Design and Optimization of an Autonomous, Ambulatory Cardiac Event Monitor

International audienceWearable sensors for health monitoring can enable the early detection of various symptoms, and hence rapid remedial actions may be undertaken. In particular, the monitoring of cardiac events by using such wearable sensors can provide real-time and more relevant diagnosis of cardiac arrhythmias than classical solutions. However, such devices usually use batteries, which require regular recharging to ensure long-term measurements. We therefore designed and evaluated a connected sensor for the ambulatory monitoring of cardiac events, which can be used as an autonomous device without the need of a battery. Even when using off-the-shelf, low-cost integrated circuits, by optimizing both the hardware and software embedded in the device, we were able to reduce the energy consumption of the entire system to below 0.4 mW while measuring and storing the ECG on a non-volatile memory. Moreover, in this paper, a power-management circuit able to store energy collected from the radio communication interface is proposed, able to make the connected sensor fully autonomous. Initial results show that this sensor could be suitable for a truly continuous and long-term monitoring of cardiac events

Autonomous Medical Care for Exploration Class Space Missions

The US-based health care system of the International Space Station (ISS) contains several subsystems, the Health Maintenance System, Environmental Health System and the Countermeasure System. These systems are designed to provide primary, secondary and tertiary medical prevention strategies. The medical system deployed in Low Earth Orbit (LEO) for the ISS is designed to enable a "stabilize and transport" concept of operations. In this paradigm, an ill or injured crewmember would be rapidly evacuated to a definitive medical care facility (DMCF) on Earth, rather than being treated for a protracted period on orbit. The medical requirements of the short (7 day) and long duration (up to 6 months) exploration class missions to the Moon are similar to LEO class missions with the additional 4 to 5 days needed to transport an ill or injured crewmember to a DCMF on Earth. Mars exploration class missions are quite different in that they will significantly delay or prevent the return of an ill or injured crewmember to a DMCF. In addition the limited mass, power and volume afforded to medical care will prevent the mission designers from manifesting the entire capability of terrestrial care. NASA has identified five Levels of Care as part of its approach to medical support of future missions including the Constellation program. In order to implement an effective medical risk mitigation strategy for exploration class missions, modifications to the current suite of space medical systems may be needed, including new Crew Medical Officer training methods, treatment guidelines, diagnostic and therapeutic resources, and improved medical informatics

Healthcare Monitoring Systems: A WBAN Approach for Patient Monitoring

Healthcare Monitoring System, which is expected to reduce healthcare expenses by enabling the continuous monitoring of patient health remotely during their daily activities in healthcare environment. Healthcare applications based on Wireless Sensor Networks are gaining high popularity in all over the world due to their features like flexibility, mobility and ease of constant monitoring of the patient in both outside and inside the body sensed as more useful. The main focus of such system is remote monitoring of patient, inside and outside the hospital room and in ICU in the sense of implantable feature for analysing the patient data. Recent developments in combining sensors, communication systems, and other fields such as cloud computing and Big Data analysis have provided the perfect tools to develop cutting edge systems for improving energy efficiency and consumption with the datasets. Smart homes, smart sensors, and Internet of Things are just a few examples of these application based technologies that will lead to more sustainable and more resilient energy systems. This research work will focus on the Wireless Sensor Networks in terms of emerging wireless technologies which means supporting infrastructure and technology and challenge design issues and as well as security, mobility and energy consumption

Hybrid Nanostructured Textile Bioelectrode for Unobtrusive Health Monitoring

Coronary heart disease, cardiovascular diseases and strokes are the leading causes of mortality in United States of America. Timely point-of-care health diagnostics and therapeutics for person suffering from these diseases can save thousands of lives. However, lack of accessible minimally intrusive health monitoring systems makes timely diagnosis difficult and sometimes impossible. To remedy this problem, a textile based nano-bio-sensor was developed and evaluated in this research. The sensor was made of novel array of vertically standing nanostructures that are conductive nano-fibers projecting from a conductive fabric. These sensor electrodes were tested for the quality of electrical contact that they made with the skin based on the fundamental skin impedance model and electromagnetic theory. The hybrid nanostructured dry electrodes provided large surface area and better contact with skin that improved electrode sensitivity and reduced the effect of changing skin properties, which are the problems usually faced by conventional dry textile electrodes. The dry electrodes can only register strong physiological signals because of high background noise levels, thus limiting the use of existing dry electrodes to heart rate measurement and respiration. Therefore, dry electrode systems cannot be used for recording complete ECG waveform, EEG or measurement of bioimpedance. Because of their improved sensitivity these hybrid nanostructured dry electrodes can be applied to measurement of ECG and bioimpedance with very low baseline noise. These textile based electrodes can be seamlessly integrated into garments of daily use such as vests and bra. In combination with embedded wireless network device that can communicate with smart phone, laptop or GPRS, they can function as wearable wireless health diagnostic systems

Study design of Heart failure Events reduction with Remote Monitoring and eHealth Support (HERMeS)

Aims: The role of non-invasive telemedicine (TM) combining telemonitoring and teleintervention by videoconference (VC) in patients recently admitted due to heart failure (HF) ('vulnerable phase' HF patients) is not well established. The aim of the Heart failure Events reduction with Remote Monitoring and eHealth Support (HERMeS) trial is to assess the impact on clinical outcomes of implementing a TM service based on mobile health (mHealth), which includes remote daily monitoring of biometric data and symptom reporting (telemonitoring) combined with VC structured, nurse-based follow-up (teleintervention). The results will be compared with those of the comprehensive HF usual care (UC) strategy based on face-to-face on-site visits at the vulnerable post-discharge phase. Methods and results: We designed a 24 week nationwide, multicentre, randomized, controlled, open-label, blinded endpoint adjudication trial to assess the effect on cardiovascular (CV) mortality and non-fatal HF events of a TM-based comprehensive management programme, based on mHealth, for patients with chronic HF. Approximately 508 patients with a recent hospital admission due to HF decompensation will be randomized (1:1) to either structured follow-up based on face-to-face appointments (UC group) or the delivery of health care using TM. The primary outcome will be a composite of death from CV causes or non-fatal HF events (first and recurrent) at the end of a 6 month follow-up period. Key secondary endpoints will include components of the primary event analysis, recurrent event analysis, and patient-reported outcomes. Conclusions: The HERMeS trial will assess the efficacy of a TM-based follow-up strategy for real-world 'vulnerable phase' HF patients combining telemonitoring and teleintervention

Wearable and Implantable Wireless Sensor Network Solutions for Healthcare Monitoring

Wireless sensor network (WSN) technologies are considered one of the key research areas in computer science and the healthcare application industries for improving the quality of life. The purpose of this paper is to provide a snapshot of current developments and future direction of research on wearable and implantable body area network systems for continuous monitoring of patients. This paper explains the important role of body sensor networks in medicine to minimize the need for caregivers and help the chronically ill and elderly people live an independent life, besides providing people with quality care. The paper provides several examples of state of the art technology together with the design considerations like unobtrusiveness, scalability, energy efficiency, security and also provides a comprehensive analysis of the various benefits and drawbacks of these systems. Although offering significant benefits, the field of wearable and implantable body sensor networks still faces major challenges and open research problems which are investigated and covered, along with some proposed solutions, in this paper

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions

Detection and prediction problems with applications in personalized health care

The United States health-care system is considered to be unsustainable due to its unbearably high cost. Many of the resources are spent on acute conditions rather than aiming at preventing them. Preventive medicine methods, therefore, are viewed as a potential remedy since they can help reduce the occurrence of acute health episodes. The work in this dissertation tackles two distinct problems related to the prevention of acute disease. Specifically, we consider: (1) early detection of incorrect or abnormal postures of the human body and (2) the prediction of hospitalization due to heart related diseases. The solution to the former problem could be used to prevent people from unexpected injuries or alert caregivers in the event of a fall. The latter study could possibly help improve health outcomes and save considerable costs due to preventable hospitalizations. For body posture detection, we place wireless sensor nodes on different parts of the human body and use the pairwise measurements of signal strength corresponding to all sensor transmitter/receiver pairs to estimate body posture. We develop a composite hypothesis testing approach which uses a Generalized Likelihood Test (GLT) as the decision rule. The GLT distinguishes between a set of probability density function (pdf) families constructed using a custom pdf interpolation technique. The GLT is compared with the simple Likelihood Test and Multiple Support Vector Machines. The measurements from the wireless sensor nodes are highly variable and these methods have different degrees of adaptability to this variability. Besides, these methods also handle multiple observations differently. Our analysis and experimental results suggest that GLT is more accurate and suitable for the problem. For hospitalization prediction, our objective is to explore the possibility of effectively predicting heart-related hospitalizations based on the available medical history of the patients. We extensively explored the ways of extracting information from patients' Electronic Health Records (EHRs) and organizing the information in a uniform way across all patients. We applied various machine learning algorithms including Support Vector Machines, AdaBoost with Trees, and Logistic Regression adapted to the problem at hand. We also developed a new classifier based on a variant of the likelihood ratio test. The new classifier has a classification performance competitive with those more complex alternatives, but has the additional advantage of producing results that are more interpretable. Following this direction of increasing interpretability, which is important in the medical setting, we designed a new method that discovers hidden clusters and, at the same time, makes decisions. This new method introduces an alternating clustering and classification approach with guaranteed convergence and explicit performance bounds. Experimental results with actual EHRs from the Boston Medical Center demonstrate prediction rate of 82% under 30% false alarm rate, which could lead to considerable savings when used in practice