Alexandros Pantelopoulos

A Survey on Wearable Sensor-Based Systems for Health Monitoring and Prognosis

The design and development of wearable biosensor systems for health monitoring has garnered lots of attention in the scientific community and the industry during the last years. Mainly motivated by increasing healthcare costs and propelled by recent technological advances in miniature biosensing devices, smart textiles, microelectronics, and wireless communications, the continuous advance of wearable sensor-based systems will potentially transform the future of healthcare by enabling proactive personal health management and ubiquitous monitoring of a patients health condition. These systems can comprise various types of small physiological sensors, transmission modules and processing capabilities, and can thus facilitate low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper attempts to comprehensively review the current research and development on wearable biosensor systems for health monitoring. A variety of system implementations are compared in an approach to identify the technological shortcomings of the current state-of-the-art in wearable biosensor solutions. An emphasis is given to multiparameter physiological sensing system designs, providing reliable vital signs measurements and incorporating real-time decision support for early detection of symptoms or context awareness. In order to evaluate the maturity level of the top current achievements in wearable health-monitoring systems, a set of significant features, that best describe the functionality and the characteristics of the systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for researchers and developers in this scientific area and to provide direction for future research improvements.

A survey on wearable biosensor systems for health monitoring

Wearable biosensor systems for health monitoring are an emerging trend and are expected to enable proactive personal health management and better treatment of various medical conditions. These systems, comprising various types of small physiological sensors, transmission modules and processing capabilities, promise to change the future of health care, by providing low-cost wearable unobtrusive solutions for continuous all-day and any-place health, mental and activity status monitoring. This paper presents a comprehensive survey on the research and development done so far on wearable biosensor systems for health-monitoring, by comparing a variety of current system implementations and approaches and identifying their technological shortcomings. A set of significant features, that best describe the functionality and the characteristics of wearable biosensor systems, has been selected to derive a thorough study. The aim of this survey is not to criticize, but to serve as a reference for current achievements and their maturity level and to provide direction for future research improvements.

DERMA/Care: An Advanced image-Processing Mobile Application for Monitoring Skin Cancer

This paper describes a mobile hardware/software system (DERMA/care) to help with screening of skin cancer (melanomas). Our system uses an inexpensive apparatus (microscope) and a smart phone (iPhone). These two components standalone are sufficient to capture highly detailed images for use by experts with medical background. However the novelty of our system lies in the fact that we further improved the efficiency of the system by implementing an advanced image-processing framework to detect suspicious areas and help with skin cancer prevention. Our main goal was to demonstrate how smart phones could turn into powerful and intelligent machines and help large populations without expertise in low-resource settings.

Design of the New Prognosis Wearable System-Prototype for Health Monitoring of People at Risk

The paper presents the design framework of the Prognosis wearable system, which aims at realizing continuous and ubiquitous health monitoring of people at risk along with providing embedded decision support to enhance disease management and/or prevention. The system’s functional scheme is built on top of a formal language for describing and fusing health symptoms that are extracted from the acquired measurements of a variety of wearable sensors, which may be distributed over a patient’s body. Moreover, by incorporating an automated intelligent and interactive dialogue system, additional health-status feedback can be obtained from the user in terms of described symptoms, captured using a voice recognition module, which can further enhance the autonomous decisional capabilities of the system. A simulation framework built in Java is also presented in detail and is based on a previously presented Stochastic Petri Net functional model of the system, which was used to model the event-based operation of Prognosis and to capture the concurrency issues that arise in such a system design. Finally, the illustrated paradigmatic application scenarios clearly highlight the benefits of using an interactive and intelligent health monitoring system for remote care of patients in critical medical conditions.

A formal language approach for multi-sensor Wearable Health-Monitoring Systems

Wearable health-monitoring systems (WHMS) promise to revolutionize health care by providing real-time unobtrusive monitoring of patientspsila physiological parameters through the deployment of several on-body and even intra-body biosensors. Although several technological issues regarding WHMS still need to be resolved, in order for them to become more applicable in real-life scenarios, it is expected that continuous ambulatory monitoring of vital signs will enable pro-active personal health management and better treatment of patients suffering from chronic diseases, of the elderly population and of emergency situations. In this paper a novel formal language based model for multi-sensor data fusion and early-detection of various conditions is presented. Patterns or even signal states indicating pathological symptoms that are presented in the signals, which can be collected from on-body distributed biosensors, are modeled as symbols of the Prognosis context-free formal language, whose grammar and production rules define the prognosis-words. The proposed approach is based on a described generic WHMS model and on a simple but at the same time efficient method for characterizing body-signalpsilas patterns and/or states. Finally, we provide several illustrative examples for better comprehension of the proposed model.

Efficient single-lead ECG beat classification using Matching Pursuit based features and an Artificial Neural Network

In this paper we employ the Matching Pursuit algorithm in order to obtain compact time-frequency representations of ECG data, which are then utilized from an ANN to achieve beat classification. To obtain optimum performance, the effect of the following attributes on the classification performance is examined: number of atoms, type of wavelet and number of ECG samples around the R peak. Our goal is to derive an accurate, efficient and real-time beat classification scheme, which could then be implemented on a resource-constrained portable device such as a cell phone. The proposed scheme is based on an existing beat classification method, but has the following favorable attributes: it utilizes less features, a single ECG lead and also only a single MLP in order to be able to discriminate between various abnormal beats. The performance of our approach is evaluated using the MIT-BIH Arrhythmia database. Provided results illustrate the accuracy of the proposed method (98.7%), which together with its simplicity (a single linear transform is required for feature extraction) justify its use for real-time classification of abnormal heartbeats on a portable heart monitoring system.

SPN-model based simulation of a wearable health monitoring system

The deployment of Wearable Health Monitoring Systems (WHMS) can potentially enable ubiquitous and continuous monitoring of a patients physiological parameters. Moreover by incorporating multiple biosensors in such a system a comprehensive estimation of the users health condition can possibly be derived. In this paper we present a Stochastic Petri Net (SPN) model of a multi-sensor WHMS along with a corresponding simulation framework implemented in Java. The proposed model is built on top of a previously published multisensor data fusion strategy, which has been expanded in this work to take into account synchronization issues and temporal dependencies between the measured bio-signals.

A Health Prognosis Wearable System with Learning Capabilities Using NNs

The deployment of Wearable Health Monitoring Systems (WHMS) is expected to address several important healthcare-related issues such as increasing healthcare costs, the rising number of the elderly population and treatment of chronic conditions. However, most of the currently developed WHMS simply serve as ambulatory physiological data loggers and transmitters in order to make the recorded bio-signals remotely available for inspection from a supervising physician. In this paper we describe our efforts towards setting-up a WHMS prototype that is capable of providing individualized embedded decision/diagnosis support for round-the-clock remote health monitoring of people at risk. To realize this goal an ANN-based approach is adopted, whereby a supervised learning period is required in order to embed patient-specific medical knowledge into the system, which will then enable it to make more accurate and “safer” estimations about the user’s health condition.

ECG Beat Classification Using Optimal Projections in Overcomplete Dictionaries

Wearable health monitoring systems (WHMS) enable ubiquitous and unobtrusive monitoring of a variety of vital signs that can be measured non-invasively. These systems have the potential to revolutionize healthcare delivery by achieving early detection of critical health changes and thus possibly even disease or hazardous event prevention. Amongst the patient populations that can greatly benefit from WHMS are Congestive Heart Failure (CHF) patients. For CHF management the detection of heart arrhythmias is of crucial importance. However, since WHMS have limited computing and storage resources, diagnostic algorithms need to be computationally inexpensive. Towards this goal, we investigate in this paper the efficiency of the Matching algorithm in deriving compact time-frequency representations of ECG data, which can then be utilized from an Artificial Neural Network (ANN) to achieve beat classification. In order to select the most appropriate decomposition structure, we examine the effect of the type of dictionary utilized (stationary wavelets, cosine packets, wavelet packets) in deriving optimal features for classfication. Our results show that by applying a greedy algorithm to determine the dictionary atoms that show the greatest correlation with the ECG morphologies, an accurate, efficient and real-time beat classification scheme can be derived. Such an algorithm can then be inexpensively run on a resource-constrained portable device such as a cell phone or even directly on a smaller microcontroller-based board. The performance of our approach is evaluated using the MIT-BIH Arrhythmia database. Provided results illustrate the accuracy of the proposed method (94.9%), which together with its simplicity (a single linear transform is required for feature extraction) justify its use for real-time classification of abnormal heartbeats on a portable heart monitoring system.

A wireless modular multi-modal multi-node patch platform for robust biosignal monitoring

In this paper a wireless modular, multi-modal, multi-node patch platform is described. The platform comprises low-cost semi-disposable patch design aiming at unobtrusive ambulatory monitoring of multiple physiological parameters. Owing to its modular design it can be interfaced with various low-power RF communication and data storage technologies, while the data fusion of multi-modal and multi-node features facilitates measurement of several biosignals from multiple on-body locations for robust feature extraction. Preliminary results of the patch platform are presented which illustrate the capability to extract respiration rate from three different independent metrics, which combined together can give a more robust estimate of the actual respiratory rate.