end, increasing the latency of the system. Meanwhile, mobile processors are becoming computationally stronger and more efficient. Heterogeneous multicore platforms (HMPs) offer a local processing solution that can alleviate the limitations of remote signal processing. This paper demonstrates the real-time performance of compressed ECG reconstruction on ARM's big.LITTLE HMP and the advantages they provide as the primary processing unit of the IoT architecture. It also investigates the efficacy of CS in minimizing power consumption of a wearable device under real-time and hardware constraints. Results show that both the orthogonal matching pursuit and subspace pursuit reconstruction algorithms can be executed on the platform in real time and yield optimum performance on a single A15 core at minimum frequency. The CS extends the battery life of wearable medical devices up to 15.4% considering ECGs suitable for wellness applications and up to 6.6% for clinical grade ECGs. Energy consumption at the gateway is largely due to an active internet connection; hence, processing the signals locally both mitigates system's latency and improves gateway's battery life. Many remote health solutions can benefit from an architecture centered around the use of HMPs, a step toward better remote health monitoring systems.Peer reviewedFinal Published versio

Bionic for Training: Smart Framework Design for Multisensor Mechatronic Platform Validation

: Home monitoring supports the continuous improvement of the therapy by sharing data with healthcare professionals. It is required when life-threatening events can still occur after hospital discharge such as neonatal apnea. However, multiple sources of external noise could affect data quality and/or increase the misdetection rate. In this study, we developed a mechatronic platform for sensor characterizations and a framework to manage data in the context of neonatal apnea. The platform can simulate the movement of the abdomen in different plausible newborn positions by merging data acquired simultaneously from three-axis accelerometers and infrared sensors. We simulated nine apnea conditions combining three different linear displacements and body postures in the presence of self-generated external noise, showing how it is possible to reduce errors near to zero in phenomena detection. Finally, the development of a smart 8Ws-based software and a customizable mobile application were proposed to facilitate data management and interpretation, classifying the alerts to guarantee the correct information sharing without specialized skills