An armband wearable device for overnight and cuff-less blood pressure measurement

Comparative Design Study for Power Reduction in Organic Optoelectronic Pulse Meter Sensor

This paper demonstrated a new design structure for minimizing the power consumption of a pulse meter. Monolithic devices composed of a red (625 nm) organic light-emitting diode (OLED) and an organic photodiode (OPD) were fabricated on the same substrate. Two organic devices were designed differently. One had a circle-shaped OLED in the center of the device and was surrounded by the OPD, while the other had the opposite structure. The external quantum efficiency (EQE) of the OLED and the OPD were 7% and 37%, respectively. We evaluated and compared the signal-to-noise ratio (SNR) of the photoplethysmogram (PPG) signal on different parts of the body and successfully acquired clear PPG signals at those positions, where the best signal was obtained from the fingertip at a SNR of about 62 dB. The proposed organic pulse meter sensor was operated successfully with a power consumption of 0.1 mW. Eventually, the proposed organic biosensor reduced the power consumption and improved the capability of the pulse meter for long-term use.

Multi-Sensor Fusion Approach for Cuff-Less Blood Pressure Measurement

Ambulatory blood pressure (BP) provides valuable information for cardiovascular risk assessment. The present cuff-based devices are intrusive for long-term BP monitoring, whereas cuff-less BP measurement methods based on pulse transit time or multi-parameter are inferior in robustness and reliability by using electrocardiogram (ECG) and photoplethysmogram signals. This study examined a multi-sensor fusion-based platform and algorithm for systolic BP (SBP), mean arterial pressure (MAP), and diastolic BP (DBP) estimation. The proposed multi-sensor platform was comprised of one ECG sensor and two pulse pressure wave sensors for simultaneous signal collection. After extracting 35 features from the collected signals, a weakly supervised feature selection method was proposed for dimension reduction because the reference oscillometric technique-based BP are intermittent and can be redeemed as coarse-grained labels. BP models were then established using a multi-instance regression algorithm. A total of 85 participants including 17 hypertensive and 12 hypotensive patients were enrolled. Experimental results showed that the proposed approach exhibited good accuracy for diverse population with an estimation error of 1.62 ± 7.76 mmHg for SBP, 1.53 ± 6.03 mmHg for MAP, and 1.49 ± 5.52 for DBP, which complied with the association for the advancement of medical instrumentation standards in BP estimation. Moreover, the estimation accuracy is with random daily fluctuations rather than long-term degradation through a maximum two-month follow-up period indicated good robustness performance. These results suggest that the proposed approach is with high reliability and robustness and thus provides a novel insight for cuff-less BP measurement.

Real-Time Healthcare Data Transmission for Remote Patient Monitoring in Patch-Based Hybrid OCC/BLE Networks

Research on electronic healthcare (eHealth) systems has increased dramatically in recent years. eHealth represents a significant example of the application of the Internet of Things (IoT), characterized by its cost effectiveness, increased reliability, and minimal human eff ort in nursing assistance. The remote monitoring of patients through a wearable sensing network has outstanding potential in current healthcare systems. Such a network can continuously monitor the vital health conditions (such as heart rate variability, blood pressure, glucose level, and oxygen saturation) of patients with chronic diseases. Low-power radio-frequency (RF) technologies, especially Bluetooth low energy (BLE), play significant roles in modern healthcare. However, most of the RF spectrum is licensed and regulated, and the effect of RF on human health is of major concern. Moreover, the signal-to-noise-plus-interference ratio in high distance can be decreased to a considerable extent, possibly leading to the increase in bit-error rate. Optical camera communication (OCC), which uses a camera to receive data from a light-emitting diode (LED), can be utilized in eHealth to mitigate the limitations of RF. However, OCC also has several limitations, such as high signal-blockage probability. Therefore, in this study, a hybrid OCC/BLE system is proposed to ensure efficient, remote, and real-time transmission of a patient's electrocardiogram (ECG) signal to a monitor. First, a patch circuit integrating an LED array and BLE transmitter chip is proposed. The patch collects the ECG data according to the health condition of the patient to minimize power consumption. Second, a network selection algorithm is developed for a new network access request generated in the patch circuit. Third, fuzzy logic is employed to select an appropriate camera for data reception. Fourth, a handover mechanism is suggested to ensure efficient network allocation considering the patient's mobility. Finally, simulations are conducted to demonstrate the performance and reliability of the proposed system.

Predicting blood pressure from physiological index data using the SVR algorithm

Background

Blood pressure diseases have increasingly been identified as among the main factors threatening human health. How to accurately and conveniently measure blood pressure is the key to the implementation of effective prevention and control measures for blood pressure diseases. Traditional blood pressure measurement methods exhibit many inherent disadvantages, for example, the time needed for each measurement is difficult to determine, continuous measurement causes discomfort, and the measurement process is relatively cumbersome. Wearable devices that enable continuous measurement of blood pressure provide new opportunities and hopes. Although machine learning methods for blood pressure prediction have been studied, the accuracy of the results does not satisfy the needs of practical applications.

Results

This paper proposes an efficient blood pressure prediction method based on the support vector machine regression (SVR) algorithm to solve the key gap between the need for continuous measurement for prophylaxis and the lack of an effective method for continuous measurement. The results of the algorithm were compared with those obtained from two classical machine learning algorithms, i.e., linear regression (LinearR), back propagation neural network (BP), with respect to six evaluation indexes (accuracy, pass rate, mean absolute percentage error (MAPE), mean absolute error (MAE), R-squared coefficient of determination (R²) and Spearman’s rank correlation coefficient). The experimental results showed that the SVR model can accurately and effectively predict blood pressure.

Conclusion

The multi-feature joint training and predicting techniques in machine learning can potentially complement and greatly improve the accuracy of traditional blood pressure measurement, resulting in better disease classification and more accurate clinical judgements.

Cuffless Blood Pressure Estimation Using Pressure Pulse Wave Signals

Pulse transit time (PTT) has received considerable attention for noninvasive cuffless blood pressure measurement. However, this approach is inconvenient to deploy in wearable devices because two sensors are required for collecting two-channel physiological signals, such as electrocardiogram and pulse wave signals. In this study, we investigated the pressure pulse wave (PPW) signals collected from one piezoelectric-induced sensor located at a single site for cuffless blood pressure estimation. Twenty-one features were extracted from PPW that collected from the radial artery, and then a linear regression method was used to develop blood pressure estimation models by using the extracted PPW features. Sixty-five middle-aged and elderly participants were recruited to evaluate the performance of the constructed blood pressure estimation models, with oscillometric technique-based blood pressure as a reference. The experimental results indicated that the mean ± standard deviation errors for the estimated systolic blood pressure and diastolic blood pressure were 0.70 ± 7.78 mmHg and 0.83 ± 5.45 mmHg, which achieved a decrease of 1.33 ± 0.37 mmHg in systolic blood pressure and 1.14 ± 0.20 mmHg in diastolic blood pressure, compared with the conventional PTT-based method. The proposed model also demonstrated a high level of robustness in a maximum 60-day follow-up study. These results indicated that PPW obtained from the piezoelectric sensor has great feasibility for cuffless blood pressure estimation, and could serve as a promising method in home healthcare settings.

Advances in Photopletysmography Signal Analysis for Biomedical Applications

Heart Rate Variability (HRV) is an important tool for the analysis of a patient’s physiological conditions, as well a method aiding the diagnosis of cardiopathies. Photoplethysmography (PPG) is an optical technique applied in the monitoring of the HRV and its adoption has been growing significantly, compared to the most commonly used method in medicine, Electrocardiography (ECG). In this survey, definitions of these technique are presented, the different types of sensors used are explained, and the methods for the study and analysis of the PPG signal (linear and nonlinear methods) are described. Moreover, the progress, and the clinical and practical applicability of the PPG technique in the diagnosis of cardiovascular diseases are evaluated. In addition, the latest technologies utilized in the development of new tools for medical diagnosis are presented, such as Internet of Things, Internet of Health Things, genetic algorithms, artificial intelligence and biosensors which result in personalized advances in e-health and health care. After the study of these technologies, it can be noted that PPG associated with them is an important tool for the diagnosis of some diseases, due to its simplicity, its cost⁻benefit ratio, the easiness of signals acquisition, and especially because it is a non-invasive technique.

Study of continuous blood pressure estimation based on pulse transit time, heart rate and photoplethysmography-derived hemodynamic covariates

It is widely recognized that pulse transit time (PTT) can track blood pressure (BP) over short periods of time, and hemodynamic covariates such as heart rate, stiffness index may also contribute to BP monitoring. In this paper, we derived a proportional relationship between BP and PPT^-2 and proposed an improved method adopting hemodynamic covariates in addition to PTT for continuous BP estimation. We divided 28 subjects from the Multi-parameter Intelligent Monitoring for Intensive Care database into two groups (with/without cardiovascular diseases) and utilized a machine learning strategy based on regularized linear regression (RLR) to construct BP models with different covariates for corresponding groups. RLR was performed for individuals as the initial calibration, while recursive least square algorithm was employed for the re-calibration. The results showed that errors of BP estimation by our method stayed within the Association of Advancement of Medical Instrumentation limits (- 0.98 ± 6.00 mmHg @ SBP, 0.02 ± 4.98 mmHg @ DBP) when the calibration interval extended to 1200-beat cardiac cycles. In comparison with other two representative studies, Chen's method kept accurate (0.32 ± 6.74 mmHg @ SBP, 0.94 ± 5.37 mmHg @ DBP) using a 400-beat calibration interval, while Poon's failed (- 1.97 ± 10.59 mmHg @ SBP, 0.70 ± 4.10 mmHg @ DBP) when using a 200-beat calibration interval. With additional hemodynamic covariates utilized, our method improved the accuracy of PTT-based BP estimation, decreased the calibration frequency and had the potential for better continuous BP estimation.

Wearable Intrinsically Soft, Stretchable, Flexible Devices for Memories and Computing

A recent trend in the development of high mass consumption electron devices is towards electronic textiles (e-textiles), smart wearable devices, smart clothes, and flexible or printable electronics. Intrinsically soft, stretchable, flexible, Wearable Memories and Computing devices (WMCs) bring us closer to sci-fi scenarios, where future electronic systems are totally integrated in our everyday outfits and help us in achieving a higher comfort level, interacting for us with other digital devices such as smartphones and domotics, or with analog devices, such as our brain/peripheral nervous system. WMC will enable each of us to contribute to open and big data systems as individual nodes, providing real-time information about physical and environmental parameters (including air pollution monitoring, sound and light pollution, chemical or radioactive fallout alert, network availability, and so on). Furthermore, WMC could be directly connected to human brain and enable extremely fast operation and unprecedented interface complexity, directly mapping the continuous states available to biological systems. This review focuses on recent advances in nanotechnology and materials science and pays particular attention to any result and promising technology to enable intrinsically soft, stretchable, flexible WMC.

The usefulness and actual use of wearable devices among the elderly population

BACKGROUND AND OBJECTIVE

Elderly populations are more prone to diseases and need continuous monitoring of parameters to ensure good health. Wearable devices (WDs) can be helpful in the early detection and management of medical conditions. However, less is known about the use of currently available WDs among elderly populations. The objectives of this study were to determine the usefulness and actual use of wearable devices among the elderly population.

METHODS

Our methodology was based on a systematic review and a survey questionnaire. In the systematic review, search was conducted in four databases PubMed, MDPI, Sage, and Scopus with search terms "wearable device" and "elderly", "wearable sensor" and "elderly". The inclusion criteria were the studies which described health-related wearable devices, its use as the outcome, conducted on a minimum of ten participants and published in the last five years. The survey was conducted on the MOOCs (Massive Open Online Course) platform. The questionnaire was related to the use of technology, intention to use, security and privacy concerns, and willingness to pay.

RESULTS

The review identified 4915 articles, of which, 31 studies eventually met the inclusion criteria. All studies reported positive impacts after assessing devices, despite certain drawbacks. The majority of the samples were males. The survey revealed responses from 233 individuals out of the 1100 participants of the course. The survey results were categorized into two age groups: 54.3% were elderly (>65 years) and 45.49% were non-elderly (≤65 years). Very few elderly people were currently using WD. More than 60% of elderly people were interested in the future use of wearable devices, and preferred future use to improve physical and mental activities. A majority of the respondents were female.

CONCLUSIONS

This study suggests awareness should be created among elderly populations regarding the use of WDs for the early detection and prevention of complications and emergencies. Elderly populations are more prone to benefits from using WDs. The review concluded that devices should be tested on elderly groups as well, considering sex equality, and on both healthy and sick participants for better insights. The survey determined the elderly as frequent users of technology, but lack of knowledge of WD and demonstrated female interest in the use of WD. In future research on WDs, it is suggested that clinical studies be conducted for longer durations, and standard protocols such as age and sex equality should be considered. Requirements from both users and physicians should be acknowledged for better cognizance of WDs.

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Predictions on Maximum Calibration Period and Acceptable Error Limits

OBJECTIVE

Pulse transit time (PTT) is being widely pursued for ubiquitous blood pressure (BP) monitoring. PTT-based systems may require periodic cuff calibrations but can still be useful for hypertension screening by affording numerous out-of-clinic measurements that can be averaged. The objective was to predict the maximum calibration period that would not compromise accuracy and acceptable error limits in light of measurement averaging for PTT-based systems.

METHODS

Well-known mathematical models and vast BP data were leveraged. Models relating PTT, age, and gender to BP were employed to determine the maximum time period for the PTT-BP calibration curve to change by <1 mmHg over physiological BP ranges for each age and gender. A model of within-person BP variability was employed to establish the screening accuracy of the conventional cuff-based approach. These models were integrated to investigate the screening accuracy of the average of numerous measurements of a PTT-based system in relation to the accuracy of its individual measurements.

RESULTS

The maximum calibration period was about 1 year for a 30 year old and declined linearly to about 6 months for a 70 year old. A PTT-based system with a precision error of >12 mmHg for systolic BP could achieve the screening accuracy of the cuff-based approach via measurement averaging.

CONCLUSION

This theoretical study indicates that PTT-based BP monitoring is viable even with periodic calibration and seemingly high measurement errors.

SIGNIFICANCE

The predictions may help guide the implementation, evaluation, and application of PTT-based BP monitoring systems in practice.

Design and implementation of a BSN-based system for plantar health evaluation with exercise load quantification

Background

Plantar pressure measurement has become increasingly useful in the evaluation of plantar health conditions thanks to the recent progression in sensing technology. Due to the large volume and high energy consumption of monitoring devices, traditional systems for plantar pressure measurement are only focused on static or short-term dynamic monitoring. It makes them inappropriate for early detections of plantar symptoms usually presented in long-term activities.

Methods

A prototype of monitoring system based on body sensor network (BSN) is proposed for quantitative assessment of plantar conditions. To further assess the severity of plantar symptoms which can be reflected from the pressure distribution in motion status, an approach to conjoint analysis of pressure distribution and exercise load quantification based on the strike frequency (SF) and heart rate (HR) is also proposed.

Results

An examination was tested on 30 subjects to verify the capabilities of the proposed system. The estimated correlation rate with reference devices (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$r>0.9$$\end{document}r>0.9) and error rate on the average (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{AE}<0.08$$\end{document}RAE<0.08) of HR and SF indicated equal measuring capabilities as the existing commercial products . Comprised of the conjoint analysis based on HR and SF, the proposed method of exercise load quantification was examined on all subjects’ recordings.

Conclusions

A prototype of an innovative BSN-based bio-physiological measurement system has been implemented for the long-term monitoring and early evaluation of plantar condition. The experimental results indicated that the proposed system has a great potential value in the applications of long-term plantar health monitoring and evaluation.

A feature exploration methodology for learning based cuffless blood pressure measurement using photoplethysmography

In this work, we propose a feature exploration method for learning-based cuffless blood pressure measurement. More specifically, to efficiently explore a large feature space from the photoplethysmography signal, we have applied several analytical techniques, including random error elimination, adaptive outlier removal, maximum information coefficient and Pearson's correlation coefficient based feature assessment methods. We evaluate fifty-seven possible feature candidates and propose three separate feature sets with each containing eleven features to predict the systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean blood pressure (MBP), respectively. From our experimental results on a realistic dataset, this work achieves 4.77±7.68, 3.67±5.69 and 3.85±5.87 mmHg prediction accuracy for SBP, DBP and MBP. In summary, using the proposed light-weight features, the proposed predictors can successfully achieve a Grade A in two standards proposed by the American National Standards of the Association for the Advancement of Medical Instrumentation (AAMI) and British Hypertension Society (BHS).

Wearable Sensors for Remote Health Monitoring

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

Wearable Sensors for Remote Health Monitoring

Life expectancy in most countries has been increasing continually over the several few decades thanks to significant improvements in medicine, public health, as well as personal and environmental hygiene. However, increased life expectancy combined with falling birth rates are expected to engender a large aging demographic in the near future that would impose significant  burdens on the socio-economic structure of these countries. Therefore, it is essential to develop cost-effective, easy-to-use systems for the sake of elderly healthcare and well-being. Remote health monitoring, based on non-invasive and wearable sensors, actuators and modern communication and information technologies offers an efficient and cost-effective solution that allows the elderly to continue to live in their comfortable home environment instead of expensive healthcare facilities. These systems will also allow healthcare personnel to monitor important physiological signs of their patients in real time, assess health conditions and provide feedback from distant facilities. In this paper, we have presented and compared several low-cost and non-invasive health and activity monitoring systems that were reported in recent years. A survey on textile-based sensors that can potentially be used in wearable systems is also presented. Finally, compatibility of several communication technologies as well as future perspectives and research challenges in remote monitoring systems will be discussed.

A Novel Method for Continuous, Noninvasive, Cuff-Less Measurement of Blood Pressure: Evaluation in Patients With Nonalcoholic Fatty Liver Disease

OBJECTIVE

One promising approach for a continuous, noninvasive, cuff-less ambulatory blood pressure (BP) monitor is to measure the pulse wave velocity or the inversely proportional pulse transit time (PTT), based on electrical and optical physiological measurements in the chest area. A device termed IsenseU-BP+ has been developed for measuring continuous BP, as well as other physiological data. The objective of this paper is to present results from the first clinical evaluation with a wide range of patients. The study was set up to verify whether IsenseU-BP+ can be used to measure raw signals with sufficient quality to derive PTT.  Methods: The test protocol, run 23 times on 18 different patients with nonalcoholic fatty liver disease, includes both supine measurement at rest as well as measurements during indoor cycling. Changes in PTT were compared with the BP changes measured using validated reference sensors.  Results: IsenseU-BP+ measured signals with good quality during rest on 17 of 18 patients despite the high diversity in age, body shape, and body mass index. Evaluation during cycling was difficult due to a lack of good reference measurements.

CONCLUSION

IsenseU-BP+ measures PTT with high quality during supine rest and exercise and could therefore be suitable for deriving noninvasive continuous BP, although evaluation during exercise was limited due to inaccurate reference BP measurements.

SIGNIFICANCE

Continuous, noninvasive measurement of BP would be highly beneficial in a number of clinical settings. Systems currently considered as the gold standard for the investigation of hypertension carry considerable limitations, which could be overcome by the method proposed here.

Pulse Arrival Time Based Cuff-Less and 24-H Wearable Blood Pressure Monitoring and its Diagnostic Value in Hypertension

Ambulatory blood pressure monitoring (ABPM) has become an essential tool in the diagnosis and management of hypertension. Current standard ABPM devices use an oscillometric cuff-based method which can cause physical discomfort to the patients with repeated inflations and deflations, especially during nighttime leading to sleep disturbance. The ability to measure ambulatory BP accurately and comfortably without a cuff would be attractive. This study validated the accuracy of a cuff-less approach for ABPM using pulse arrival time (PAT) measurements on both healthy and hypertensive subjects for potential use in hypertensive management, which is the first of its kind. The wearable cuff-less device was evaluated against a standard cuff-based device on 24 subjects of which 15 have known hypertension. BP measurements were taken from each subject over a 24-h period by the cuff-less and cuff-based devices every 15 to 30 minutes during daily activities. Mean BP of each subject during daytime, nighttime and over 24-h were calculated. Agreement between mean nighttime systolic BP (SBP) and diastolic (DBP) measured by the two devices evaluated using Bland-Altman plot were -1.4 ± 6.6 and 0.4 ± 6.7 mmHg, respectively. Receiver operator characteristics (ROC) statistics was used to assess the diagnostic accuracy of the cuff-less approach in the detection of BP above the hypertension threshold during nighttime (>120/70 mmHg). The area under ROC curves were 0.975/0.79 for nighttime. The results suggest that PAT-based approach is accurate and promising for ABPM without the issue of sleep disturbances associated with cuff-based devices.

Low Cost Inkjet Printed Smart Bandage for Wireless Monitoring of Chronic Wounds

Chronic wounds affect millions of patients around the world and their treatment is challenging as the early signs indicating their development are subtle. In addition, a type of chronic wound, known as pressure ulcer, develops in patients with limited mobility. Infection and frequent bleeding are indicators of chronic wound development. In this article, we present an unprecedented low cost continuous wireless monitoring system, realized through inkjet printing on a standard bandage, which can send early warnings for the parameters like irregular bleeding, variations in pH levels and external pressure at wound site. In addition to the early warnings, this smart bandage concept can provide long term wound progression data to the health care providers. The smart bandage comprises a disposable part which has the inkjet printed sensors and a reusable part constituting the wireless electronics. This work is an important step towards futuristic wearable sensors for remote health care applications.

Remote Patient Monitoring via Non-Invasive Digital Technologies: A Systematic Review

BACKGROUND

We conducted a systematic literature review to identify key trends associated with remote patient monitoring (RPM) via noninvasive digital technologies over the last decade.

MATERIALS AND METHODS

A search was conducted in EMBASE and Ovid MEDLINE. Citations were screened for relevance against predefined selection criteria based on the PICOTS (Population, Intervention, Comparator, Outcomes, Timeframe, and Study Design) format. We included studies published between January 1, 2005 and September 15, 2015 that used RPM via noninvasive digital technology (smartphones/personal digital assistants [PDAs], wearables, biosensors, computerized systems, or multiple components of the formerly mentioned) in evaluating health outcomes compared to standard of care or another technology. Studies were quality appraised according to Critical Appraisal Skills Programme.

RESULTS

Of 347 articles identified, 62 met the selection criteria. Most studies were randomized control trials with older adult populations, small sample sizes, and limited follow-up. There was a trend toward multicomponent interventions (n = 26), followed by smartphones/PDAs (n = 12), wearables (n = 11), biosensor devices (n = 7), and computerized systems (n = 6). Another key trend was the monitoring of chronic conditions, including respiratory (23%), weight management (17%), metabolic (18%), and cardiovascular diseases (16%). Although substantial diversity in health-related outcomes was noted, studies predominantly reported positive findings.

CONCLUSIONS

This review will help decision makers develop a better understanding of the current landscape of peer-reviewed literature, demonstrating the utility of noninvasive RPM in various patient populations. Future research is needed to determine the effectiveness of RPM via noninvasive digital technologies in delivering patient healthcare benefits and the feasibility of large-scale implementation.

The Effect of Electrode Designs Based on the Anatomical Heart Location for the Non-Contact Heart Activity Measurement

This research is an extension of a previous research [1] on the different effects of sensor location that is relatively suitable for heart rate sensing. This research aimed to elucidate the causes of wide variations in heart rate measurements from the same sensor position among subjects, as observed in previous research [1], and to enhance designs of the inductive textile electrode to overcome these variations. To achieve this, this study comprised two parts: In part 1, X-ray examinations were performed to determine the cause of the wide variations noted in the findings from previous research [1], and we found that at the same sensor position, the heart activity signal differed with slight differences in the positions of the heart of each subject owing to individual differences in the anatomical heart location. In part 2, three types of dual-loop-type textile electrodes were devised to overcome variations in heart location that were confirmed in part 1 of the study. The variations with three types of sensor designs were compared with that with a single-round type of electrode design, by using computer simulation and by performing a t-test on the data obtained from the experiments. We found that the oval-oval shaped, dual-loop-type textile electrode was more suitable than the single round type for determining morphological characteristics as well as for measuring appropriate heart activity signals. Based on these results, the oval-oval, dual-loop-type was a better inductive textile electrode that more effectively overcomes individual differences in heart location during heart activity sensing based on the magnetic-induced conductivity principle.

Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice

Ubiquitous blood pressure (BP) monitoring is needed to improve hypertension detection and control and is becoming feasible due to recent technological advances such as in wearable sensing. Pulse transit time (PTT) represents a well-known potential approach for ubiquitous BP monitoring. The goal of this review is to facilitate the achievement of reliable ubiquitous BP monitoring via PTT. We explain the conventional BP measurement methods and their limitations; present models to summarize the theory of the PTT-BP relationship; outline the approach while pinpointing the key challenges; overview the previous work toward putting the theory to practice; make suggestions for best practice and future research; and discuss realistic expectations for the approach.

Big data for health

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

A survey on signals and systems in ambulatory blood pressure monitoring using pulse transit time

Blood pressure monitoring based on pulse transit or arrival time has been the focus of much research in order to design ambulatory blood pressure monitors. The accuracy of these monitors is limited by several challenges, such as acquisition and processing of physiological signals as well as changes in vascular tone and the pre-ejection period. In this work, a literature survey covering recent developments is presented in order to identify gaps in the literature. The findings of the literature are classified according to three aspects. These are the calibration of pulse transit/arrival times to blood pressure, acquisition and processing of physiological signals and finally, the design of fully integrated blood pressure measurement systems. Alternative technologies as well as locations for the measurement of the pulse wave signal should be investigated in order to improve the accuracy during calibration. Furthermore, the integration and validation of monitoring systems needs to be improved in current ambulatory blood pressure monitors.

Noninvasive Characterization of Vascular Tone by Model-Based System Identification in Healthy and Heart Failure Patients

Current markers for heart failure (HF) diagnosis and prognosis are mainly for the evaluation of cardiac functions. Since previous studies have reported that HF patients demonstrated abnormal vascular responses to external stimuli, it is speculated that vascular tone, a measure of activation level of vascular wall, may be able to reflect these abnormalities to assist HF detection. Nevertheless, vascular tone is difficult to be objectively quantified using existing tools. In this study, a vascular tone index was estimated from noninvasive blood pressure and pulse transit time measurements using system identification technique. This method was evaluated in 35 subjects (10 healthy, 13 with HF risk factors and 12 HF patients) in a regular maximal exercise test. It was found that the vascular tone index significantly increased by 24.4 ± 26.6% (p < 0.01) during maximal exercise in the healthy subjects. Moreover, the response was gradually attenuated in the risk-factor and HF groups (15.8 ± 36.5 and 0.9 ± 17.9%, respectively). The results reveal the association between the vascular tone response to maximal exercise and HF disease or risks. To conclude, the proposed method provides a quantitative characterization of vascular tone which may be a useful indicator of the pathological changes of the arteries or the heart.