Claudio De Capua

A Smart ECG Measurement System Based on Web-Service-Oriented Architecture for Telemedicine Applications

The opportunity for cardiac patients to have constantly monitored their health state at home is now possible by means of telemedicine applications. In fact, today, portable and simple-to-use devices allow one to get preliminary domestic diagnoses of the heart status. In this paper, the authors present an original ECG measurement system based on web-service-oriented architecture to monitor the heart health of cardiac patients. The projected device is a smart patient-adaptive system able to provide personalized diagnoses by using personal data and clinical history of the monitored patient. In the presence of a pathology occurrence, the system is able to call the emergency service for assistance. An ECG sensor has the task to acquire, condition, and sample the heart electrical impulses, whereas a personal digital assistant (PDA) performs the diagnosis according to the measurement uncertainty and, in case of a critical situation, calls the medical staff. The system has two removable and updatable memory devices: the first memory device stores the clinical and personal data of the patient, and the second memory device stores information on the metrological status of the measurement system. This way, according to the personal data and historical information of the patient, the measurement system adapts itself by selecting the best fitted ECG model as a reference to configure the computing algorithm. Further information on the measurement uncertainty is used to qualify the reliability of the final clinical response to reduce the occurrence of a faulty diagnosis. Through the PDA graphic interface, the user can display his personal data, observe the graph of his ECG signal, and read diagnosis information with the relative reliability level. Moreover, the patient can choose to print his ECG graph through a Bluetooth printer or to send it to a specialist by a General Packet Radio Service (GPRS) modem.

Remote Didactic Laboratory “G. Savastano,” The Italian Experience for E-Learning at the Technical Universities in the Field of Electrical and Electronic Measurement: Architecture and Optimization of the Communication Performance Based on Thin Client Technology

The Remote Didactic Laboratory Laboratorio Didattico Remoto -LA.DI.RE. “G. Savastano” is an e-learning measurement laboratory supported by the Italian Ministry of Education and University. It provides the students of electric and electronic measurement courses with access to remote measurement laboratories, delivering different didactic activities related to measurement experiments. The core of the software architecture is the integration of the Learning Management System (LMS) with the remotely accessible measurement laboratories through web services and thin client paradigm, providing a new approach to remote experiments on measurement instrumentation. The overview of this paper is on the different solutions concerning the thin client technology, and the solution implemented is described. This solution takes into account the delivered services to students and teachers and permits optimization of the communication performances. The results of the comparison among the performances of different implementations of the thin client paradigm highlight the advantages of the adopted solution. As a consequence, the description of the thin client protocol implemented, together with the presentation of the LMS and delivered services given in a previous paper, makes an exhaustive analysis of the software architecture of the LA.DI.RE. “G. Savastano.”

A remote doctor for homecare and medical diagnoses on cardiac patients by an adaptive ECG analysis

Today the telemedicine applications are experiencing rapid growth. The possibility of having portable devices to use comfortably at home for monitoring the health of the patient has involved many research areas. In this paper a remote doctor for homecare and medical diagnoses on cardiac patient is proposed. An original smart system has been projected in order to adapt itself to the patient and develop, in this way, a personalized diagnosis taking into account his personal data and his clinical history; a practical and economical device that can adapt to any type of patient. The system is based on a PDA and an ECG sensor which acquires, at a fixed sampling frequency, the heart electrical impulses. In details, a removable and updatable memory is used to store the clinical and personal patient data, while an additional internal and read-only memory stores information on the metrological status of the measurement system. On PDA display, it is possible to observe the ECG signal graph, the information relating to diagnosis and the measurement uncertainty. The patient also has the opportunity to print the results by a Bluetooth printer. Through build-in models the ECG waveform is analyzed in order to diagnose possible arrhythmias occurrences or the happening of a heart attack according to some parameters like age, sex and physical constitution of the patient, and information on the measurement uncertainty. The embedded information on the system metrological performances and patient data are used to configure the computing algorithm and so to reduce the happening of diagnosis faults.

A smart energy meter for power grids

The growing demand of energy and the need of finding alternative energy sources to the traditional ones, due to the progressive decrease of fossil fuels and an increasing concern towards the environment, have led to a revolution in terms of energy production in the last decade. As a consequence, the distributed generation is more and more widely spreading. The network, in this new dimension, has to change its management and the energy distribution so to achieve and maintain high efficiency requirements. Coming to drop the concept of centralized production, it is immediate to conclude that an efficient distribution of energy must necessarily bring into account the energy footprint of the area, because the energy transport should be always as short as possible, to minimize losses and maximize the efficiency of the network. This concept is the core of the smart-grid idea, on which the global scientific community is investing heavily in research, the idea is a power distribution grid, based on the experience in the information and communications technology field, which can route the energy through appropriate algorithms that are able to determine the optimal path. Of course, behind all this there must be a network structure capable of acquiring detailed data from widespread production and consumption of energy and make them easily available along with additional information, e.g. the Power Quality of the energy exchanged. This information is demanded by simple user, who wants to personally evaluate the functioning of the system, and also by technical personnel, who needs to access to reliable data to perform targeted and efficient interventions. In the present paper, the authors propose a smart energy meter for energy management in power grids. The measurement system has been projected and developed according to the IEEE 1451 (ISO/IEC/IEEE 21451) guidelines. The system is based on a mobile application in order to improve the data exchange and availability.

Ocular Biometric Measurements to Diagnose Neurological Disorders Due to Wilson Disease

Wilson disease is a pathology because of a gene mutation causing malfunction in the copper excretion from the organism. Therefore, copper accumulation in the body gives rise to oxidative processes. Hence, this rare disease causes several disorders affecting tissues and organs. Neurological disorders lead to copper accumulation in the brain. Approximately 95% of individuals with neurological/psychiatric disorders show a visible symptom in their eyes known as Kayser-Fleischer ring. It is a golden-brown, sometimes orange or greyish, ring because of copper deposit in the cornea. In the medical screening, it is considered as a diagnostic sign of Wilson disease more than ever in individuals with neurological problems. The authors propose an innovative technique based on ocular biometric measurements to diagnose the pathology and the origin of a neurological disorder. An image processing algorithm detects the Kayser-Fleischer ring in the eye cornea through segmentation. Subsequently, biometric measurements provide further information on the severity level of pathology. The aim is to provide a non-invasive diagnostic technique to improve the accuracy of the current methods used in practice and to reduce the possible interpretation errors.

An ISO/IEC/IEEE 21451 Compliant Algorithm for Detecting Sensor Faults

Preservation of metrological properties of any measuring and sensing system is an important issue to assure reliable measurements. So, in particular, calibration is the confirmation process used in the practice to verify the metrological properties of any transducer and sensor. Nevertheless, calibration needs specific procedures, measurement instrumentation, and skills. It involves the use of resources and costs to be supported. Therefore, preliminary tests are suggested to define when calibration is needed. In this sight, a transducer or sensor can be smart by verifying its status so to detect possible faults and estimate autonomously the next calibration time. As a result, the development of algorithms for automated fault detection and maintenance is an important target for developers engaged to project smart transducers and sensors. In particular, built-in algorithms can allow the same sensor to check its reliability before performing any measurement. In this paper, we propose an algorithm for detecting sensor faults. The aim is to provide a sensor-friendly algorithm for detecting failures by performing intermediate tests in view of the next calibration. The algorithm has been developed to be in compliance with ISO/IEC/IEEE 21451 standard. In detail, the algorithm allows sensor to estimate its accuracy and the repeatability of measurement so to get information on measurement reliability. Depending on the specific sensor, few points of calibration curve are checked and compared with reference values. If the estimated accuracy or repeatability are below the specification reported in the last calibration data, sensor apprises that a new calibration is required. Based on the general features, the algorithm can be used for any sensor.

A Smart Power Meter to Monitor Energy Flow in Smart Grids: The Role of Advanced Sensing and IoT in the Electric Grid of the Future

This paper aims to describe the role of advanced sensing systems in the electric grid of the future. In detail, the project, development, and experimental validation of a smart power meter are described in the following. The authors provide an outline of the potentialities of the sensing systems and IoT to monitor efficiently the energy flow among nodes of an electric network. The described power meter uses the metrics proposed in the IEEE Standard 1459–2010 to analyze and process voltage and current signals. Information concerning the power consumption and power quality could allow the power grid to route efficiently the energy by means of more suitable decision criteria. The new scenario has changed the way to exchange energy in the grid. Now, energy flow must be able to change its direction according to needs. Energy cannot be now routed by considering just only the criterion based on the simple shortening of transmission path. So, even energy coming from a far node should be preferred, if it has higher quality standards. In this view, the proposed smart power meter intends to support the smart power grid to monitor electricity among different nodes in an efficient and effective way.

Diagnosis of gastric disorders by non-invasive myoelectrical measurements

In the paper, the authors present a model to diagnose gastric disorders by non-invasive measurements. Gastric electrical activity is measured by electrogastrographic technique. Skin surface electrodes placed in epigastric area are used to record myoelectrical signals. Cutaneous electrogastrography (EGG) records gastric slow waves from the body surface. Acquired signals are filtered and subsequently processed by Discrete Wavelet Transform. The study of dominant frequency components allows physician to assess gastric activity arrhythmias (tachygastria, bradygastria and irregular rhythm) due to specific pathologies. Power spectral density diagrams are obtained. The proposed diagnostic criteria estimate the presence of gastric pathology by the analysis of the frequency and power of the EGG waveform. Specific diagnostic parameters have been defined and put in comparison with reference values of normal EGG in order to evaluate arrhythmias occurrence. An innovative approach has been used to optimize the multifactorial diagnosis model.

A Wearable Measurement System for the Risk Assessment Due to Physical Agents: Whole Body Mechanical Vibration Injuries

Whole Body Vibration (WBV) is caused by vibration transmitted to human body through the seat or the feet. Typical causes are motor vehicles and machines. So high levels of whole-body vibration affect people who drive vehicles over rough surfaces as part of their job, for example off-road vehicles such as tractors, excavators, pallet-trucks and dumper trucks. Exposure to high levels of vibration can risk the health and safety of the worker. Typically vibration transmitted to body may be cause of back injuries and may aggravate pre-existing pathologies affecting the lumbar spine. The risks are greatest when the vibration magnitudes are high, or if the exposure time is long. Moreover frequent and regular exposure to severe shocks or jolts can increase the resultant effects. So syndromes and diseases affect backbone and cardio-vascular system. The European Directive 2002/44/EC deals with the minimum health and safety requirements regarding the exposure of workers to the risks arising from physical agents. The “Vibration Directive” sets minimum standards for reducing the risks from whole-body vibration, so that daily exposure limits are fixed.

A Measurement System Design Technique for Improving Performances and Reliability of Smart and Fault-Tolerant Biomedical Systems

The present Chapter intends to provide a practical guide for designers in planning smart measurement systems to be used in critical applications like medical ones. The authors propose an original approach to the design of measurement instrumentation with high performances. The procedure allows the designer to characterize the best measurement uncertainty that the system must have. The main purpose is to project a system with appropriate performances, so that suitable accuracy and reliability levels are guaranteed for the resulting measurements. The used approach starts from the consideration that typically the measured data are used during the processing stage in order to make decisions. In example, medical diagnoses are based on measurements which are put in comparison with reference limits. Therefore the measurement uncertainty can affect the reliability of the final results so to be source of mistaken decisions. Consequently high values of measurement uncertainty may be cause of unreliable data and inaccurate diagnoses. In the Chapter, a statistical model is used in order to characterize the functional relationship between the measurement uncertainty and the probability to make mistaken decisions because of the same uncertainty. Consequently the designer can characterize the best uncertainty value for the measurement system to be projected. So suitable reliability can be guaranteed during the decision-making stage by assuring a tolerable probability of mistaken decision. Furthermore the Chapter describes the architecture used in order to design smart and patient-adaptive biomedical systems. In detail the use of specific memory devices is shown. Information concerning the metrological characteristics of system and the patient data are so made available. In detail, information on measurement uncertainty and calibration curve is stored in a first memory device in order to estimate the reliability of measurement results. Whereas a further writable and readable storage device stores private and medical data of the specific examined subject. Such memory is a personal data-logger replaced for each patient and updated with the passing of time according to the current clinical conditions of the subject. In this way the computing algorithm fits the patient by means of the available information so to qualify the final diagnosis. In fact the available data allow the system to adapt and configure itself according to the patient features and to his health state so to get fault-tolerant diagnoses. In this way it is possible to project a biomedical system which is updateable and configurable according to the specific subject. Experimental results concerning the project of an ECG measurement system are added.