Android Application to Detect and Alert Tachycardia and Bradycardia using Pulse Rate Sensor

Heart rate monitoring is most vital in preventing disorders related to heart. Failure to detect heart disorder in early stage may lead to death. The lacking of devices to immediately detect the abnormalities in heart and alert the patients emergency contact lead to this problem. In this report the author propose a system to detect two heart disorders called Tachycardia and Bradycardia which are caused by abnormalities in heart rate. The proposed system will consist of a pulse sensor which will be connected to a smartphone via Bluetooth. The signal information which is processed by the microcontroller will be sent to the mobile phone. An app created will send an alert to the emergency contacts of the patients when Tachycardia or Bradycardia condition has been detected by the sensor. This will increase the possibilities of giving immediate treatment to the patient, and hope to reduce the death rate caused by heart disorder

Biomedical Signal Transceivers

With the growing costs of healthcare, the need for mobile health monitoring devices is critical. A wireless transceiver provides a cost effective way to transmit biomedical signals to the various personal electronic devices, such as computers, cellular devices, and other mobile devices. Different kinds of biomedical signals can be processed and transmitted by these devices, including electroencephalograph (EEG), electrocardiograph (ECG), and electromyography (EMG). By utilizing wireless transmission, the user gains freedom to connect with fewer constraints to their personal devices to view and monitor their health condition. In this chapter, in the first few sections, we will introduce the reader with the basic design of the biomedical transceivers and some of the design issues. In the subsequent sections, we will be presenting design challenges for wireless transceivers, specially using a common wireless protocol Bluetooth. Furthermore, we will share our experience of implementing a biomedical transceiver for ECG signals and processing them. We conclude the discussion with current trends and future work

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

Developing residential wireless sensor networks for ECG healthcare monitoring

Wireless technology development has increased rapidly due to it’s convenience and cost effectiveness compared to wired applications, particularly considering the advantages offered by Wireless Sensor Network (WSN) based applications. Such applications exist in several domains including healthcare, medical, industrial and home automation. In the present study, a home-based wireless ECG monitoring system using Zigbee technology is considered. Such systems can be useful for monitoring people in their own home as well as for periodic monitoring by physicians for appropriate healthcare, allowing people to live in their home for longer. Health monitoring systems can continuously monitor many physiological signals and offer further analysis and interpretation. The characteristics and drawbacks of these systems may affect the wearer’s mobility during monitoring the vital signs. Real-time monitoring systems record, measure, and monitor the heart electrical activity while maintaining the consumer’s comfort. Zigbee devices can offer low-power, small size, and a low-cost suitable solution for monitoring the ECG signal in the home, but such systems are often designed in isolation, with no consideration of existing home control networks and smart home solutions. The present study offers a state of the art review and then introduces the main concepts and contents of the wireless ECG monitoring systems. In addition, models of the ECG signal and the power consumption formulas are highlighted. Challenges and future perspectives are also reported. The paper concludes that such mass-market health monitoring systems will only be prevalent when implemented together with home environmental monitoring and control systems

iCloudECG: A Mobile Cardiac Telemedicine System

With rising healthcare costs and a substantially growing number of patients 65 or over, the benefits of telemedicine and patient self-monitoring systems are becoming increasingly evident. Patients, physicians, hospitals, and even insurance providers benefit from vigilant, cost-effective patient monitoring systems. This thesis describes the development of a portable, smart-phone connected system for continuous cardiac monitoring. The system is capable of continuously monitoring the conditions of the heart, automated detection of cardiac arrhythmias, and real-time notifying patients and physicians of the detected abnormalities. The system consists of four main subsystems: 1) a Bluetooth capable chest-strap ECG, 2) an Android-enabled mobile device, 3) a cloud-based analysis, storage, and notification system, and 4) a web-application portal. Data is collected by the single-lead ECG device, and transmitted to the mobile device via Bluetooth. An application allows the patient to view their ECG output in real-time, view the last 24 hours of recordings, and receive notifications and details regarding any detected abnormalities. The mobile device transmits the ECG data to a remote server for pre-processing and analysis, and then stores the data in a database which the patient or physician can access via a web-interface. The developed system can be used as a telemedicine system for management of cardiovascular diseases

Mobile Personal Health Monitoring for Automated Classification of Electrocardiogram Signals in Elderly

Mobile electrocardiogram (ECG) monitoring is an emerging area that has received increasing attention in recent years, but still real-life validation for elderly residing in low and middle-income countries is scarce. We developed a wearable ECG monitor that is integrated with a self-designed wireless sensor for ECG signal acquisition. It is used with a native purposely designed smartphone application, based on machine learning techniques, for automated classification of captured ECG beats from aged people. When tested on 100 older adults, the monitoring system discriminated normal and abnormal ECG signals with a high degree of accuracy (97%), sensitivity (100%), and specificity (96.6%). With further verification, the system could be useful for detecting cardiac abnormalities in the home environment and contribute to prevention, early diagnosis, and effective treatment of cardiovascular diseases, while keeping costs down and increasing access to healthcare services for older persons

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

Fog Computing in Medical Internet-of-Things: Architecture, Implementation, and Applications

In the era when the market segment of Internet of Things (IoT) tops the chart in various business reports, it is apparently envisioned that the field of medicine expects to gain a large benefit from the explosion of wearables and internet-connected sensors that surround us to acquire and communicate unprecedented data on symptoms, medication, food intake, and daily-life activities impacting one's health and wellness. However, IoT-driven healthcare would have to overcome many barriers, such as: 1) There is an increasing demand for data storage on cloud servers where the analysis of the medical big data becomes increasingly complex, 2) The data, when communicated, are vulnerable to security and privacy issues, 3) The communication of the continuously collected data is not only costly but also energy hungry, 4) Operating and maintaining the sensors directly from the cloud servers are non-trial tasks. This book chapter defined Fog Computing in the context of medical IoT. Conceptually, Fog Computing is a service-oriented intermediate layer in IoT, providing the interfaces between the sensors and cloud servers for facilitating connectivity, data transfer, and queryable local database. The centerpiece of Fog computing is a low-power, intelligent, wireless, embedded computing node that carries out signal conditioning and data analytics on raw data collected from wearables or other medical sensors and offers efficient means to serve telehealth interventions. We implemented and tested an fog computing system using the Intel Edison and Raspberry Pi that allows acquisition, computing, storage and communication of the various medical data such as pathological speech data of individuals with speech disorders, Phonocardiogram (PCG) signal for heart rate estimation, and Electrocardiogram (ECG)-based Q, R, S detection.Comment: 29 pages, 30 figures, 5 tables. Keywords: Big Data, Body Area Network, Body Sensor Network, Edge Computing, Fog Computing, Medical Cyberphysical Systems, Medical Internet-of-Things, Telecare, Tele-treatment, Wearable Devices, Chapter in Handbook of Large-Scale Distributed Computing in Smart Healthcare (2017), Springe

HEALTH MONITORING SYSTEM (HMS) FOR RESCUERS

The main purpose of this project is to develop a system to remotely monitor real time measurement of physiological parameters of rescuers (firefighters, chemical rescuers etc.) who are exposed to hazard during rescue execution to fulfill the need for minimizing risks endangering rescuers’ lives. It helps first-aid work as necessary support will be given once the person who monitor outside the field observes abnormal vital signs. The system consists of health monitoring device, computer and smartphone. The health monitoring device is a new generation of “smart” garments, integrating wearable sensors which will allow monitoring heart rate, breathing rate, skin temperature, posture and activity of the user. Sensors implemented ensure noninvasive measurement method, without interfering into human body. Computer and smartphone are used to communicate with the device’s sensors that capture comprehensive physiological data from user. The acquired measurements are sent wirelessly via Bluetooth, and displayed on a computer or a smartphone. Real-time physiological measurements of rescuers can be observed. This paper will also discuss on the performance of the health monitoring device. The accuracy and reliability of health monitoring is tested. Further recommendations will be given to improve this system

Android Application to Detect and Alert Tachycardia and Bradycardia using Pulse Rate Sensor

Heart rate monitoring is most vital in preventing disorders related to heart. Failure to detect heart disorder in early stage may lead to death. The lacking of devices to immediately detect the abnormalities in heart and alert the patients emergency contact lead to this problem. In this report the author propose a system to detect two heart disorders called Tachycardia and Bradycardia which are caused by abnormalities in heart rate. The proposed system will consist of a pulse sensor which will be connected to a smartphone via Bluetooth. The signal information which is processed by the microcontroller will be sent to the mobile phone. An app created will send an alert to the emergency contacts of the patients when Tachycardia or Bradycardia condition has been detected by the sensor. This will increase the possibilities of giving immediate treatment to the patient, and hope to reduce the death rate caused by heart disorder