Continuous Health Monitoring System Research Articles

Cuffless Blood Pressure Estimation Based on Data-Oriented Continuous Health Monitoring System.

Measuring blood pressure continuously helps monitor health and also prevent lifestyle related diseases to extend the expectancy of healthy life. Blood pressure, which is nowadays used for monitoring patient, is one of the most useful indexes for prevention of lifestyle related diseases such as hypertension. However, continuously monitoring the blood pressure is unrealistic because of discomfort caused by the tightening of a cuff belt. We have earlier researched the data-oriented blood pressure estimation without using a cuff. Remarkably, our blood pressure estimation method only uses a photoplethysmograph sensor. Therefore, the application is flexible for sensor locations and measuring situations. In this paper, we describe the implementation of our estimation method, the launch of a cloud system which can collect and manage blood pressure data measured by a wristwatch-type photoplethysmograph sensor, and the construction of our applications to visualize life-log data including the time-series data of blood pressure.

Structural Health Monitoring of an Historical Building: The Main Spire of the Duomo Di Milano

ABSTRACTHealth assessment of historical buildings by means of monitoring systems is for sure a fundamental step to preserve architectural heritage, especially for countries as Italy, rich in ancient monuments. Events such as earthquakes or severe weather phenomena constitute a major risk for these buildings because of their age and the related weakness to sustain exceptional excitations. The availability of a continuous, real-time, and automatic structural health monitoring system is considered a useful tool for early detection of a potentially dangerous situation for the structure and its occupants. The choice of the best measurement strategy provided by the monitoring system is crucial: a compromise must be found between the need of information, complexity of the measuring system and the related costs.Due to the difficulty in modelling most historical structures, it is a hard task to fix proper and safe limits relying on model-generated predictions only. The availability of long-term records of the structural response can help get this goal, not just relying on models, but also detecting changes in some parameters obtained from time records.This article presents a challenging example: the structural health monitoring of the Duomo di Milano main spire during and after restoration.

Robust cardiac event change detection method for long-term healthcare monitoring applications.

A long-term continuous cardiac health monitoring system highly demands more battery power for real-time transmission of electrocardiogram (ECG) signals and increases bandwidth, treatment costs and traffic load of the diagnostic server. In this Letter, the authors present an automated low-complexity robust cardiac event change detection (CECD) method that can continuously detect specific changes in PQRST morphological patterns and heart rhythms and then enable transmission/storing of the recorded ECG signals. The proposed CECD method consists of four stages: ECG signal quality assessment, R-peak detection and beat waveform extraction, temporal and RR interval feature extraction and cardiac event change decision. The proposed method is tested and validated using both normal and abnormal ECG signals including different types of arrhythmia beats, heart rates and signal quality. Results show that the method achieves an average sensitivity of 99.76%, positive predictivity of 94.58% and overall accuracy of 94.32% in determining the changes in heartbeat waveforms of the ECG signals.

Continuous Modal Parameter Identification of a Cable-Stayed Bridge Based on Robustious Decomposition and Covariance-Driven Stochastic Subspace Identification

The objective of this paper was to develop an effective and efficient continuous health monitoring system. For this purpose, a novel improved ensemble empirical mode decomposition (EEMD) method has been introduced to decompose the nonlinear and non-stationary dynamic data. The improved EEMD method can commendably remove the noise from the original signals, and also it can alleviate the phenomenon of mode mixing. Then the reconstructed signal obtained after applying the improved EEMD technique was used to identify the continuous modal parameters such as frequency and damping ratio using the covariance-driven stochastic subspace identification method. The above techniques were then employed to the data obtained from a real-life cable-stayed bridge to identify its continuous modal parameters. The results validate that the proposed method is very effective in dealing with dynamic nonlinear and non-stationary data and can be used very effectually in real-life continuous health monitoring of cable-stayed bridges.

A Continuous Health Monitoring System for Photovoltaic Array Using Arduino Microcontroller

In this paper new technique is developed to monitor the health status of the PV panels in the array. For finding the health status short circuit current is measured continuously over a fixed time period. This technique can classify the health status into four categories such as Healthy, Low Fault, Medium Fault and High Fault. By this classification faulty operation can be rectified and power generation may be improved. In case of high faults, PV panels can be protected. The cost requirement for the implementation is very low. The proposed technique is implemented in MATLAB Simulation and hardware. The array considered in this paper is 2 × 2 Series Parallel.

SCRHM: A Secure Continuous Remote Health Monitoring System

With the growing need for the remote caring at home and the everincreasing popularity of mobile devices, more and more applications are being developed to enable remote health monitoring. Combining cloud computing with mobile technologies has enabled the healthcare service provider to offer continuous health data collection and physicians to conveniently monitor and assess users’ health while they are at home. However, users’ health data contain highly confidential information, and servers in the cloud are out of users’ trusted domain. Therefore, users may be reluctant to take advantage of remote health monitoring systems before they make sure their data are properly protected. In this paper, we propose a secure continuous remote health monitoring system named SCRHM with the main component, a searchable encryption scheme supporting range searches for remote health monitoring system. With this scheme, remote health monitoring service provider is able to detect outliers over encrypted health parameters. Using analysis, we prove the correctness and security of the proposed scheme on privacy protection of users’ health data. Via simulation experiments, we validate the performance of the proposed scheme in terms of computation and communication overhead.

Single input-single output identification thermal response model of bridge using nonlinear ARX with wavelet networks

Model identification of a system can be used for a variety of purposes, including model updates, damage assessment, active control and original design evaluation. We used single input-single output (SISO) nonlinear regression with least square solution and nonlinear AutoRegresive with eXogenous inputs (NLARX) with wavelet neural networks models to identify the thermal response of a bridge under hard environmental effects and estimated the nonlinearity model parameters. Fu-Sui Bridge strain structural health monitoring measurements were used as a case study. Nonlinear regression analysis showed that the thermal response is a nonlinear effect with temperature changes; and the NLARX with wavelet network solution is capable of accurately predicting thermal response and can help with interpreting measurements from continuous bridge health monitoring systems.

Damage detection using the signal entropy of an ultrasonic sensor network

Piezoelectric ultrasonic sensors used to propagate guided waves can potentially be implemented to inspect large areas in engineering structures. However, the inherent dispersion and noise of guided acoustic signals, multiple echoes in the structure, as well as a lack of an approximate or exact model, limit their use as a continuous structural health monitoring system. In this work, the implementation of a network of piezoelectric sensors randomly placed on a plate-like structure to detect and locate artificial damage is studied. A network of macro fiber composite (MFC) sensors working in a pitch–catch configuration was set on an aluminum thin plate 1.9 mm in thickness. Signals were analyzed in the time-scale domain using the discrete wavelet transform. The objectives of this work were threefold, namely to first develop a damage index based on the entropy distribution using short time wavelet entropy of the ultrasonic waves generated by a sensor network, second to determine the performance of an array of spare MFC sensors to detect artificial damage, and third to implement a time-of-arrival (TOA) algorithm on the gathered signals for damage location of an artificial circular discontinuity. Our preliminary test results show that the proposed methodology provides sufficient information for damage detection, which, once combined with the TOA algorithm, allows localization of the damage.

Structural Health Monitoring of Composite Laminated Plates Using an Array of PWAS

The advances in using composite materials in different structural application lead to the need of continuous, robust, and structural health monitoring (SHM) systems. One of the most promising techniques for SHM is electromechanical impedance (EMI) technique which depends mainly on the coupling nature of piezoelectric ceramics. Piezoelectric wafer active sensors (PWAS) can be employed as both a well-controlled actuator and sensor at the same time for diagnostic algorithms based on EMI technique. The study presented in this paper interested in EMI technique application to detect damage in composite laminated plates by applying synchronized system of PWAS array operated remotely by exerting harmonic analysis in desired frequency ranges. Frequency ranges are selected upon modal analysis of the healthy tested structure. Harmonic analysis is carried out for different damage scenarios. Extracted electrical charge spectrum data from each PWAS for the previous scenarios can be processed to plot electrical impedance for each case. The modeling process was carried out using a finite element commercial package, ANSYS v.15.0 in which multi-physics-based modeling can be used for such structure made of laminated composite material. The extracted resultant spectrum, for healthy structure, is used as a datum in which it is related to its damaged counterparts through damage identification indices such as root mean square deviation (RMSD), and damage detection index (DDI). These indices were used as indicators for the changes in the modal parameters and hence, yielded reasonable results for both damage quantification and localization purposes.

Energy-Efficient Long-term Continuous Personal Health Monitoring

Continuous health monitoring using wireless body area networks of implantable and wearable medical devices (IWMDs) is envisioned as a transformative approach to healthcare. Rapid advances in biomedical sensors, low-power electronics, and wireless communications have brought this vision to the verge of reality. However, key challenges still remain to be addressed. The constrained sizes of IWMDs imply that they are designed with very limited processing, storage, and battery capacities. Therefore, there is a very strong need for efficiency in data collection, analysis, storage, and communication. In this paper, we first quantify the energy and storage requirements of a continuous personal health monitoring system that uses eight biomedical sensors: (1) heart rate, (2) blood pressure, (3) oxygen saturation, (4) body temperature, (5) blood glucose, (6) accelerometer, (7) electrocardiogram (ECG), and (8) electroencephalogram (EEG). Our analysis suggests that there exists a significant gap between the energy and storage requirements for long-term continuous monitoring and the capabilities of current devices. To enable energy-efficient continuous health monitoring, we propose schemes for sample aggregation, anomaly-driven transmission, and compressive sensing to reduce the overheads of wirelessly transmitting, storing, and encrypting/authenticating the data. We evaluate these techniques and demonstrate that they result in two to three orders-of-magnitude improvements in energy and storage requirements, and can help realize the potential of long-term continuous health monitoring.

European and global approaches to survey ageing populations and perspectives for joint approaches to measure age-related health outcomes

European and global approaches to survey ageing populations and perspectives for joint approaches to measure age-related health outcomes

New channel model for wireless body area network with compressed sensing theory

Wireless body area networks (WBANs) consist of small intelligent wireless sensors attached on or implanted in the body to collect vital biomedical data for providing a Continuous Health Monitoring System for diagnostic and therapeutic purposes. To fully exploit the benefits of WBANs the power consumption and sampling rate should be restricted to a minimum. The power usage can be minimised by optimising the features of multipath fading channels (MFCs) such as the number of arrival paths. That is why an improving of MFCs as well as a simple and generic channel model is inevitably required. With this in mind, compressed sensing (CS) theory, as a new sampling procedure, is employed to MFCs. Advance WBANs with the authors new model for MFCs based on CS theory will be able to deliver healthcare not only to patients in hospital and medical centres; but also in their homes and workplaces thus offering cost saving, and improving the quality of life. The authors simulation results illustrate 20% reduction for path loss and 10% for bit-error rate at gate way (GW). The simulation results also confirm that signal amplitude at GW increases by 25%, which will result in an increase, in the distance, between transmitter and receiver sensors.

Low sampling rate algorithm for wireless ECG systems based on compressed sensing theory

Wireless body area networks (WBANs) consist of small intelligent biomedical wireless sensors attached on or implanted in the body to collect vital biomedical data from the human body providing continuous health monitoring systems. The WBANs promise to be a key element in wireless electrocardiogram (ECG) monitoring systems for next generation. ECG signals are widely used in healthcare systems as a noninvasive technique for diagnosis of heart conditions. However, the use of conventional ECG system is restricted by patient’s mobility, transmission capacity, and physical size. Therefore, there is a great demand to improve wireless ECG systems. With this in mind, compressed sensing (CS) procedure as a new sampling approach and the collaboration of the sensing matrix selection algorithm based on dynamic thresholding approach are used to provide a robust low-complexity detection algorithm in gateways and access points with high probability and enough accuracy. Advanced wireless ECG systems based on our approach will be able to deliver healthcare not only to patients in hospitals and medical centers, but also at their homes and workplaces thus offering cost saving, and improving the quality of life. Our simulation results show an increment of 1 % for sensitivity as well as 0.15 % for the prediction level and good detection accuracy. The simulation results also confirm that the binary Toeplitz matrix provides the best signal-to-noise ratio and compression performance with the highest energy efficiency for random sensing matrix in CS procedure.

COORDINATED SENSING OF NETWORKED BODY SENSORS USING MARKOV DECISION PROCESSES

This article describes a Markov decision process (MDP) framework for coordinated sensing between correlated sensors in a body-area network. The technique is designed to extend the life of mobile continuous health-monitoring systems based on energy-constrained wearable sensors. The technique enables distributed sensors in a body-area network to adapt their sampling rates in response to changing criticality of the detected data and the limited energy reserve at each sensor node. The relationship between energy consumption, sampling rates, and utility of coordinated measurements is formulated as an MDP. This MDP is solved to generate a globally optimal policy that specifies the sampling rates for each sensor for all possible states of the system. This policy is computed offline before deployment and only the resulting policy is stored within each sensor node. We also present a method of executing the global policy without requiring continuous communication between the sensors. Each sensor node maintains a local estimate of the global state. Communication occurs only when an information-theoretic model of the uncertainty in the local state estimates exceeds a predefined threshold. We show results on simulated data that demonstrate the efficacy of this distributed-control framework and compare the performance of the proposed controller with other policies.

Sensing Methodologies and Sensor Networks for Health Monitoring of Civil Infrastructures

Large and complex civil infrastructures are especially susceptible to random vibrations, whether they are from high ground accelerations, strong wind forces, or abnormal loads such as explosions. Thus, it is imperative that the robust and continuous health monitoring system needs to be designed and implemented so as to meet the demanding goals of increasing structural safety and reliability, while reducing its operating and maintenance costs. The efficiency of a structural health monitoring system (SHMS) relies on the sensitivity of the acquired data to structural changes that may be obtained by an extended sensor network on structures as shown in “Methodology developments in sensor placement for health monitoring of civil infrastructures” and [1]. Recent advances in sensing technologies have enabled the use of large numbers of sensors for health monitoring of civil infrastructures. The sensors utilized in an SHMS are required to monitor not only the structural status including the stress, displacement, and acceleration but also those influential environmental parameters, such as the wind speed, temperature, and quality of its foundation. Since a large sensor network needs to be involved in an SHMS, how to deploy it in a complicated, spatially extended structure so that the data acquired from those locations will result in the best identification of structural characteristics will be a challenging task. On the other hand, a high cost of data acquisition systems (sensors and their supporting instruments) and accessibility limitation constrain in many cases the wide distribution of a large sensor networks on a structure. Especially, many civil structures have to be tested under operational conditions, in which the sensors are not easily amenable to be removed or changed [2, 3]. It is, therefore, of increasing interests to seek rational placement and design a method of sensor networks that contain as much information as possible about the health state of a structure. This special issue aims to aggregate the latest research efforts contributing to theoretical, methodological, and technological advances in the integration of various aspects of SHMS applications within a broad range of disciplines. Some research topics towards this target include intelligent and efficient algorithms in the design of sensor networks; evaluation criteria for different sensor networks placement methods; inherent relationship between different sensor networks placement methods; uncertainty, sensitivity, reliability, and redundancy in sensor networks; application of large scale sensors for civil infrastructures and others related. A total of 26 peer-reviewed papers have been presented in this special issue. These include identifying the optimal number and location of sensors, as well as the robustness, which aims at maintaining the stability of the sensor network when some sensors malfunction, and so forth. Without a doubt, the papers reflect the state-of-the-art researches and developments of this subject. The Guest Editor would like to express his sincere appreciation to all the authors who have contributed their work to this special issue.

A 5.2 mW Self-Configured Wearable Body Sensor Network Controller and a 12 $\mu$W Wirelessly Powered Sensor for a Continuous Health Monitoring System

A self-configured body sensor network controller and a high efficiency wirelessly powered sensor are presented for a wearable, continuous health monitoring system. The sensor chip harvests its power from the surrounding health monitoring band using an Adaptive Threshold Rectifier (ATR) with 54.9% efficiency, and it consumes 12 μW to implement an electrocardiogram (ECG) analog front-end and an ADC. The ATR is implemented with a standard CMOS process for low cost. The adhesive bandage type sensor patch is composed of the sensor chip, a Planar-Fashionable Circuit Board (P-FCB) inductor, and a pair of dry P-FCB electrodes. The dry P-FCB electrodes enable long term monitoring without skin irritation. The network controller automatically locates the sensor position, configures the sensor type (self-configuration), wirelessly provides power to the configured sensors, and transacts data with only the selected sensors while dissipating 5.2 mW at a single 1.8 V supply. Both the sensor and the health monitoring band are implemented using P-FCB for enhanced wearability and for lower production cost. The sensor chip and the network controller chip occupy 4.8 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and 15.0 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively, including pads, in standard 0.18 μm 1P6M CMOS technology.

Wireless and embedded carbon nanotube networks for damage detection in concretestructures

Concrete structures undergo an uncontrollable damage process manifesting in the form ofcracks due to the coupling of fatigue loading and environmental effects. In order to achievelong-term durability and performance, continuous health monitoring systems are needed tomake critical decisions regarding operation, maintenance and repairs. Recentadvances in nanostructured materials such as carbon nanotubes have opened thedoor for new smart and advanced sensing materials that could effectively be usedin health monitoring of structures where wireless and real time sensing couldprovide information on damage development. In this paper, carbon nanotubenetworks were embedded into a cement matrix to develop an in situ wirelessand embedded sensor for damage detection in concrete structures. By wirelesslymeasuring the change in the electrical resistance of the carbon nanotube networks, theprogress of damage can be detected and monitored. As a proof of concept, wirelesscement–carbon nanotube sensors were embedded into concrete beams and subjected tomonotonic and cyclic loading to evaluate the effect of damage on their response.Experimental results showed that the wireless response of the embedded nanotube sensorschanges due to the formation of cracks during loading. In addition, the nanotubesensors were able to detect the initiation of damage at an early stage of loading.

Guided-wave Health Monitoring of Aircraft Composite Panels under Changing Temperature

This study deals with the health monitoring of fiber-reinforced composite panels using ultrasonic guided waves and flexible piezocomposite transducer patches in a changing temperature environment corresponding to normal aircraft operations (—40°C to +60°C). The wave propagation problem is first studied analytically by a model that accounts for temperature effects on the transducer piezo-mechanical properties, the transducer-panel interaction, and the panel wave dispersion properties. Experiments are also conducted on a Carbon-fiber Reinforced Plastic (CFRP) [0/±45/0]S laminate subjected to the —40°C to +60°C temperature excursion. Both model and experiment indicate substantial changes in the detected guided wave amplitude solely due to the temperature excursion. The second part of the study presents an application to bond defect detection in a simulated CFRP skin-to-spar joint of Unmanned Aerial Vehicle wings. It is shown that a statistical outlier analysis based on multiple guided-wave amplitude features and on a baseline partition is effective in detecting bond defects (poorly cured adhesive and two sizes of disbonds) despite the —40°C to +60°C temperature change. The results encourage the development of a continuous health monitoring system for composite aircraft wings during their normal operations.

A 24-hour health monitoring system in a smart house

This paper presents a 24-hour continuous health monitoring system installed in an Intelligent Sweet Home, which is an advanced smart house developed at Human-friendly Welfare Robot System Engineering Research Center in Korea. Aiming to aid independent living of the elderly and/or people with disabilities, this system is composed of three parts: (i) a measurement device of biosignals, (ii) a monitoring system for caregivers, and (iii) client/server personal computers (PCs) for processing and archiving of history data. The measurement device measures electromyogram (EMG), electrocardiogram (ECG), body temperature, and blood pressure, and delivers the data to the client PC via Bluetooth channels. For the process, some surface-type active electrodes have been developed to measure, amplify, and filter the biosignals more clearly and conveniently. The monitoring system sends the information on health condition and security status to the caregiver’s cellular phone via a short message service (SMS) server. The caregiver only can access the system using a password and observe the situation in the Intelligent Sweet Home.

Damage assessment using flexibility and flexibility-based curvature for structural healthmonitoring

As a result of the recent advances in sensors, information technologies and materialscience, a considerable amount of research has been conducted in the area of smartinfrastructures. While there are many important components of a smart infrastructure,an automated and continuous structural health monitoring (SHM) system is acritical one. SHM is typically used to track and evaluate the performance of astructure, symptoms of operational incidents, anomalies due to deterioration anddamage during regular operation as well as after an extreme event. Successful healthmonitoring applications can be achieved by integrating experimental, analyticaland information technologies on real-life operating structures. However, real-lifeinvestigations must be backed up by laboratory benchmark studies for validatingtheory, concepts, and new technologies. For this reason, a physical bridge model isdeveloped to implement SHM methods and technologies. In this study, differentaspects of model development are outlined in terms of design considerations,instrumentation, finite element modeling, and simulating damage scenarios. Differentdamage detection methods are evaluated using the numerical and the physicalmodels. Modal parameter estimation studies are carried out to reliably identifythe eigenvalues, eigenvectors and modal scaling from the measurement data. Toassess the simulated damage, modal flexibility-based displacements and curvaturesare employed. Structural behavior after damage is evaluated by inspecting thedeflected shapes obtained using modal flexibility. More localized damage simulationssuch as stiffness reduction at a joint yield a very subtle stiffness decrease. In thiscase, the writers use a baseline to identify damage and also investigate the useof curvature as a complementary index. Curvature is advantageous for certaincases where the displacement results do not provide substantial changes. Issuesrelated to using curvature as a damage identification index are also addressed.