A multiparameter wearable physiologic monitoring system for space and terrestrial applications

Skin sensing and wearable technology as tools to measure atopic dermatitis severity

Wearable medical technology encompasses a range of electronic devices that act as biosensors. Atopic dermatitis (AD) is the commonest inflammatory skin disease and represents an important area of need in which to leverage the power of wearable biosensor technology, especially as the impact of COVID-19 increases the likelihood of virtual consultations becoming an integrated part of clinical practice. The aim of this review is to systematically define the published evidence for the utility of wearable biosensors in assessment and management of atopic dermatitis (AD). A systematic literature search was conducted for publications from 1995 onwards for 'sensor' OR 'sensing' OR 'biosensor' OR 'biomarker'. Results were combined ('AND') with a search for 'wearable' OR 'actigraphy' OR 'Internet of things' OR 'microneedle' OR 'patch' OR 'e-textile' OR 'smart textile' and atopic dermatitis (MESH terms). Fifty seven abstracts were identified from the database search of which 39 were selected for detailed review. Broadly, wearable sensing systems in atopic dermatitis were split into three categories: wearable biosensor modules (actigraphy and smartwatches), clothing and integrated fabrics placed onto the epidermis and intradermal or subcutaneous sensors. The best evidence for correlation with AD disease severity was with actigraphy measurements of itch. However, newer approaches including sensing skin barrier function, inflammation and small molecule analysis as well as employing artificial intelligence offer more potential for advanced disease monitoring. Skin diseases, specifically AD, stand to benefit greatly from wearable technology, because of the ease of direct contact to the skin, the high prevalence of the disease and the large unmet need for better disease control in this group. However, important emphasis must be placed on validating the correlation of data from such technology with patient-reported outcomes. Wearable biosensors offer a huge potential to deliver better diagnostics, monitoring and treatment outcomes for patients.

Cardiorespiratory Sensors and Their Implications for Out-of-Hospital Cardiac Arrest Detection: A Systematic Review

Out-of-hospital cardiac arrest (OHCA) is a major health problem, with a poor survival rate of 2-11%. For the roughly 75% of OHCAs that are unwitnessed, survival is approximately 2-4.4%, as there are no bystanders present to provide life-saving interventions and alert Emergency Medical Services. Sensor technologies may reduce the number of unwitnessed OHCAs through automated detection of OHCA-associated physiological changes. However, no technologies are widely available for OHCA detection. This review identifies research and commercial technologies developed for cardiopulmonary monitoring that may be best suited for use in the context of OHCA, and provides recommendations for technology development, testing, and implementation. We conducted a systematic review of published studies along with a search of grey literature to identify technologies that were able to provide cardiopulmonary monitoring, and could be used to detect OHCA. We searched MEDLINE, EMBASE, Web of Science, and Engineering Village using MeSH keywords. Following inclusion, we summarized trends and findings from included studies. Our searches retrieved 6945 unique publications between January, 1950 and May, 2023. 90 studies met the inclusion criteria. In addition, our grey literature search identified 26 commercial technologies. Among included technologies, 52% utilized electrocardiography (ECG) and 40% utilized photoplethysmography (PPG) sensors. Most wearable devices were multi-modal (59%), utilizing more than one sensor simultaneously. Most included devices were wearable technologies (84%), with chest patches (22%), wrist-worn devices (18%), and garments (14%) being the most prevalent. ECG and PPG sensors are heavily utilized in devices for cardiopulmonary monitoring that could be adapted to OHCA detection. Developers seeking to rapidly develop methods for OHCA detection should focus on using ECG- and/or PPG-based multimodal systems as these are most prevalent in existing devices. However, novel sensor technology development could overcome limitations in existing sensors and could serve as potential additions to or replacements for ECG- and PPG-based devices.

Development of a low-cost, compact, wireless, 16 - channel biopotential data acquisition, signal conditioning and arbitrary waveform stimulator

The health and fitness of the human body rely heavily on physiological parameters. These parameters can be measured using various tools such as ECG, EMG, EEG, EOG, among others, to obtain real-time physiological data. Analysing the bio-signals obtained from these measurements can provide valuable information that can be used to improve health-care in terms of observation, diagnosis, and treatment. In bio-signal pattern recognition applications, more channels provide multiple information simultaneously. Different biosignal acquisition devices are available in the market, most of which are designed for specific signals like ECG, EMG, EEG etc The gain of the amplifiers and frequency of the filters are designed as per the targeted signals; due to which one device cannot be used for other signals. Also, most of the systems are wired system which is not comfortable for animal studies. In this paper, a low-cost, compact, wireless, 16 channel biopotential data acquisition system with integrated electrical stimulator is designed and implemented. There are several novel and flexible design approaches were incorporated in the proposed design like (1) It has user selectable digital filter in each channel based on the signal frequencies like ECG, EMG, EEG, EOG. The same system will be used to acquire different signals simultaneously. (2) It has variable gain with a configurable analog bandpass filter. (3) It can acquire signals from 4 patients simultaneously. (4) The system is capable to acquire signal from both two-electrode as well as three-electrode configurations. (5) It has integrated stimulator with trapezoidal, charge-balanced, biphasic stimulus output with near zero DC level and user selectable pulse duration or frequency of the stimulus. The developed system has the ability to acquire and transmit data wirelessly in real-time at a high transfer rate. To validate the performance of the system, tests were conducted on the acquired signals using a simulator.

Comparison of Slate Safety Wearable Device to Ingestible Pill and Wearable Heart Rate Monitor

BACKGROUND

With the increase in concern for deaths and illness related to the increase in temperature globally, there is a growing need for real-time monitoring of workers for heat stress indicators. The purpose of this study was to determine the usability of the Slate Safety (SS) wearable physiological monitoring system.

METHODS

Twenty nurses performed a common task in a moderate or hot environment while wearing the SS device, the Polar 10 monitor, and having taken the e-Celsius ingestible pill. Data from each device was compared for correlation and accuracy.

RESULTS

High correlation was determined between the SS wearable device and the Polar 10 system (0.926) and the ingestible pill (0.595). The SS was comfortable to wear and easily monitored multiple participants from a distance.

CONCLUSIONS

The Slate Safety wearable device demonstrated accuracy in measuring core temperature and heart rate while not restricting the motion of the worker, and provided a remote monitoring platform for physiological parameters.

A Review of Stimuli-Responsive Smart Materials for Wearable Technology in Healthcare: Retrospective, Perspective, and Prospective

In recent years thanks to the Internet of Things (IoT), the demand for the development of miniaturized and wearable sensors has skyrocketed. Among them, novel sensors for wearable medical devices are mostly needed. The aim of this review is to summarize the advancements in this field from current points of view, focusing on sensors embedded into textile fabrics. Indeed, they are portable, lightweight, and the best candidates for monitoring biometric parameters. The possibility of integrating chemical sensors into textiles has opened new markets in smart clothing. Many examples of these systems are represented by color-changing materials due to their capability of altering optical properties, including absorption, reflectance, and scattering, in response to different external stimuli (temperature, humidity, pH, or chemicals). With the goal of smart health monitoring, nanosized sol-gel precursors, bringing coupling agents into their chemical structure, were used to modify halochromic dyestuffs, both minimizing leaching from the treated surfaces and increasing photostability for the development of stimuli-responsive sensors. The literature about the sensing properties of functionalized halochromic azo dyestuffs applied to textile fabrics is reviewed to understand their potential for achieving remote monitoring of health parameters. Finally, challenges and future perspectives are discussed to envisage the developed strategies for the next generation of functionalized halochromic dyestuffs with biocompatible and real-time stimuli-responsive capabilities.

Generating Alerts from Breathing Pattern Outliers

Analysing human physiological data allows access to the health state and the state of mind of the subject individual. Whenever a person is sick, having a panic attack, happy or scared, physiological signals will be different. In terms of physiological signals, we focus, in this manuscript, on monitoring breathing patterns. The scope can be extended to also address heart rate and other variables. We describe an analysis of breathing rate patterns during activities including resting, walking, running and watching a movie. We model normal breathing behaviours by statistically analysing signals, processed to represent quantities of interest. We consider moving maximum/minimum, the amplitude and the Fourier transform of the respiration signal, working with different window sizes. We then learn a statistical model for the basal behaviour, per individual, and detect outliers. When outliers are detected, a system that incorporates our approach would send a visible signal through a smart garment or through other means. We describe alert generation performance in two datasets-one literature dataset and one collected as a field study for this work. In particular, when learning personal rest distributions for the breathing signals of 14 subjects, we see alerts generated more often when the same individual is running than when they are tested in rest conditions.

Deployment of Wireless Sensor Network and IoT Platform to Implement an Intelligent Animal Monitoring System

This study aimed to realize Sustainable Development Goals (SDGs), i.e., no poverty, zero hunger, and sustainable cities and communities through the implementation of an intelligent cattle-monitoring system to enhance dairy production. Livestock industries in developing countries lack the technology that can directly impact meat and dairy products, where human resources are a major factor. This study proposed a novel, cost-effective, smart dairy-monitoring system by implementing intelligent wireless sensor nodes, the Internet of Things (IoT), and a Node-Micro controller Unit (Node-MCU). The proposed system comprises three modules, including an intelligent environmental parameter regularization system, a cow collar (equipped with a temperature sensor, a GPS module to locate the animal, and a stethoscope to update the heart rate), and an automatic water-filling unit for drinking water. Furthermore, a novel IoT-based front end has been developed to take data from prescribed modules and maintain a separate database for further analysis. The presented Wireless Sensor Nodes (WSNs) can intelligently determine the case of any instability in environmental parameters. Moreover, the cow collar is designed to obtain precise values of the temperature, heart rate, and accurate location of the animal. Additionally, auto-notification to the concerned party is a valuable addition developed in the cow collar design. It employed a plug-and-play design to provide ease in implementation. Moreover, automation reduces human intervention, hence labor costs are decreased when a farm has hundreds of animals. The proposed system also increases the production of dairy and meat products by improving animal health via the regularization of the environment and automated food and watering. The current study represents a comprehensive comparative analysis of the proposed implementation with the existing systems that validate the novelty of this work. This implementation can be further stretched for other applications, i.e., smart monitoring of zoo animals and poultry.

Research on Digital Technology Use in Cardiology: Bibliometric Analysis

Background

Digital technology uses in cardiology have become a popular research focus in recent years. However, there has been no published bibliometric report that analyzed the corresponding academic literature in order to derive key publishing trends and characteristics of this scientific area.

Objective

We used a bibliometric approach to identify and analyze the academic literature on digital technology uses in cardiology, and to unveil popular research topics, key authors, institutions, countries, and journals. We further captured the cardiovascular conditions and diagnostic tools most commonly investigated within this field.

Methods

The Web of Science electronic database was queried to identify relevant papers on digital technology uses in cardiology. Publication and citation data were acquired directly from the database. Complete bibliographic data were exported to VOSviewer, a dedicated bibliometric software package, and related to the semantic content of titles, abstracts, and keywords. A term map was constructed for findings visualization.

Results

The analysis was based on data from 12,529 papers. Of the top 5 most productive institutions, 4 were based in the United States. The United States was the most productive country (4224/12,529, 33.7%), followed by United Kingdom (1136/12,529, 9.1%), Germany (1067/12,529, 8.5%), China (682/12,529, 5.4%), and Italy (622/12,529, 5.0%). Cardiovascular diseases that had been frequently investigated included hypertension (152/12,529, 1.2%), atrial fibrillation (122/12,529, 1.0%), atherosclerosis (116/12,529, 0.9%), heart failure (106/12,529, 0.8%), and arterial stiffness (80/12,529, 0.6%). Recurring modalities were electrocardiography (170/12,529, 1.4%), angiography (127/12,529, 1.0%), echocardiography (127/12,529, 1.0%), digital subtraction angiography (111/12,529, 0.9%), and photoplethysmography (80/12,529, 0.6%). For a literature subset on smartphone apps and wearable devices, the Journal of Medical Internet Research (20/632, 3.2%) and other JMIR portfolio journals (51/632, 8.0%) were the major publishing venues.

Conclusions

Digital technology uses in cardiology target physicians, patients, and the general public. Their functions range from assisting diagnosis, recording cardiovascular parameters, and patient education, to teaching laypersons about cardiopulmonary resuscitation. This field already has had a great impact in health care, and we anticipate continued growth.

Application of Wearable Sensors in the Treatment of Cervical Spondylosis Radiculopathy with Acupuncture

Research shows that cervical spondylosis radiculopathy (CSR) is the most common type of cervical spondylosis in clinic, and Chinese medicine treatment has obvious advantages, among which acupuncture therapy has received increasing attention. CSR has the characteristics of high incidence, long treatment time, and easy recurrence after treatment. In order to meet the different needs of different patients, this paper uses wearable sensors to collect patient dynamic data, extracts the action features of cervical spondylosis to design a scoring system, analyzes the input feature scores through a convolutional neural network (CNN) model, and then outputs personalized acupuncture treatment plan. The development status of wearable sensors at home and abroad is introduced, and the modules and functions of the wearable sensors are designed. The CNN network is used as the network model for classification and recognition. The experimental results show that the CNN model used in this paper has a high classification accuracy, achieving an accuracy of up to 97%, and can help produce an effective treatment plan. In order to determine whether the treatment plan output by the model is effective, each group of data is handed over to two cervical spondylosis experts for scoring, and then the final treatment plan is determined from 10 acupuncture plans. In our experiments, 9 out of 10 plans generated by the CNN model were the same as generated by the experts, which shows the effectiveness of the model.

Security Threats and Cryptographic Protocols for Medical Wearables

In the past few years, the use of several medical devices is increasing. This paper will pay attention to a device developed to get measures of the temperature of diabetic foot. These wearables usually do not have cryptographic protocols to guarantee data security. This study analyzes the existing security in these devices, and simulate malware propagation taking into account the vulnerabilities and lack of security in these highly-constrained interconnected devices. A simulation of malware spreading in a network made by 10 and 15 individuals with 6 and 34 sensors each one, respectively, is included in this study. To avoid such attacks, a lightweight cryptographic protocol could be a satisfactory solution. Considering the quick development of quantum computers, several current cryptographic protocols have been compromised.

Data Augmentation and Transfer Learning for Data Quality Assessment in Respiratory Monitoring

Changes in respiratory rate have been found to be one of the early signs of health deterioration in patients. In remote environments where diagnostic tools and medical attention are scarce, such as deep space exploration, the monitoring of the respiratory signal becomes crucial to timely detect life-threatening conditions. Nowadays, this signal can be measured using wearable technology; however, the use of such technology is often hampered by the low quality of the recordings, which leads more often to wrong diagnosis and conclusions. Therefore, to apply these data in diagnosis analysis, it is important to determine which parts of the signal are of sufficient quality. In this context, this study aims to evaluate the performance of a signal quality assessment framework, where two machine learning algorithms (support vector machine-SVM, and convolutional neural network-CNN) were used. The models were pre-trained using data of patients suffering from chronic obstructive pulmonary disease. The generalization capability of the models was evaluated by testing them on data from a different patient population, presenting normal and pathological breathing. The new patients underwent bariatric surgery and performed a controlled breathing protocol, displaying six different breathing patterns. Data augmentation (DA) and transfer learning (TL) were used to increase the size of the training set and to optimize the models for the new dataset. The effect of the different breathing patterns on the performance of the classifiers was also studied. The SVM did not improve when using DA, however, when using TL, the performance improved significantly (p < 0.05) compared to DA. The opposite effect was observed for CNN, where the biggest improvement was obtained using DA, while TL did not show a significant change. The models presented a low performance for shallow, slow and fast breathing patterns. These results suggest that it is possible to classify respiratory signals obtained with wearable technologies using pre-trained machine learning models. This will allow focusing on the relevant data and avoid misleading conclusions because of the noise, when designing bio-monitoring systems.

A Dynamic Light-Weight Symmetric Encryption Algorithm for Secure Data Transmission via BLE Beacons

Pervasive sensing with Body Sensor Networks (BSNs) is a promising technology for continuous health monitoring. Since the sensor nodes are resource-limited, on-node processing and advertisement of digested information via BLE beacon is a promising technique that can enable a node gateway to communicate with more sensor nodes and extend the sensor node’s lifetime before requiring recharging. This study proposes a Dynamic Light-weight Symmetric (DLS) encryption algorithm designed and developed to address the challenges in data protection and real-time secure data transmission via message advertisement. The algorithm uses a unique temporal encryption key to encrypt each transmitting packet with a simple function such as XOR. With small additional overhead on computational resources, DLS can significantly enhance security over existing baseline encryption algorithms. To evaluate its performance, the algorithm was utilized on beacon data encryption over advertising channels. The experiments demonstrated the use of the DLS encryption algorithm on top of various light-weight symmetric encryption algorithms (i.e., TEA, XTEA, PRESENT) and a MD5 hash function. The experimental results show that DLS can achieve acceptable results for avalanche effect, key sensitivity, and randomness in ciphertexts with a marginal increase in the resource usage. The proposed DLS encryption algorithm is suitable for implementation at the application layer, is light and energy efficient, reduces/removes the need for secret key exchange between sensor nodes and the server, is applicable to dynamic message size, and also protects against attacks such as known plaintext attack, brute-force attack, replaying attack, and differential attack.

Considerations for Wearable Sensors to Monitor Physical Performance During Spaceflight Intravehicular Activities

Wearable sensors provide the capability to noninvasively monitor physiological parameters during spaceflight, including those related to physical performance and daily activity. Regular monitoring of general health and exercise capabilities in astronauts can ensure adequate performance levels and record health changes caused by the space environment. Relevant measurables include vital signs, cardiovascular health, and activity monitoring. Wearable sensor devices can be comfortable for long-term use and easy to operate, which is particularly important during more autonomous future planetary missions. Many devices are currently being developed and tested, but few wearable devices or integrated "smart" garments have been assigned for regular use on the International Space Station. The unique needs of the space environment must be considered to facilitate the development and implementation of wearable devices, particularly "smart" sensor garments, for space applications.

Sensor Networks for Aerospace Human-Machine Systems

Intelligent automation and trusted autonomy are being introduced in aerospace cyber-physical systems to support diverse tasks including data processing, decision-making, information sharing and mission execution. Due to the increasing level of integration/collaboration between humans and automation in these tasks, the operational performance of closed-loop human-machine systems can be enhanced when the machine monitors the operator's cognitive states and adapts to them in order to maximise the effectiveness of the Human-Machine Interfaces and Interactions (HMI²). Technological developments have led to neurophysiological observations becoming a reliable methodology to evaluate the human operator's states using a variety of wearable and remote sensors. The adoption of sensor networks can be seen as an evolution of this approach, as there are notable advantages if these sensors collect and exchange data in real-time, while their operation is controlled remotely and synchronised. This paper discusses recent advances in sensor networks for aerospace cyber-physical systems, focusing on Cognitive HMI² (CHMI²) implementations. The key neurophysiological measurements used in this context and their relationship with the operator's cognitive states are discussed. Suitable data analysis techniques based on machine learning and statistical inference are also presented, as these techniques allow processing both neurophysiological and operational data to obtain accurate cognitive state estimations. Lastly, to support the development of sensor networks for CHMI² applications, the paper addresses the performance characterisation of various state-of-the-art sensors and the propagation of measurement uncertainties through a machine learning-based inference engine. Results show that a proper sensor selection and integration can support the implementation of effective human-machine systems for various challenging aerospace applications, including Air Traffic Management (ATM), commercial airliner Single-Pilot Operations (SIPO), one-to-many Unmanned Aircraft Systems (UAS), and space operations management.

Review on Wearables to Monitor Foot Temperature in Diabetic Patients

One of the diseases that could affect diabetic patients is the diabetic foot problem. Unnoticed minor injuries and subsequent infection can lead to ischemic ulceration, and may end in a foot amputation. Preliminary studies have shown that there is a positive relationship between increased skin temperature and the pre⁻ulceration phase. Hence, we have carried out a review on wearables, medical devices, and sensors used specifically for collecting vital data. In particular, we are interested in the measure of the foot⁻temperature. Since there is a large amount of this type of medical wearables, we will focus on those used to measure temperature and developed in Spain.

Realization and Technology Acceptance Test of a Wearable Cardiac Health Monitoring and Early Warning System with Multi-Channel MCGs and ECG

In this work, a wearable smart clothing system for cardiac health monitoring with a multi-channel mechanocardiogram (MCG) has been developed to predict the myo-cardiac left ventricular ejection fraction (LVEF) function and to provide early risk warnings to the subjects. In this paper, the realization of the core of this system, i.e., the Cardiac Health Assessment and Monitoring Platform (CHAMP), with respect to its hardware, firmware, and wireless design features, is presented. The feature values from the CHAMP system have been correlated with myo-cardiac functions obtained from actual heart failure (HF) patients. The usability of this MCG-based cardiac health monitoring smart clothing system has also been evaluated with technology acceptance model (TAM) analysis and the results indicate that the subject shows a positive attitude toward using this wearable MCG-based cardiac health monitoring and early warning system.

BRAINsens: Body-Worn Reconfigurable Architecture of Integrated Network Sensors

Body sensor network (BSN) is a promising human-centric technology to monitor neurophysiological data. We propose a fully-reconfigurable architecture that addresses the major challenges of a heterogenous BSN, such as scalabiliy, modularity and flexibility in deployment. Existing BSNs especially with Electroencephalogarm (EEG) have these limitations mainly due to the use of driven-right-leg (DRL) circuit. We address these limitations by custom-designing DRL-less EEG smart sensing nodes (SSN) for modular and spatially distributed systems. Each single-channel EEG SSN with a input-referred noise of 0.82 μV_rms and CMRR of 70 dB (at 60 Hz), samples brain signals at 512 sps. SSNs in the network can be configured at the time of deployment and can process information locally to significantly reduce data payload of the network. A Control Command Node (CCN) initializes, synchronizes, periodically scans for the available SSNs in the network, aggregates their data and sends it wirelessly to a paired device at a baud rate of 115.2 kbps. At the given settings of the I²C bus speed of 100 kbps, CCN can configure up to 39 EEG SSNs in a lego-like platform. The temporal and frequency-domain performance of the designed "DRL-less" EEG SSNs is evaluated against a research-grade Neuroscan and consumer-grade Emotiv EPOC EEG. The results show that the proposed network system with wearable EEG can be deployed in situ for continuous brain signal recording in real-life scenarios. The proposed system can also seamlessly incorporate other physiological SSNs for ECG, HRV, temperature etc. along with EEG within the same topology.

Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks

Determining and improving the wellbeing of people is one of the priorities of the OECD countries. Nowadays many sensors allow monitoring different parameters in regard to the wellbeing of people. These sensors can be deployed in smartphones, clothes or accessories like watches. Many studies have been performed on wearable devices that monitor certain aspects of the health of people, especially for specific diseases. In this paper, we propose a non-invasive low-cost and low-energy physical wellbeing monitoring system that provides a wellness score based on the obtained data. We present the architecture of the system and the disposition of the sensors on the sock. The algorithm of the system is presented as well. The wellness threshold evaluation module allows determining if the monitored parameter is within healthy ranges. The message forwarding module allows decreasing the energy consumption of the system by detecting the presence of alerts or changes in the data. Finally, a simulation was performed in order to determine the energy consumption of the system. Results show that our algorithm allows saving 44.9% of the initial energy in 10,000 min for healthy people.

Sensor Based Smart Real Time Monitoring of Patients Conditions Using Wireless Protocol

This article describes how physiological signal monitoring plays an important role in identifying the health condition of heart. In recent years, online monitoring and processing of biomedical signals play a major role in accurate clinical diagnosis. Therefore, there is a requirement for the developing of online monitoring systems that will be helpful for physicians to avoid mistakes. This article focuses on the method for real time acquisition of an ECG and PPG signal and it's processing and monitoring for tele-health applications. This article also presents the real time peak detection of ECG and PPG for vital parameters measurement. The implementation and design of the proposed wireless monitoring system can be done using a graphical programming environment that utilizes less power and a minimized area with reasonable speed. The advantages of the proposed work are very simple, low cost, easy integration with programming environment and continuous monitoring of physiological signals.

Advanced biosensors for monitoring astronauts' health during long-duration space missions

Long-duration space missions pose important health concerns for astronauts, especially regarding the adverse effects of microgravity and exposure to high-energy cosmic rays. The long-term maintenance of crew health and performance mainly relies on prevention, early diagnoses, condition management, and medical interventions in situ. In-flight biosensor diagnostic devices and medical procedures must use few resources and operate in a microgravity environment, which complicates the collection and management of biological samples. Moreover, the biosensors must be certified for in-flight operation according to strict design and safety regulations. Herein, we report on the state of the art and recent advances in biosensing diagnostic instrumentation for monitoring astronauts' health during long-duration space missions, including portable and wearable biosensors. We discuss perspectives on new-format biosensors in autonomous space clinics. We also describe our own work in developing biosensing devices for non-invasively diagnosing space-related diseases, and how they are used in long-duration missions. Finally, we discuss the benefits of space exploration for Earth-based medicine.

On the Design of an Efficient Cardiac Health Monitoring System Through Combined Analysis of ECG and SCG Signals

Cardiovascular disease (CVD) is a major public concern and socioeconomic problem across the globe. The popular high-end cardiac health monitoring systems such as magnetic resonance imaging (MRI), computerized tomography scan (CT scan), and echocardiography (Echo) are highly expensive and do not support long-term continuous monitoring of patients without disrupting their activities of daily living (ADL). In this paper, the continuous and non-invasive cardiac health monitoring using unobtrusive sensors is explored aiming to provide a feasible and low-cost alternative to foresee possible cardiac anomalies in an early stage. It is learned that cardiac health monitoring based on sole usage of electrocardiogram (ECG) signals may not provide powerful insights as ECG provides shallow information on various cardiac activities in the form of electrical impulses only. Hence, a novel low-cost, non-invasive seismocardiogram (SCG) signal along with ECG signals are jointly investigated for the robust cardiac health monitoring. For this purpose, the in-laboratory data collection model is designed for simultaneous acquisition of ECG and SCG signals followed by mechanisms for the automatic delineation of relevant feature points in acquired ECG and SCG signals. In addition, separate feature points based novel approach is adopted to distinguish between normal and abnormal morphology in each ECG and SCG cardiac cycle. Finally, a combined analysis of ECG and SCG is carried out by designing a Naïve Bayes conditional probability model. Experiments on Institutional Review Board (IRB) approved licensed ECG/SCG signals acquired from real subjects containing 12,000 cardiac cycles show that the proposed feature point delineation mechanisms and abnormal morphology detection methods consistently perform well and give promising results. In addition, experimental results show that the combined analysis of ECG and SCG signals provide more reliable cardiac health monitoring compared to the standalone use of ECG and SCG.

A Cardiac Early Warning System with Multi Channel SCG and ECG Monitoring for Mobile Health

Use of information and communication technology such as smart phone, smart watch, smart glass and portable health monitoring devices for healthcare services has made Mobile Health (mHealth) an emerging research area. Coronary Heart Disease (CHD) is considered as a leading cause of death world wide and an increasing number of people die prematurely due to CHD. Under such circumstances, there is a growing demand for a reliable cardiac monitoring system to catch the intermittent abnormalities and detect critical cardiac behaviors which lead to sudden death. Use of mobile devices to collect Electrocardiography (ECG), Seismocardiography (SCG) data and efficient analysis of those data can monitor a patient’s cardiac activities for early warning. This paper presents a novel cardiac data acquisition method and combined analysis of Electrocardiography (ECG) and multi channel Seismocardiography (SCG) data. An early warning system is implemented to monitor the cardiac activities of a person and accuracy assessment of the early warning system is conducted for the ECG data only. The assessment shows 88% accuracy and effectiveness of our proposed analysis, which implies the viability and applicability of the proposed early warning system.

Characterization of Textile-Insulated Capacitive Biosensors

Capacitive biosensors are an emerging technology revolutionizing wearable sensing systems and personal healthcare devices. They are capable of continuously measuring bioelectrical signals from the human body while utilizing textiles as an insulator. Different textile types have their own unique properties that alter skin-electrode capacitance and the performance of capacitive biosensors. This paper aims to identify the best textile insulator to be used with capacitive biosensors by analysing the characteristics of 6 types of common textile materials (cotton, linen, rayon, nylon, polyester, and PVC-textile) while evaluating their impact on the performance of a capacitive biosensor. A textile-insulated capacitive (TEX-C) biosensor was developed and validated on 3 subjects. Experimental results revealed that higher skin-electrode capacitance of a TEX-C biosensor yields a lower noise floor and better signal quality. Natural fabric such as cotton and linen were the two best insulating materials to integrate with a capacitive biosensor. They yielded the lowest noise floor of 2 mV and achieved consistent electromyography (EMG) signals measurements throughout the performance test.

Power optimisation for wearable heart rate measurement device with wireless charging

Continuous measurement of heart rate is necessary for monitoring the patients with heart ailments. A wearable which continuously measures heart rate of an individual by a method called reflectance-based photoplethysmography (PPG) computes the heart rate of an individual according to the volumetric changes in blood flowing through the body is developed. In order to make the device more compact as well as with IP67 and IP68 standard, wireless charging technique is employed because it helps to get rid of wires while charging. Following the Qi standard for designing wireless power receiver circuits makes the device interoperable and work with greater efficiency with reduced losses. Impedance matching and designing the circuit to operate under resonance condition increases coupling efficiency in case of inductive coupling.

PlaIMoS: A Remote Mobile Healthcare Platform to Monitor Cardiovascular and Respiratory Variables

The number of elderly and chronically ill patients has grown significantly over the past few decades as life expectancy has increased worldwide, leading to increased demands on the health care system and significantly taxing traditional health care practices. Consequently, there is an urgent need to use technology to innovate and more constantly and intensely monitor, report and analyze critical patient physiological parameters beyond conventional clinical settings in a more efficient and cost effective manner. This paper presents a technological platform called PlaIMoS which consists of wearable sensors, a fixed measurement station, a network infrastructure that employs IEEE 802.15.4 and IEEE 802.11 to transmit data with security mechanisms, a server to analyze all information collected and apps for iOS, Android and Windows 10 mobile operating systems to provide real-time measurements. The developed architecture, designed primarily to record and report electrocardiogram and heart rate data, also monitors parameters associated with chronic respiratory illnesses, including patient blood oxygen saturation and respiration rate, body temperature, fall detection and galvanic resistance.

Performance Optimization of Priority Assisted CSMA/CA Mechanism of 802.15.6 under Saturation Regime

Due to the recent development in the field of Wireless Sensor Networks (WSNs), the Wireless Body Area Networks (WBANs) have become a major area of interest for the developers and researchers. Human body exhibits postural mobility due to which distance variation occurs and the status of connections amongst sensors change time to time. One of the major requirements of WBAN is to prolong the network lifetime without compromising on other performance measures, i.e., delay, throughput and bandwidth efficiency. Node prioritization is one of the possible solutions to obtain optimum performance in WBAN. IEEE 802.15.6 CSMA/CA standard splits the nodes with different user priorities based on Contention Window (CW) size. Smaller CW size is assigned to higher priority nodes. This standard helps to reduce delay, however, it is not energy efficient. In this paper, we propose a hybrid node prioritization scheme based on IEEE 802.15.6 CSMA/CA to reduce energy consumption and maximize network lifetime. In this scheme, optimum performance is achieved by node prioritization based on CW size as well as power in respective user priority. Our proposed scheme reduces the average back off time for channel access due to CW based prioritization. Additionally, power based prioritization for a respective user priority helps to minimize required number of retransmissions. Furthermore, we also compare our scheme with IEEE 802.15.6 CSMA/CA standard (CW assisted node prioritization) and power assisted node prioritization under postural mobility in WBAN. Mathematical expressions are derived to determine the accurate analytical model for throughput, delay, bandwidth efficiency, energy consumption and life time for each node prioritization scheme. With the intention of analytical model validation, we have performed the simulations in OMNET++/MIXIM framework. Analytical and simulation results show that our proposed hybrid node prioritization scheme outperforms other node prioritization schemes in terms of average network delay, average throughput, average bandwidth efficiency and network lifetime.

Graphite Based Electrode for ECG Monitoring: Evaluation under Freshwater and Saltwater Conditions

We proposed new electrodes that are applicable for electrocardiogram (ECG) monitoring under freshwater- and saltwater-immersion conditions. Our proposed electrodes are made of graphite pencil lead (GPL), a general-purpose writing pencil. We have fabricated two types of electrode: a pencil lead solid type (PLS) electrode and a pencil lead powder type (PLP) electrode. In order to assess the qualities of the PLS and PLP electrodes, we compared their performance with that of a commercial Ag/AgCl electrode, under a total of seven different conditions: dry, freshwater immersion with/without movement, post-freshwater wet condition, saltwater immersion with/without movement, and post-saltwater wet condition. In both dry and post-freshwater wet conditions, all ECG-recorded PQRST waves were clearly discernible, with all types of electrodes, Ag/AgCl, PLS, and PLP. On the other hand, under the freshwater- and saltwater-immersion conditions with/without movement, as well as post-saltwater wet conditions, we found that the proposed PLS and PLP electrodes provided better ECG waveform quality, with significant statistical differences compared with the quality provided by Ag/AgCl electrodes.

Implementation of Context Aware e-Health Environments Based on Social Sensor Networks

In this work, context aware scenarios applied to e-Health and m-Health in the framework of typical households (urban and rural) by means of deploying Social Sensors will be described. Interaction with end-users and social/medical staff is achieved using a multi-signal input/output device, capable of sensing and transmitting environmental, biomedical or activity signals and information with the aid of a combined Bluetooth and Mobile system platform. The devices, which play the role of Social Sensors, are implemented and tested in order to guarantee adequate service levels in terms of multiple signal processing tasks as well as robustness in relation with the use wireless transceivers and channel variability. Initial tests within a Living Lab environment have been performed in order to validate overall system operation. The results obtained show good acceptance of the proposed system both by end users as well as by medical and social staff, increasing interaction, reducing overall response time and social inclusion levels, with a compact and moderate cost solution that can readily be largely deployed.

Tablet PC Enabled Body Sensor System for Rural Telehealth Applications

Telehealth systems benefit from the rapid growth of mobile communication technology for measuring physiological signals. Development and validation of a tablet PC enabled noninvasive body sensor system for rural telehealth application are discussed in this paper. This system includes real time continuous collection of physiological parameters (blood pressure, pulse rate, and temperature) and fall detection of a patient with the help of a body sensor unit and wireless transmission of the acquired information to a tablet PC handled by the medical staff in a Primary Health Center (PHC). Abnormal conditions are automatically identified and alert messages are given to the medical officer in real time. Clinical validation is performed in a real environment and found to be successful. Bland-Altman analysis is carried out to validate the wrist blood pressure sensor used. The system works well for all measurements.

Novel electrodes for underwater ECG monitoring

We have developed hydrophobic electrodes that provide all morphological waveforms without distortion of an ECG signal for both dry and water-immersed conditions. Our electrode is comprised of a mixture of carbon black powder (CB) and polydimethylsiloxane (PDMS). For feasibility testing of the CB/PDMS electrodes, various tests were performed. One of the tests included evaluation of the electrode-to-skin contact impedance for different diameters, thicknesses, and different pressure levels. As expected, the larger the diameter of the electrodes, the lower the impedance and the difference between the large sized CB/PDMS and the similarly-sized Ag/AgCl hydrogel electrodes was at most 200 kΩ, in favor of the latter. Performance comparison of CB/PDMS electrodes to Ag/AgCl hydrogel electrodes was carried out in three different scenarios: a dry surface, water immersion, and postwater immersion conditions. In the dry condition, no statistical differences were found for both the temporal and spectral indices of the heart rate variability analysis between the CB/PDMS and Ag/AgCl hydrogel (p > 0.05) electrodes. During water immersion, there was significant ECG amplitude reduction with CB/PDMS electrodes when compared to wet Ag/AgCl electrodes kept dry by their waterproof adhesive tape, but the reduction was not severe enough to obscure the readability of the recordings, and all morphological waveforms of the ECG signal were discernible even when motion artifacts were introduced. When water did not penetrate tape-wrapped Ag/AgCl electrodes, high fidelity ECG signals were observed. However, when water penetrated the Ag/AgCl electrodes, the signal quality degraded to the point where ECG morphological waveforms were not discernible.

The acceptance of personal health devices among patients with chronic conditions

BACKGROUND

Personal health devices (PHDs) are rapidly developing and getting smarter. But little is known about chronic patients' acceptance of such PHDs.

OBJECTIVE

The objective of this study is to explore how chronic patients accept PHDs and what are the main factors that predict use intention of PHDs. The results will provide suggestions for the design of PHDs and e-health services.

METHOD

A questionnaire survey was conducted to identify the main factors that affect chronic patients' acceptance of PHDs. Three hundred and forty-six valid responses from chronic patients were collected and the data were analyzed using exploratory factor analysis and regression analysis method. The questionnaire also included questions about respondents' experience of PHDs and preference of PHD functions. These questions help to understand lived experience of PHD users and to explain the factors that influence their use intention.

RESULT

Five influencing factors that predict use intention of PHDs were identified: attitude toward technology, perceived usefulness, ease of learning and availability, social support, and perceived pressure. An acceptance model of PHDs was proposed based on these factors, and suggestions for PHD designers and e-health service designers were discussed. The exploration of PHD experience indicated that ease of learning and social norm significantly influenced PHD use intention, and many respondents expressed negative opinions on the accuracy, durability and maintenance service of PHDs. Besides, people generally expressed positive attitude toward future functions of a PHD.

Adaptive motion artefact reduction in respiration and ECG signals for wearable healthcare monitoring systems

Wearable healthcare monitoring systems (WHMSs) have received significant interest from both academia and industry with the advantage of non-intrusive and ambulatory monitoring. The aim of this paper is to investigate the use of an adaptive filter to reduce motion artefact (MA) in physiological signals acquired by WHMSs. In our study, a WHMS is used to acquire ECG, respiration and triaxial accelerometer (ACC) signals during incremental treadmill and cycle ergometry exercises. With these signals, performances of adaptive MA cancellation are evaluated in both respiration and ECG signals. To achieve effective and robust MA cancellation, three axial outputs of the ACC are employed to estimate the MA by a bank of gradient adaptive Laguerre lattice (GALL) filter, and the outputs of the GALL filters are further combined with time-varying weights determined by a Kalman filter. The results show that for the respiratory signals, MA component can be reduced and signal quality can be improved effectively (the power ratio between the MA-corrupted respiratory signal and the adaptive filtered signal was 1.31 in running condition, and the corresponding signal quality was improved from 0.77 to 0.96). Combination of the GALL and Kalman filters can achieve robust MA cancellation without supervised selection of the reference axis from the ACC. For ECG, the MA component can also be reduced by adaptive filtering. The signal quality, however, could not be improved substantially just by the adaptive filter with the ACC outputs as the reference signals.

Are We There Yet? Feasibility of Continuous Stress Assessment via Wireless Physiological Sensors

Stress can lead to headaches and fatigue, precipitate addictive behaviors (e.g., smoking, alcohol and drug use), and lead to cardiovascular diseases and cancer. Continuous assessment of stress from sensors can be used for timely delivery of a variety of interventions to reduce or avoid stress. We investigate the feasibility of continuous stress measurement via two field studies using wireless physiological sensors - a four-week study with illicit drug users (n = 40), and a one-week study with daily smokers and social drinkers (n = 30). We find that 11+ hours/day of usable data can be obtained in a 4-week study. Significant learning effect is observed after the first week and data yield is seen to be increasing over time even in the fourth week. We propose a framework to analyze sensor data yield and find that losses in wireless channel is negligible; the main hurdle in further improving data yield is the attachment constraint. We show the feasibility of measuring stress minutes preceding events of interest and observe the sensor-derived stress to be rising prior to self-reported stress and smoking events.

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 review on architectures and communications technologies for wearable health-monitoring systems

Nowadays society is demanding more and more smart healthcare services that allow monitoring patient status in a non-invasive way, anywhere and anytime. Thus, healthcare applications are currently facing important challenges guided by the u-health (ubiquitous health) and p-health (pervasive health) paradigms. New emerging technologies can be combined with other widely deployed ones to develop such next-generation healthcare systems. The main objective of this paper is to review and provide more details on the work presented in “LOBIN: E-Textile and Wireless-Sensor-Network-Based Platform for Healthcare Monitoring in Future Hospital Environments”, published in the IEEE Transactions on Information Technology in Biomedicine, as well as to extend and update the comparison with other similar systems. As a result, the paper discusses the main advantages and disadvantages of using different architectures and communications technologies to develop wearable systems for pervasive healthcare applications.

Wearable medical systems for p-Health

Driven by the growing aging population, prevalence of chronic diseases, and continuously rising healthcare costs, the healthcare system is undergoing a fundamental transformation, from the conventional hospital-centered system to an individual-centered system. Current and emerging developments in wearable medical systems will have a radical impact on this paradigm shift. Advances in wearable medical systems will enable the accessibility and affordability of healthcare, so that physiological conditions can be monitored not only at sporadic snapshots but also continuously for extended periods of time, making early disease detection and timely response to health threats possible. This paper reviews recent developments in the area of wearable medical systems for p-Health. Enabling technologies for continuous and noninvasive measurements of vital signs and biochemical variables, advances in intelligent biomedical clothing and body area networks, approaches for motion artifact reduction, strategies for wearable energy harvesting, and the establishment of standard protocols for the evaluation of wearable medical devices are presented in this paper with examples of clinical applications of these technologies.

An embedded reconfigurable architecture for patient-specific multi-paramater medical monitoring

A robust medical monitoring device should be able to provide intelligent diagnosis based on accurate analysis of physiological parameters in real-time. At the same time, such device must be able to adapt to the characteristics of a specific patient and desired diagnostic needs, and continue to operate even in presence of unexpected artifacts and accidental errors. A reconfigurable architecture is proposed for real-time assessment of individual's health status based on development of a patient-specific health index and online analysis and fusion of multi-parameter physiological signals. This is achieved by static configuration of processing elements and communication blocks in the architecture based on the patient's diagnostic needs. The proposed architecture is prototyped as a single integrated device on an FPGA platform and is evaluated using multi-parameter data from intensive care units (ICUs). Three representative test cases of concurrently analyzing Blood Pressure, Heart Rate, and Electrocardiogram (ECG) data from MIMIC database are presented. The results show the effectiveness of the proposed technique in eliminating false alarms caused by patient movements, monitor noise, or imperfections in the detection schemes.

A review of wearable sensors and systems with application in rehabilitation

The aim of this review paper is to summarize recent developments in the field of wearable sensors and systems that are relevant to the field of rehabilitation. The growing body of work focused on the application of wearable technology to monitor older adults and subjects with chronic conditions in the home and community settings justifies the emphasis of this review paper on summarizing clinical applications of wearable technology currently undergoing assessment rather than describing the development of new wearable sensors and systems. A short description of key enabling technologies (i.e. sensor technology, communication technology, and data analysis techniques) that have allowed researchers to implement wearable systems is followed by a detailed description of major areas of application of wearable technology. Applications described in this review paper include those that focus on health and wellness, safety, home rehabilitation, assessment of treatment efficacy, and early detection of disorders. The integration of wearable and ambient sensors is discussed in the context of achieving home monitoring of older adults and subjects with chronic conditions. Future work required to advance the field toward clinical deployment of wearable sensors and systems is discussed.

Development and evaluation of an ambulatory stress monitor based on wearable sensors

Chronic stress is endemic to modern society. However, as it is unfeasible for physicians to continuously monitor stress levels, its diagnosis is nontrivial. Wireless body sensor networks offer opportunities to ubiquitously detect and monitor mental stress levels, enabling improved diagnosis, and early treatment. This article describes the development of a wearable sensor platform to monitor a number of physiological correlates of mental stress. We discuss tradeoffs in both system design and sensor selection to balance information content and wearability. Using experimental signals collected from the wearable sensor, we describe a selected number of physiological features that show good correlation with mental stress. In particular, we propose a new spectral feature that estimates the balance of the autonomic nervous system by combining information from the power spectral density of respiration and heart rate variability. We validate the effectiveness of our approach on a binary discrimination problem when subjects are placed under two psychophysiological conditions: mental stress and relaxation. When used in a logistic regression model, our feature set is able to discriminate between these two mental states with a success rate of 81% across subjects.

Wireless remote monitoring of glucose using a functionalized ZnO nanowire arrays based sensor

This paper presents a prototype wireless remote glucose monitoring system interfaced with a ZnO nanowire arrays-based glucose sensor, glucose oxidase enzyme immobilized onto ZnO nanowires in conjunction with a Nafion^® membrane coating, which can be effectively applied for the monitoring of glucose levels in diabetics. Global System for Mobile Communications (GSM) services like General Packet Radio Service (GPRS) and Short Message Service (SMS) have been proven to be logical and cost effective methods for gathering data from remote locations. A communication protocol that facilitates remote data collection using SMS has been utilized for monitoring a patient’s sugar levels. In this study, we demonstrate the remote monitoring of the glucose levels with existing GPRS/GSM network infra-structures using our proposed functionalized ZnO nanowire arrays sensors integrated with standard readily available mobile phones. The data can be used for centralized monitoring and other purposes. Such applications can reduce health care costs and allow caregivers to monitor and support to their patients remotely, especially those located in rural areas.

Wearable and implantable wireless sensor network solutions for healthcare monitoring

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

Mobile telemedicine: a survey study

Telemedicine involves the use of advanced and reliable communication techniques to deliver biomedical signals over long distances. In such systems, biomedical information is transmitted using wireline or wireless communication systems. Mobile telemedicine is an improved form of telemedicine, in which advanced wireless communication systems are used to deliver the biomedical signals of patients at any place and any time. Mobile telemedicine employs advanced concepts and techniques from the fields of electrical engineering, computer science, biomedical engineering, and medicine to overcome the restrictions involved in conventional telemedicine and realize an improvement in the quality of service of medicine. In this paper, we study several mobile telemedicine systems, and it is important to gain a good understanding of mobile telemedicine systems because in the further, such systems are expected to become ubiquitous for the delivery of biomedical signals for medicine.

A mobile monitoring system of blood pressure for underserved in China by information and communication technology service

High blood pressure (BP, hypertension) is a leading chronic condition in China and has become the main risk factor for many high-risk diseases, such as heart attacks. However, the platform for chronic disease measurement and management is still lacking, especially for underserved Chinese. To achieve the early diagnosis of hypertension, one BP monitoring system has been designed. The proposed design consists of three main parts: user domain, server domain, and channel domain. All three units and their materialization, validation tests on reliability, and usability are described in this paper, and the conclusion is that the current design concept is feasible and the system can be developed toward sufficient reliability and affordability with further optimization. This idea might also be extended into one platform for other physiological signals, such as blood sugar and ECG.

An 11 μ w, two-electrode transimpedance biosignal amplifier with active current feedback stabilization

A novel two-electrode biosignal amplifier circuit is demonstrated by using a composite transimpedance amplifier input stage with active current feedback. Micropower, low gain-bandwidth product operational amplifiers can be used, leading to the lowest reported overall power consumption in the literature for a design implemented with off-the-shelf commercial integrated circuits (11 μW). Active current feedback forces the common-mode input voltage to stay within the supply rails, reducing baseline drift and amplifier saturation problems that can be present in two-electrode systems. The bandwidth of the amplifier extends from 0.05-200 Hz and the midband voltage gain (assuming an electrode-to-skin resistance of 100 kΩ) is 48 dB. The measured output noise level is 1.2 mV pp, corresponding to a voltage signal-to-noise ratio approaching 50 dB for a typical electrocardiogram (ECG) level input of 1 mVpp. Recordings were taken from a subject by using the proposed two-electrode circuit and, simultaneously, a three-electrode standard ECG circuit. The residual of the normalized ensemble averages for both measurements was computed, and the power of this residual was 0.54% of the power of the standard ECG measurement output. While this paper primarily focuses on ECG applications, the circuit can also be used for amplifying other biosignals, such as the electroencephalogram.