Mechanically flexible wireless multisensor platform for human physical activity and vitals monitoring

Second-Order Level-Crossing Sampling Analog to Digital Converter for Electrocardiogram Delineation and Premature Ventricular Contraction Detection

This article presents an electrocardiogram (ECG) delineation and arrhythmia heartbeat detection system using a novel second-order level-crossing sampling analog to digital converter (ADC) for real-time data compression and feature extraction. The proposed system consists of the front-end integrated circuit of the data converter, the delineation algorithm, and the arrhythmia detection algorithm. Compared with conventional level-sampling ADCs, the proposed circuit updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has sloped sampling levels. The computing is done digitally and is implemented by modifying the digital control logic of a conventional Successive-approximation-register (SAR) ADC. The system separates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the digital value of the selected sampling points and the timestamp between the selected sampling points. The main advantages are data savings for the data converter and the following digital signal processing or communication circuits, which are ideal for low-power sensors. The test chip was fabricated using a 180 nm CMOS process. When sensing sparse signals such as ECG signals the proposed ADC achieves a compression factor of 8.33. The delineation algorithm uses a triangle filter method to locate the fiducial points and measures the intervals, slopes, and morphology of the QRS complex and the P/T waves. Those extracted features are then used in the arrhythmia heartbeat detection algorithm to identify Premature Ventricular Contraction (PVC). The overall performance of the system is evaluated using the MIT-BIH database and the QT database, which is also compared with the recently reported systems. The accuracy, sensitivity, specificity, PPV, and F1 score are 97.3%, 89.6%, 97.8%, 73.3%, and 0.81 for detecting PVC.

Stretchable printed device for the simultaneous sensing of temperature and strain validated in a mouse wound healing model

Temperature and strain are two vital parameters that play a significant role in wound diagnosis and healing. As periodic temperature measurements with a custom thermometer or strain measurements with conventional metallic gauges became less feasible for the modern competent health monitoring, individual temperature and strain measurement modalities incorporated into wearables and patches were developed. The proposed research in the article shows the development of a single sensor solution which can simultaneously measure both the above mentioned parameters. This work integrates a thermoelectric principle based temperature measurement approach into wearables, ensuring flexibility and bendability properties without affecting its thermo-generated voltage. The modified thermoelectric material helped to achieve stretchability of the sensor, thanks to its superior mechano-transduction properties. Moreover, the stretch-induced resistance changes become an additional marker for strain measurements so that both the parameters can be measured with the same sensor. Due to the independent measurement parameters (open circuit voltage and sensor resistance), the sensing model is greatly attractive for measurements without cross-sensitivity. The highly resilient temperature and strain sensor show excellent linearity, repeatability and good sensitivity. Besides, due to the compatibility of the fabrication scheme to low-temperature processing of the flexible materials and to mass volume production, printed fabrication methodologies were adopted to realize the sensor. This promises low-cost production and a disposable nature (single use) of the sensor patch. For the first time, this innovative temperature-strain dual parameter sensor concept has been tested on mice wounds in vivo. The preliminary experiments on mice wounds offer prospects for developing smart, i.e. sensorized, wound dressings for clinical applications.

An ECG Delineation and Arrhythmia Classification System Using Slope Variation Measurement by Ternary Second-Order Delta Modulators for Wearable ECG Sensors

This paper presents a system for electrocardiogram (ECG) delineation and arrhythmia classification. The proposed system consists of a front-end integrated circuit, a delineation algorithm implemented on an FPGA board, and an arrhythmia classification algorithm. The front-end circuit applies a ternary second-order Delta modulator to measure the slope variation of the input analog ECG signal. The circuit converts the analog inputs into a pulse density modulated bitstream, whose pulse density is proportional to the slope variation of the input analog signal regardless of the instantaneous amplitude. The front-end chip can detect the minimum slope variation of 3.2 mV/ms ² within a 3 ms timing error. The front-end integrated circuit was fabricated with a 180 nm CMOS process occupying a 0.25 mm ² area with a 151 nW power consumption at the sampling rate of 1 kS/s. Based on the slope variation obtained from the front-end circuit, a delineation algorithm is designed to detect fiducial points in the ECG waveform. The delineation algorithm was tested on a Spartan-6 FPGA. The delineation system can detect the intervals, slopes, and morphology of the QRS/PT waves and form a feature set that contains 22 features. Based on these features, a rotate linear kernel support vector machine (SVM) is applied for patient-specific arrhythmia classification of the ventricular ectopic beat (VEB), supraventricular ectopic beat (SVEB), and heartbeats originating in sinus node. The performance of the proposed system is comparable to the recently published methods while providing a promising solution for the low-complexity implementation of future wearable ECG monitoring systems.

Energy-Positive Activity Recognition - From Kinetic Energy Harvesting to Smart Self-Sustainable Wearable Devices

Wearable, intelligent, and unobtrusive sensor nodes that monitor the human body and the surrounding environment have the potential to create valuable data for preventive human-centric ubiquitous healthcare. To attain this vision of unobtrusiveness, the smart devices have to gather and analyze data over long periods of time without the need for battery recharging or replacement. This article presents a software-configurable kinetic energy harvesting and power management circuit that enables self-sustainable wearable devices. By exploiting the kinetic transducer as an energy source and an activity sensor simultaneously, the proposed circuit provides highly efficient context-aware control features. Its mixed-signal nano-power context awareness allows reaching energy neutrality even in energy-drought periods, thus significantly relaxing the energy storage requirements. Furthermore, the asynchronous sensing approach also doubles as a coarse-grained human activity recognition frontend. Experimental results, using commercial micro-kinetic generators, demonstrate the flexibility and potential of this approach: the circuit achieves a quiescent current of 57 nA and a maximum load current of 300 mA, delivered with a harvesting efficiency of 79%. Based on empirically collected motion data, the system achieves an energy surplus of over 232 mJ per day in a wrist-worn application while executing activity recognition at an accuracy of 89% and a latency of 60 s.

Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning

Coughing is a common symptom for many respiratory disorders, and can spread droplets of various sizes containing bacterial and viral pathogens. Mild coughs are usually overlooked in the early stage, not only because they are barely noticeable by the person and the people around, but also because the present recording method is not comfortable, private, or reliable for long-term monitoring. In this paper, a wearable radio-frequency (RF) sensor is presented to recognize the mild cough signal directly from the local trachea vibration characteristics, and can isolate interferences from nearby people. The sensor operates at the ultra-high-frequency band, and can couple the RF energy to the upper respiratory track by the near field of the sensing antenna. The retrieved tissue vibration caused by the cough airflow burst can then be analyzed by a convolutional neural network trained on the frequency-time spectra. The sensing antenna design is analyzed for performance improvement. During the human study of 5 participants over 100 minutes of prescribed routines, the overall recognition ratio is above 90% and the false positive ratio during other routines is below 2.09%.

Review and Challenges of Technologies for Real-Time Human Behavior Monitoring

A person's behavior significantly influences their health and well-being. It also contributes to the social environment in which humans interact, with cascading impacts to the health and behaviors of others. During social interactions, our understanding and awareness of vital nonverbal messages expressing beliefs, emotions, and intentions can be obstructed by a variety of factors including greatly flawed self-awareness. For these reasons, human behavior is a very important topic to study using the most advanced technology. Moreover, technology offers a breakthrough opportunity to improve people's social awareness and self-awareness through machine-enhanced recognition and interpretation of human behaviors. This paper reviews (1) the social psychology theories that have established the framework to study human behaviors and their manifestations during social interactions and (2) the technologies that have contributed to the monitoring of human behaviors. State-of-the-art in sensors, signal features, and computational models are categorized, summarized, and evaluated from a comprehensive transdisciplinary perspective. This review focuses on assessing technologies most suitable for real-time monitoring while highlighting their challenges and opportunities in near-future applications. Although social behavior monitoring has been highly reported in psychology and engineering literature, this paper uniquely aims to serve as a disciplinary convergence bridge and a guide for engineers capable of bringing new technologies to bear against the current challenges in real-time human behavior monitoring.

Transformable Electrocardiograph Using Robust Liquid-Solid Heteroconnector

In this study, a highly transformable electrocardiograph that can considerably deform the position of stretchable electrodes based on the lead method for diagnosing heart disease was developed; these electrodes exhibited high resistance stability against considerable stretching and multiple stretching. To realize the large deformable functionality of the electrodes of a system, liquid metal electrodes and a heteroconnector composed of a liquid metal paste and carbon-based conductive rubber were employed. The developed device can achieve a 200% strain with only 6% resistance change and a high stability of resistances after the 100-time stretching test. In addition, the study demonstrated electrocardiograms in different lead methods of adult and child using the same device. The proposed combination of large deformable electrodes with high electric stability and a robust heteroconnector is an important technology, and it presents a considerable advancement in the application of stretchable electronic systems.

Classifying sitting, standing, and walking using plantar force data

Prolonged static weight-bearing at work may increase the risk of developing plantar fasciitis (PF). However, to establish a causal relationship between weight-bearing and PF, a low-cost objective measure of workplace behaviors is needed. This proof-of-concept study assesses the classification accuracy and sensitivity of low-resolution plantar pressure measurements in distinguishing workplace postures. Plantar pressure was measured using an in-shoe measurement system in eight healthy participants while sitting, standing, and walking. Data was resampled to simulate on/off characteristics of 24 plantar force sensitive resistors. The top 10 sensors were evaluated using leave-one-out cross-validation with machine learning algorithms: support vector machines (SVMs), decision tree (DT), discriminant analysis (DA), and k-nearest neighbors (KNN). SVM and DT best classified sitting, standing, and walking. High classification accuracy was obtained with five sensors (98.6% and 99.1% accuracy, respectively) and even a single sensor (98.4% and 98.4%, respectively). The central forefoot and the medial and lateral midfoot were the most important classification sensor locations. On/off plantar pressure measurements in the midfoot and central forefoot can accurately classify workplace postures. These results provide the foundation for a low-cost objective tool to classify and quantify sedentary workplace postures.

Application of Modern Multi-Sensor Holter in Diagnosis and Treatment

Modern Holter devices are very trendy tools used in medicine, research, or sport. They monitor a variety of human physiological or pathophysiological signals. Nowadays, Holter devices have been developing very fast. New innovative products come to the market every day. They have become smaller, smarter, cheaper, have ultra-low power consumption, do not limit everyday life, and allow comfortable measurements of humans to be accomplished in a familiar and natural environment, without extreme fear from doctors. People can be informed about their health and 24/7 monitoring can sometimes easily detect specific diseases, which are normally passed during routine ambulance operation. However, there is a problem with the reliability, quality, and quantity of the collected data. In normal life, there may be a loss of signal recording, abnormal growth of artifacts, etc. At this point, there is a need for multiple sensors capturing single variables in parallel by different sensing methods to complement these methods and diminish the level of artifacts. We can also sense multiple different signals that are complementary and give us a coherent picture. In this article, we describe actual interesting multi-sensor principles on the grounds of our own long-year experiences and many experiments.

A Real-Time Arrhythmia Heartbeats Classification Algorithm Using Parallel Delta Modulations and Rotated Linear-Kernel Support Vector Machines

Real-time wearable electrocardiogram monitoring sensor is one of the best candidates in assisting cardiovascular disease diagnosis. In this paper, we present a novel real-time machine learning system for Arrhythmia classification. The system is based on the parallel Delta modulation and QRS/PT wave detection algorithms. We propose a patient dependent rotated linear-kernel support vector machine classifier that combines the global and local classifiers, with three types of feature vectors extracted directly from the Delta modulated bit-streams. The performance of the proposed system is evaluated using the MIT-BIH Arrhythmia database. According to the AAMI standard, two binary classifications are performed and evaluated, which are supraventricular ectopic beat (SVEB) versus the rest four classes, and ventricular ectopic beat (VEB) versus the rest. For SVEB classification, the preferred SkP-32 method's F1 score, sensitivity, specificity, and positive predictivity value are 0.83, 79.3%, 99.6%, and 88.2%, respectively, and for VEB classification, the numbers are 0.92%, 92.8%, 99.4%, and 91.6%, respectively. The results show that the performance of our proposed approach is comparable to that of published research. The proposed low-complexity algorithm has the potential to be implemented as an on-sensor machine learning solution.

A rapid approach to assess cardiac contractility by ballistocardiogram and electrocardiogram

In this paper, we propose a rapid assessment on cardiac contractility by using the time interval between the I wave of ballistocardiogram (BCG) and the R wave of electrocardiogram (ECG) which is referred to as the RI interval. The whole work can be divided into two parts. First, the correlation between the RI interval and the ejection fraction (EF), which is a clinical index to assess systolic performance, was computed. For 39 subjects, the correlation coefficient is -0.54 (p<0.001). Moreover, RI intervals of heart failure (HF) patients and healthy subjects were measured, and a significant difference was found among different New York Heart Association (NYHA) classes and the healthy group. Second, the beat-to-beat correlation analysis between the RI interval and the pre-ejection period (PEP), which is a parameter of systolic time interval to evaluate the cardiac contractility, was calculated. For 4578 heart beats across eight healthy subjects, the correlation coefficient is 0.85 (p<0.001). As a conclusion, these results indicate that the RI interval can be used as a noninvasive assessment of cardiac contractility.

Electrical extraction of piezoelectric constants

The piezoelectric materials are incorporated in smart structure to exhibit specific functionality. The activity of piezoelectric material dimension and electrical properties can be changed with an applied stress. These variations are translated to a change in the capacitance of the structure. This work takes a close outlook on the use of the capacitance-voltage measurements for the extraction of double piezoelectric thin film material deposited at the two faces of a flexible steel sheet. The piezoelectric thin film materials have been deposited using RF sputtering techniques. Gamry analyzer references 3000 was used to collect the capacitance-voltage measurements from both layers. The developed algorithm extracts directly the piezoelectric coefficients knowing the film thickness, the applied voltage, and the capacitance ratio. The capacitance ratio is the ratio between the capacitances of the film when the applied field in antiparallel and parallel to the polling field direction, respectively. The method has been calibrated using a piezoelectric bulk ceramic and validated by comparing the result with the reported values in the literature. The extracted values using the current approach match well the values extracted by other existing methods.

A Real-Time QRS Detection System With PR/RT Interval and ST Segment Measurements for Wearable ECG Sensors Using Parallel Delta Modulators

This paper presents a real-time electrocardiogram (ECG) monitoring system for wearable devices. The system is based on the proposed parallel delta modulator architecture with local maximum point and local minimum point algorithms to detect QRS and PT waves. Therefore, using the proposed system and algorithm, real-time PR and RT intervals, and ST segment measurements can be achieved in long-term wearable ECG recording. The algorithm is tested with the MIT-BIH Arrhythmia Database for QRS complex detection and with the QT Database for the P and T wave detections. The simulation result shows that the algorithm achieves above 99%, 91%, and 98% accuracy in the QRS complex, P wave, and T wave detections, respectively. Experimental results are presented from the system prototype, in which the parallel delta modulator circuits are fabricated in IBM 0.13 $\mu \text{m}$ standard CMOS technology and the algorithms are implemented in a Xilinx Spartan-6 field programmable gate array (FPGA). The parallel delta modulators consume 720 nW at 1 kHz sampling rate with $\pm$0.6 V power supply. The proposed system has the potential to be applied in future long-term wearable ECG recording devices.

Dip coated stretchable and bendable PEDOTPSS films on low modulus micro-bumpy PDMS substrate

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOTPSS) is an organic conductive polymer which has a great potential to replace metallic conductors in thin film electronics. This paper reports the experimental findings on the electromechanical performance of conductive PEDOTPSS thin films on a stretchable and flexible low modulus polymer substrate, polydimethylsiloxane (PDMS), having random micro-bumpy roughness features. All films were fabricated using the method of dip coating, which is cost effective and also favorable for mass production. The main goal of the study is to quantify the stretchability and bendability of dip coated PEDOTPSS thin films on PDMS substrate having random micro-bumpy type of roughness features. The films displayed almost constant resistance up to 10% axial strain and were also found to remain conductive when bent up to a diameter of 2 mm.

An Enhanced Method to Estimate Heart Rate from Seismocardiography via Ensemble Averaging of Body Movements at Six Degrees of Freedom

Continuous cardiac monitoring has been developed to evaluate cardiac activity outside of clinical environments due to the advancement of novel instruments. Seismocardiography (SCG) is one of the vital components that could develop such a monitoring system. Although SCG has been presented with a lower accuracy, this novel cardiac indicator has been steadily proposed over traditional methods such as electrocardiography (ECG). Thus, it is necessary to develop an enhanced method by combining the significant cardiac indicators. In this study, the six-axis signals of accelerometer and gyroscope were measured and integrated by the L2 normalization and multi-dimensional kineticardiography (MKCG) approaches, respectively. The waveforms of accelerometer and gyroscope were standardized and combined via ensemble averaging, and the heart rate was calculated from the dominant frequency. Thirty participants (15 females) were asked to stand or sit in relaxed and aroused conditions. Their SCG was measured during the task. As a result, proposed method showed higher accuracy than traditional SCG methods in all measurement conditions. The three main contributions are as follows: (1) the ensemble averaging enhanced heart rate estimation with the benefits of the six-axis signals; (2) the proposed method was compared with the previous SCG method that employs fewer-axis; and (3) the method was tested in various measurement conditions for a more practical application.

Automatic Detection of Seismocardiogram Sensor Misplacement for Robust Pre-Ejection Period Estimation in Unsupervised Settings

Seismocardiography (SCG), the measurement of the local chest vibrations due to the movements of blood and the heart, is a non-invasive technique for assessing myocardial contractility via the pre-ejection period (PEP). Recently, SCG-based extraction of PEP has been shown to be an effective means of classifying decompensated from compensated heart failure patients, and thus can be potentially used for monitoring such patients at home. Accurate extraction of PEP from SCG signals hinges on lab-based population data (i.e., regression curves) linking particular time-domain features of the SCG signal to corresponding features from reference standard bulky instruments such as impedance cardiography (ICG). Such regression curves, in the case of SCG, have always been estimated based on the "ideal" positioning of the SCG sensor on the chest. However, in settings such as the home where users may position the SCG measurement hardware on the chest without supervision, it is likely that the sensor will not always be placed exactly on this "ideal" location on the sternum, but rather on other positions on the chest as well. In this study, we show for the first time that the regression curve for estimating PEP from SCG signals differs significantly as the position of the sensor changes. We further devise a method to automatically detect when the sensor is placed in any position other than the desired one in order to avoid inaccurate systolic time interval estimation. Our classification algorithm for this purpose resulted in 0.83 precision and 0.82 recall when classifying whether the sensor is placed in the desired position or not. The classifier was tested with heartbeats taken both at rest, and also during exercise recovery to ensure that waveform changes due to positioning could be accurately discriminated from those due to physiological effects.

Automatic Identification of Systolic Time Intervals in Seismocardiogram

Continuous and non-invasive monitoring of hemodynamic parameters through unobtrusive wearable sensors can potentially aid in early detection of cardiac abnormalities, and provides a viable solution for long-term follow-up of patients with chronic cardiovascular diseases without disrupting the daily life activities. Electrocardiogram (ECG) and siesmocardiogram (SCG) signals can be readily acquired from light-weight electrodes and accelerometers respectively, which can be employed to derive systolic time intervals (STI). For this purpose, automated and accurate annotation of the relevant peaks in these signals is required, which is challenging due to the inter-subject morphological variability and noise prone nature of SCG signal. In this paper, an approach is proposed to automatically annotate the desired peaks in SCG signal that are related to STI by utilizing the information of peak detected in the sliding template to narrow-down the search for the desired peak in actual SCG signal. Experimental validation of this approach performed in conventional/controlled supine and realistic/challenging seated conditions, containing over 5600 heart beat cycles shows good performance and robustness of the proposed approach in noisy conditions. Automated measurement of STI in wearable configuration can provide a quantified cardiac health index for long-term monitoring of patients, elderly people at risk and health-enthusiasts.

Epidermal mechano-acoustic sensing electronics for cardiovascular diagnostics and human-machine interfaces

Physiological mechano-acoustic signals, often with frequencies and intensities that are beyond those associated with the audible range, provide information of great clinical utility. Stethoscopes and digital accelerometers in conventional packages can capture some relevant data, but neither is suitable for use in a continuous, wearable mode, and both have shortcomings associated with mechanical transduction of signals through the skin. We report a soft, conformal class of device configured specifically for mechano-acoustic recording from the skin, capable of being used on nearly any part of the body, in forms that maximize detectable signals and allow for multimodal operation, such as electrophysiological recording. Experimental and computational studies highlight the key roles of low effective modulus and low areal mass density for effective operation in this type of measurement mode on the skin. Demonstrations involving seismocardiography and heart murmur detection in a series of cardiac patients illustrate utility in advanced clinical diagnostics. Monitoring of pump thrombosis in ventricular assist devices provides an example in characterization of mechanical implants. Speech recognition and human-machine interfaces represent additional demonstrated applications. These and other possibilities suggest broad-ranging uses for soft, skin-integrated digital technologies that can capture human body acoustics.

A Wearable Patch to Enable Long-Term Monitoring of Environmental, Activity and Hemodynamics Variables

We present a low power multi-modal patch designed for measuring activity, altitude (based on high-resolution barometric pressure), a single-lead electrocardiogram, and a tri-axial seismocardiogram (SCG). Enabled by a novel embedded systems design methodology, this patch offers a powerful means of monitoring the physiology for both patients with chronic cardiovascular diseases, and the general population interested in personal health and fitness measures. Specifically, to the best of our knowledge, this patch represents the first demonstration of combined activity, environmental context, and hemodynamics monitoring, all on the same hardware, capable of operating for longer than 48 hours at a time with continuous recording. The three-channels of SCG and one-lead ECG are all sampled at 500 Hz with high signal-to-noise ratio, the pressure sensor is sampled at 10 Hz, and all signals are stored to a microSD card with an average current consumption of less than 2 mA from a 3.7 V coin cell (LIR2450) battery. In addition to electronic characterization, proof-of-concept exercise recovery studies were performed with this patch, suggesting the ability to discriminate between hemodynamic and electrophysiology response to light, moderate, and heavy exercise.

A Flexible and Wearable Human Stress Monitoring Patch

A human stress monitoring patch integrates three sensors of skin temperature, skin conductance, and pulsewave in the size of stamp (25 mm × 15 mm × 72 μm) in order to enhance wearing comfort with small skin contact area and high flexibility. The skin contact area is minimized through the invention of an integrated multi-layer structure and the associated microfabrication process; thus being reduced to 1/125 of that of the conventional single-layer multiple sensors. The patch flexibility is increased mainly by the development of flexible pulsewave sensor, made of a flexible piezoelectric membrane supported by a perforated polyimide membrane. In the human physiological range, the fabricated stress patch measures skin temperature with the sensitivity of 0.31 Ω/°C, skin conductance with the sensitivity of 0.28 μV/0.02 μS, and pulse wave with the response time of 70 msec. The skin-attachable stress patch, capable to detect multimodal bio-signals, shows potential for application to wearable emotion monitoring.

Unobtrusive sensing and wearable devices for health informatics

The aging population, prevalence of chronic diseases, and outbreaks of infectious diseases are some of the major challenges of our present-day society. To address these unmet healthcare needs, especially for the early prediction and treatment of major diseases, health informatics, which deals with the acquisition, transmission, processing, storage, retrieval, and use of health information, has emerged as an active area of interdisciplinary research. In particular, acquisition of health-related information by unobtrusive sensing and wearable technologies is considered as a cornerstone in health informatics. Sensors can be weaved or integrated into clothing, accessories, and the living environment, such that health information can be acquired seamlessly and pervasively in daily living. Sensors can even be designed as stick-on electronic tattoos or directly printed onto human skin to enable long-term health monitoring. This paper aims to provide an overview of four emerging unobtrusive and wearable technologies, which are essential to the realization of pervasive health information acquisition, including: 1) unobtrusive sensing methods, 2) smart textile technology, 3) flexible-stretchable-printable electronics, and 4) sensor fusion, and then to identify some future directions of research.

Feasibility of Energy-Autonomous Wireless Microsensors for Biomedical Applications: Powering and Communication

In this review, biomedical-related wireless miniature devices such as implantable medical devices, neural prostheses, embedded neural systems, and body area network systems are investigated and categorized. The two main subsystems of such designs, the RF subsystem and the energy source subsystem, are studied in detail. Different application classes are considered separately, focusing on their specific data rate and size characteristics. Also, the energy consumption of state-of-the-art communication practices is compared to the energy that can be generated by current energy scavenging devices, highlighting gaps and opportunities. The RF subsystem is classified, and the suitable architecture for each category of applications is highlighted. Finally, a new figure of merit suitable for wireless biomedical applications is introduced to measure the performance of these devices and assist the designer in selecting the proper system for the required application. This figure of merit can effectively fill the gap of a much required method for comparing different techniques in simulation stage before a final design is chosen for implementation.

Compressive sensing of electrocardiogram signals by promoting sparsity on the second-order difference and by using dictionary learning

A new algorithm for the reconstruction of electrocardiogram (ECG) signals and a dictionary learning algorithm for the enhancement of its reconstruction performance for a class of signals are proposed. The signal reconstruction algorithm is based on minimizing the lp pseudo-norm of the second-order difference, called as the lp(2d) pseudo-norm, of the signal. The optimization involved is carried out using a sequential conjugate-gradient algorithm. The dictionary learning algorithm uses an iterative procedure wherein a signal reconstruction and a dictionary update steps are repeated until a convergence criterion is satisfied. The signal reconstruction step is implemented by using the proposed signal reconstruction algorithm and the dictionary update step is implemented by using the linear least-squares method. Extensive simulation results demonstrate that the proposed algorithm yields improved reconstruction performance for temporally correlated ECG signals relative to the state-of-the-art lp(1d)-regularized least-squares and Bayesian learning based algorithms. Also for a known class of signals, the reconstruction performance of the proposed algorithm can be improved by applying it in conjunction with a dictionary obtained using the proposed dictionary learning algorithm.

A configurable and low-power mixed signal SoC for portable ECG monitoring applications

This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts. A custom digital signal processor consisting of 4-way SIMD processor provides the configurability and advanced functionality like motion artifact removal and R peak detection. A built-in 12-bit analog-to-digital converter (ADC) is capable of adaptive sampling achieving a compression ratio of up to 7, and loop buffer integration reduces the power consumption for on-chip memory access. The SoC is implemented in 0.18 μm CMOS process and consumes 32 μ W from a 1.2 V while heart beat detection application is running, and integrated in a wireless ECG monitoring system with Bluetooth protocol. Thanks to the ECG SoC, the overall system power consumption can be reduced significantly.

Leveraging knowledge from physiological data: on-body heat stress risk prediction with sensor networks

The paper demonstrates that wearable sensor systems, coupled with real-time on-body processing and actuation, can enhance safety for wearers of heavy protective equipment who are subjected to harsh thermal environments by reducing risk of Uncompensable Heat Stress (UHS). The work focuses on Explosive Ordnance Disposal operatives and shows that predictions of UHS risk can be performed in real-time with sufficient accuracy for real-world use. Furthermore, it is shown that the required sensory input for such algorithms can be obtained with wearable, non-intrusive sensors. Two algorithms, one based on Bayesian nets and another on decision trees, are presented for determining the heat stress risk, considering the mean skin temperature prediction as a proxy. The algorithms are trained on empirical data and have accuracies of 92.1±2.9% and 94.4±2.1%, respectively when tested using leave-one-subject-out cross-validation. In applications such as Explosive Ordnance Disposal operative monitoring, such prediction algorithms can enable autonomous actuation of cooling systems and haptic alerts to minimize casualties.

A 0.7-V 17.4- μ W 3-lead wireless ECG SoC

This paper presents a fully integrated sub-1 V 3-lead wireless ECG System-on-Chip (SoC) for wireless body sensor network applications. The SoC includes a two-channel ECG front-end with a driven-right-leg circuit, an 8-bit SAR ADC, a custom-designed 16-bit microcontroller, two banks of 16 kb SRAM, and a MICS band transceiver. The microcontroller and SRAM blocks are able to operate at sub-/near-threshold regime for the best energy consumption. The proposed SoC has been implemented in a standard 0.13- μ m CMOS process. Measurement results show the microcontroller consumes only 2.62 pJ per instruction at 0.35 V . Both microcontroller and memory blocks are functional down to 0.25 V. The entire SoC is capable of working at single 0.7-V supply. At the best case, it consumes 17.4 μ W in heart rate detection mode and 74.8 μW in raw data acquisition mode under sampling rate of 500 Hz. This makes it one of the best ECG SoCs among state-of-the-art biomedical chips.

An application of reconfigurable technologies for non-invasive fetal heart rate extraction

This paper illustrates the use of a reconfigurable system for fetal electrocardiogram (FECG) estimation from mother's abdomen ECG measurements. The system is based on two different reconfigurable devices. Initially, a field-programmable analog array (FPAA) device implements the analog reconfigurable preprocessing for ECG signal acquisition. The signal processing chain continues onto a field-programmable gate array (FPGA) device, which contains all the communication and interfacing protocols along with specific digital signal processing blocks required for fundamental period extraction from FECG waveforms. The synergy between these devices provides the system the ability to change any necessary parameter during the acquisition process for enhancing the result. The use of a FPGA allows implementing different algorithms for FECG signal extraction, such as adaptive signal filtering. Preliminary works employ commercially available development platforms for test experiments, which suffice for the processing of real FECG signals from biomedical databases, as the presented results illustrate.