Health Monitoring System Using Wireless Sensor Network

Cílem této práce je návrh a vývoj přenosného prototypu elektrokardiografu (EKG), který umožňuje zachytit biologický signál a bezdrátově jej přenést do chytrého zařízení - přes Bluetooth, pro následnou analýzu a vizualizaci v mobilní aplikace. Tento prototyp pracuje se senzorem AD620AN, což je přístrojový zesilovač, který se používá k zesílení velmi slabých signálů, jako jsou biologické signály. Kromě toho byla prostřednictvím Android Studio vyvinuta mobilní aplikace, která přijímá EKG signál přenášený prototypem. Měření byla provedena u 25 účastníků, následně byla získaná data vyhodnocena a analyzována a vrcholy R byly detekovány pomocí programovacího prostředí MATLAB verze R2021b. Rozsah experimentální části byl však omezen z důvodu hygienických opatření uložených šířením viru SARS-CoV-2 (COVID-19).The aim of this work is focused on the design and development of a portable prototype of electrocardiograph (ECG) that allows capturing the biological signal and transferring it to a smart device wirelessly - via Bluetooth, for subsequent analysis and visualization from a mobile application. This prototype works with an AD620AN sensor, which is an instrumentation amplifier which is used for the treatment of very weak signals, such as biological signals. In addition, a mobile application was developed through Android Studio, which receives the ECG signal transmitted by the prototype. Measurements were made to 25 participants, subsequently the acquired data were evaluated and analysed, and the R waves were detected using the MATLAB programming environment of R2021b version. However, the scope of the experimental part was limited due to the sanitary measures imposed by the spread of the SARS-CoV-2 virus (COVID-19)

The Application of Computer Techniques to ECG Interpretation

This book presents some of the latest available information on automated ECG analysis written by many of the leading researchers in the field. It contains a historical introduction, an outline of the latest international standards for signal processing and communications and then an exciting variety of studies on electrophysiological modelling, ECG Imaging, artificial intelligence applied to resting and ambulatory ECGs, body surface mapping, big data in ECG based prediction, enhanced reliability of patient monitoring, and atrial abnormalities on the ECG. It provides an extremely valuable contribution to the field

Integrating Various Sensor Readings from MySignals into a Standalone Mobile Health App

This project integrates various health parameters Temperature, Heart Rate, Pulse Rate, Blood Pressure, Respiration Rate, ECG, EMG, GSR, Spirometry values, Snore, Blood Sugar, Body Positions and Weight measured through wired and wireless sensors, into a standalone mobile health app. It shows Health Status of the user based on these parameters, and displays the values in different colors for normal and abnormal readings. Users can plot graphs of their selected parameters to enhance their understanding and how one parameter can affect the other. The project also presents a deeper analysis of Glucose and Heart Rate by calculating Glucose Variability and Heart Rate Variability. These analyses give patients and doctors more insightful information about their health and may act as a guidance to decide a better treatment regimen.It can also count the number of steps of patients for monitoring their activity and send SMS to patients and caregivers if the Health Status is abnormal

Design for energy-efficient and reliable fog-assisted healthcare IoT systems

Cardiovascular disease and diabetes are two of the most dangerous diseases as they are the leading causes of death in all ages. Unfortunately, they cannot be completely cured with the current knowledge and existing technologies. However, they can be effectively managed by applying methods of continuous health monitoring. Nonetheless, it is difficult to achieve a high quality of healthcare with the current health monitoring systems which often have several limitations such as non-mobility support, energy inefficiency, and an insufficiency of advanced services. Therefore, this thesis presents a Fog computing approach focusing on four main tracks, and proposes it as a solution to the existing limitations. In the first track, the main goal is to introduce Fog computing and Fog services into remote health monitoring systems in order to enhance the quality of healthcare. In the second track, a Fog approach providing mobility support in a real-time health monitoring IoT system is proposed. The handover mechanism run by Fog-assisted smart gateways helps to maintain the connection between sensor nodes and the gateways with a minimized latency. Results show that the handover latency of the proposed Fog approach is 10%-50% less than other state-of-the-art mobility support approaches. In the third track, the designs of four energy-efficient health monitoring IoT systems are discussed and developed. Each energy-efficient system and its sensor nodes are designed to serve a specific purpose such as glucose monitoring, ECG monitoring, or fall detection; with the exception of the fourth system which is an advanced and combined system for simultaneously monitoring many diseases such as diabetes and cardiovascular disease. Results show that these sensor nodes can continuously work, depending on the application, up to 70-155 hours when using a 1000 mAh lithium battery. The fourth track mentioned above, provides a Fog-assisted remote health monitoring IoT system for diabetic patients with cardiovascular disease. Via several proposed algorithms such as QT interval extraction, activity status categorization, and fall detection algorithms, the system can process data and detect abnormalities in real-time. Results show that the proposed system using Fog services is a promising approach for improving the treatment of diabetic patients with cardiovascular disease

Wiki-health: from quantified self to self-understanding

Today, healthcare providers are experiencing explosive growth in data, and medical imaging represents a significant portion of that data. Meanwhile, the pervasive use of mobile phones and the rising adoption of sensing devices, enabling people to collect data independently at any time or place is leading to a torrent of sensor data. The scale and richness of the sensor data currently being collected and analysed is rapidly growing. The key challenges that we will be facing are how to effectively manage and make use of this abundance of easily-generated and diverse health data. This thesis investigates the challenges posed by the explosive growth of available healthcare data and proposes a number of potential solutions to the problem. As a result, a big data service platform, named Wiki-Health, is presented to provide a unified solution for collecting, storing, tagging, retrieving, searching and analysing personal health sensor data. Additionally, it allows users to reuse and remix data, along with analysis results and analysis models, to make health-related knowledge discovery more available to individual users on a massive scale. To tackle the challenge of efficiently managing the high volume and diversity of big data, Wiki-Health introduces a hybrid data storage approach capable of storing structured, semi-structured and unstructured sensor data and sensor metadata separately. A multi-tier cloud storage system—CACSS has been developed and serves as a component for the Wiki-Health platform, allowing it to manage the storage of unstructured data and semi-structured data, such as medical imaging files. CACSS has enabled comprehensive features such as global data de-duplication, performance-awareness and data caching services. The design of such a hybrid approach allows Wiki-Health to potentially handle heterogeneous formats of sensor data. To evaluate the proposed approach, we have developed an ECG-based health monitoring service and a virtual sensing service on top of the Wiki-Health platform. The two services demonstrate the feasibility and potential of using the Wiki-Health framework to enable better utilisation and comprehension of the vast amounts of sensor data available from different sources, and both show significant potential for real-world applications.Open Acces

Precordial Bipolar Leads for Mobile ECG Applications

Advances in measurement technology and wireless signal transfer have enabled the design of new, smaller and portable—even plaster-like—electrocardiographic (ECG) measurement devices that enable patient monitoring at home or in emergency situations. The development of new, miniaturized biomedical sensors has opened up possibilities for their application, but also set new demands on signal analysis and interpretation. In particular, the new small wireless systems often utilize bipolar electrodes that have a shorter interelectrode distance (IED) and different electrode locations from those of the standard 12-lead system. This affects the quality and the information content of the signal. The general objective of this thesis was to evaluate the performance of short IED precordial bipolar ECG leads and to determine their optimal location. This thesis adopted three methods to assess the properties of new bipolar precordial ECG leads: modeling, body surface potential map (BSPM) data, and exercise ECG data. First, two realistic, three-dimensional (3D) thorax models and lead field analysis were used to evaluate whether modeling of the measuring sensitivity of ECG leads could be used as a tool for designing new ECG leads. Second, BSPM data was used to study whether short-distance bipolar leads (IED approximately 6 cm) provide an ECG signal that is adequate for clinical utilization. Third, BSPM data was used to define where a bipolar ECG lead should be located in order to maximize the ECG signal strength within healthy subjects. Finally, the value of bipolar leads for diagnosing two major cardiac conditions—left ventricular hypertrophy (LVH) and coronary artery disease (CAD)—was assessed. It was found that the modeled measuring sensitivity corresponds to the changes in actual ECG signal strength, so modeling can be useful, especially in cases where in vivo measurements are impossible such as in designing implantable applications. Based on ECG data from 236 healthy subjects, all studied bipolar ECG leads with a short IED (approximately 6 cm) provided a detectable signal when compared to a low noise level of 15 μV and considering the P-wave as the smallest parameter. The optimal location of the bipolar lead was diagonally near the chest electrodes of the standard precordial leads V2, V3, and V4 (to maximize QRS amplitude), or above the chest electrodes of leads V1 and V2 (to maximize P-wave amplitude). In the selected clinical applications, LVH and CAD, the performance of bipolar leads was surprisingly good. In differentiating LVH (n=305) and healthy subjects (n=236), the performance of a correctly positioned small bipolar lead was similar to that of the traditional Sokolow-Lyon method. When differentiating CAD (n=255) patients from non-CAD (n=126) or low-likelihood of CAD (n=198) subjects, the overall performance of bipolar lead CM5 corresponded to that of standard lead V5. These results indicate that short IED bipolar leads provide a signal that is adequate for clinical use. Furthermore, the performance of these leads was shown to be similar or even superior to that of the commonly used standard leads. It can be concluded that when correctly positioned, short IED bipolar leads are useful and can give additional value for clinical diagnostics. These results provide promising information on the applicability and potential of short IED bipolar ECG leads, and demonstrate that they are worth developing further

Event-driven Middleware for Body and Ambient Sensor Applications

Continuing development of on-body and ambient sensors has led to a vast increase in sensor-based assistance and monitoring solutions. A growing range of modular sensors, and the necessity of running multiple applications on the sensor information, has led to an equally extensive increase in efforts for system development. In this work, we present an event-driven middleware for on-body and ambient sensor networks allowing multiple applications to define information types of their interest in a publish/subscribe manner. Incoming sensor data is hereby transformed into the required data representation which lifts the burden of adapting the application with respect to the connected sensors off the developer's shoulders. Furthermore, an unsupervised on-the-fly reloading of transformation rules from a remote server allows the system's adaptation to future applications and sensors at run-time as well as reducing the number of connected sensors. Open communication channels distribute sensor information to all interested applications. In addition to that, application-specific event channels are introduced that provide tailor-made information retrieval as well as control over the dissemination of critical information. The system is evaluated based on an Android implementation with transformation rules implemented as OSGi bundles that are retrieved from a remote web server. Evaluation shows a low impact of running the middleware and the transformation rules on a phone and highlights the reduced energy consumption by having fewer sensors serving multiple applications. It also points out the behavior and limits of the open and application-specific event channels with respect to CPU utilization, delivery ratio, and memory usage. In addition to the middleware approach, four (preventive) health care applications are presented. They take advantage of the mediation between sensors and applications and highlight the system's capabilities. By connecting body sensors for monitoring physical and physiological parameters as well as ambient sensors for retrieving information about user presence and interactions with the environment, full-fledged health monitoring examples for monitoring a user throughout the day are presented. Vital parameters are gathered from commercially available biosensors and the mediator device running both the middleware and the application is an off-the-shelf smart phone. For gaining information about a user's physical activity, custom-built body and ambient sensors are presented and deployed