Medical data processing and analysis for remote health and activities monitoring

Recent developments in sensor technology, wearable computing, Internet of Things (IoT), and wireless communication have given rise to research in ubiquitous healthcare and remote monitoring of human\u2019s health and activities. Health monitoring systems involve processing and analysis of data retrieved from smartphones, smart watches, smart bracelets, as well as various sensors and wearable devices. Such systems enable continuous monitoring of patients psychological and health conditions by sensing and transmitting measurements such as heart rate, electrocardiogram, body temperature, respiratory rate, chest sounds, or blood pressure. Pervasive healthcare, as a relevant application domain in this context, aims at revolutionizing the delivery of medical services through a medical assistive environment and facilitates the independent living of patients. In this chapter, we discuss (1) data collection, fusion, ownership and privacy issues; (2) models, technologies and solutions for medical data processing and analysis; (3) big medical data analytics for remote health monitoring; (4) research challenges and opportunities in medical data analytics; (5) examples of case studies and practical solutions

A smartphone-based Teleradiology system

The development of a teleradiology application for remote monitoring and processing of patient image data using 2nd generation mobile devices with enhanced network services, is of extreme interest, especially when the final means of display is a smartphone, a very light and compact handheld device. In the following paper the development of applications, that are responsible for remote monitoring and processing of medical images, is investigated

A Survey of Different IoMT Protocols for Healthcare Applications

The increasing use of wireless technologies in healthcare has provided new opportunities for remote patient monitoring, medical device communication, and electronic health record management. However, choosing the appropriate wireless technology for healthcare applications can be challenging due to their unique advantages and limitations. In this context, the following study explores the applications and limitations of various wireless technologies used in healthcare, including BLE, Zigbee, Wi-Fi, Cellular, LoRaWAN, NB-IoT, and Thread. BLE is commonly used for wireless data transfer from medical devices, remote patient monitoring, and location tracking. Zigbee is used for remote patient monitoring, medical device communication, and home health monitoring. Wi-Fi is used for remote patient monitoring, telemedicine, and electronic health record management. Cellular technology is used for remote patient monitoring, telemedicine, and emergency response. LoRaWAN is used for remote patient monitoring, asset tracking, and environmental monitoring. NB-IoT is used for remote patient monitoring and medical device communication. Thread is used for remote patient monitoring, asset tracking, and environmental monitoring. The study reveals that each wireless technology has its own unique advantages and limitations. For example, BLE has a limited range of up to 10 meters and limited bandwidth, while Zigbee has a range of up to 100 meters and limited bandwidth. Wi-Fi has high power consumption, which may not be suitable for battery-operated medical devices, while Cellular technology also has high power consumption and limited coverage in certain areas. LoRaWAN has limited bandwidth, and NB-IoT coverage may be limited in certain areas. Thread has a limited range and limited bandwidth. Our study recommend that healthcare providers should consider the range, bandwidth, power consumption, and reliability of communication to ensure that the chosen wireless technology meets the requirements of their application

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

Review of sensors for remote patient monitoring

Remote patient monitoring (RPM) of physiological measurements can provide an efficient method and high quality care to patients. The physiological signals measurement is the initial and the most important factor in RPM. This paper discusses the characteristics of the most popular sensors, which are used to obtain vital clinical signals in prevalent RPM systems. The sensors discussed in this paper are used to measure ECG, heart sound, pulse rate, oxygen saturation, blood pressure and respiration rate, which are treated as the most important vital data in patient monitoring and medical examination

Compressed television transmission: A market survey

NASA's compressed television transmission technology is described, and its potential market is considered; a market that encompasses teleconferencing, remote medical diagnosis, patient monitoring, transit station surveillance, as well as traffic management and control. In addition, current and potential television transmission systems and their costs and potential manufacturers are considered

A Lightweight Blockchain and Fog-enabled Secure Remote Patient Monitoring System

IoT has enabled the rapid growth of smart remote healthcare applications. These IoT-based remote healthcare applications deliver fast and preventive medical services to patients at risk or with chronic diseases. However, ensuring data security and patient privacy while exchanging sensitive medical data among medical IoT devices is still a significant concern in remote healthcare applications. Altered or corrupted medical data may cause wrong treatment and create grave health issues for patients. Moreover, current remote medical applications' efficiency and response time need to be addressed and improved. Considering the need for secure and efficient patient care, this paper proposes a lightweight Blockchain-based and Fog-enabled remote patient monitoring system that provides a high level of security and efficient response time. Simulation results and security analysis show that the proposed lightweight blockchain architecture fits the resource-constrained IoT devices well and is secure against attacks. Moreover, the augmentation of Fog computing improved the responsiveness of the remote patient monitoring system by 40%.Comment: 32 pages, 13 figures, 5 tables, accepted by Elsevier "Internet of Things; Engineering Cyber Physical Human Systems" journal on January 9, 202

Internet-based training of coronary artery patients: the Heart Cycle Trial

© 2016, Springer Japan. Low adherence to cardiac rehabilitation (CR) might be improved by remote monitoring systems that can be used to motivate and supervise patients and tailor CR safely and effectively to their needs. The main objective of this study was to evaluate the feasibility of a smartphone-guided training system (GEX) and whether it could improve exercise capacity compared to CR delivered by conventional methods for patients with coronary artery disease (CAD). A prospective, randomized, international, multi-center study comparing CR delivered by conventional means (CG) or by remote monitoring (IG) using a new training steering/feedback tool (GEx System). This consisted of a sensor monitoring breathing rate and the electrocardiogram that transmitted information on training intensity, arrhythmias and adherence to training prescriptions, wirelessly via the internet, to a medical team that provided feedback and adjusted training prescriptions. Exercise capacity was evaluated prior to and 6 months after intervention. 118 patients (58 ± 10 years, 105 men) with CAD referred for CR were randomized (IG: n = 55, CG: n = 63). However, 15 patients (27 %) in the IG and 18 (29 %) in the CG withdrew participation and technical problems prevented a further 21 patients (38 %) in the IG from participating. No training-related complications occurred. For those who completed the study, peak VO 2 improved more (p = 0.005) in the IG (1.76 ± 4.1 ml/min/kg) compared to CG (−0.4 ± 2.7 ml/min/kg). A newly designed system for home-based CR appears feasible, safe and improves exercise capacity compared to national CR. Technical problems reflected the complexity of applying remote monitoring solutions at an international level

Physiology-Aware Rural Ambulance Routing

In emergency patient transport from rural medical facility to center tertiary hospital, real-time monitoring of the patient in the ambulance by a physician expert at the tertiary center is crucial. While telemetry healthcare services using mobile networks may enable remote real-time monitoring of transported patients, physiologic measures and tracking are at least as important and requires the existence of high-fidelity communication coverage. However, the wireless networks along the roads especially in rural areas can range from 4G to low-speed 2G, some parts with communication breakage. From a patient care perspective, transport during critical illness can make route selection patient state dependent. Prompt decisions with the relative advantage of a longer more secure bandwidth route versus a shorter, more rapid transport route but with less secure bandwidth must be made. The trade-off between route selection and the quality of wireless communication is an important optimization problem which unfortunately has remained unaddressed by prior work. In this paper, we propose a novel physiology-aware route scheduling approach for emergency ambulance transport of rural patients with acute, high risk diseases in need of continuous remote monitoring. We mathematically model the problem into an NP-hard graph theory problem, and approximate a solution based on a trade-off between communication coverage and shortest path. We profile communication along two major routes in a large rural hospital settings in Illinois, and use the traces to manifest the concept. Further, we design our algorithms and run preliminary experiments for scalability analysis. We believe that our scheduling techniques can become a compelling aid that enables an always-connected remote monitoring system in emergency patient transfer scenarios aimed to prevent morbidity and mortality with early diagnosis treatment.Comment: 6 pages, The Fifth IEEE International Conference on Healthcare Informatics (ICHI 2017), Park City, Utah, 201