Wearable Electrocardiography Research Articles

Wearable Smart Cardiac Care: Take Vipasyana for Example

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the Departments of Electrical Engineering and Computer Science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

A 107.6 dB-DR Three-Step Incremental ADC for Motion-Tolerate Biopotential Signals Recording.

This article describes a power-efficient, high dynamic range (DR) incremental ADC (IADC) for wearable biopotential signals recording, where DC and low-frequency disturbances such as electrode offset, 50/60 Hz interference and motion artifact must be tolerated. To achieve a wide DR, the IADC performs a three-step conversion by combining zoom-SAR and extended counting (EC) on top of a second-order incremental delta-sigma modulator (ΔΣM). The hybrid architecture notably reduces the oversampling ratio (OSR) with respect to conventional incremental ΔΣMs, while using the EC further improves the Signal-to-Noise-and-Distortion Ratio (SNDR) by 7.4 to 25.6 dB. Fabricated in a 0.18-μm CMOS technology, the IADC achieves 107.6-dB DR, 104.9-dB peak SNR, and 99.3-dB peak SNDR at 2 kS/s while dissipating 130 μW from 1.8-V (analog) / 1.2-V (digital) supply. This translates to a highly competitive FoMDR of 176.5 dB. The high-DR IADC reduces the gain of the preceding instrumentation amplifier (IA) such that significant DC and low-frequency disturbances can be tolerated. The advantages of high DR have been demonstrated by wearable Electrocardiography (ECG) and Electroencephalography (EEG) recordings under motion artifact.

Atrial Fibrillation Detection from Compressed ECG Measurements for Wireless Body Sensor Network

Recent years have witnessed an increasing prevalence of wearable devices in the public, where atrial fibrillation (AF) detection is a popular application in these devices. Generally, AF detection is performed on cloud whereas this paper describes an on-device AF detection method. Technically, compressed sensing (CS) is first used for electrocardiograph (ECG) acquisition. Then QRS detection is proposed to be performed directly on the compressed CS measurements, rather than on the reconstructed signals on the powerful cloud server. Based on the extracted QRS information, AF is determined by quantitatively analyzing the ( RR , dRR ) plot. Databases with ECG samples collected from both medical-level (MIT-BIH afdb) and wearable ECG devices (Physionet Challenge 2017) are introduced for performance validation. The experiment results well demonstrate that our on-device AF detection algorithm can approach the performance of those implemented on the raw signals. Our proposal is suitable for AF screening directly on the wearable devices, without the support of the data center for signal reconstruction and intelligent analysis.

Technology Acceptance Model and Wearable ElectroCardioGraph (DubDub)

Model penerimaan teknologi (TAM) mengusulkan bahwa persepsi kemudahan penggunaan dan persepsi kegunaan memprediksi penggunaan alat. Studi yang sedang berjalan menyelidiki TAM untuk tugas-tugas yang berhubungan dengan pekerjaan dengan teknologi DubDub, dan menggunakan TAM sebagai dasar untuk membuat hipotesis pengaruh variabel-variabel tersebut terhadap penggunaan DubDub sebagai alat memantau kondisi jantung saat bekerja. Kematian terhadap serangan jantung saat melakukan aktivitas menurut data kemenkes diIndonesia sangat tinggi. Hal ini telah menjadi momok menakutkan bagi pekerja usia produktif, sehingga perlu adanya alat yang dapat memonitor kondisi jantung secara realtime. DubDub sebagai wearable electrocardiograph diharapkan mampu mengurangi tingkat kematian akibat serangan jantung di Indonesia, walaupun DubDub tidak menggantikan kegunaan ECG yang berada dirumah sakit. Studi ini berfokus pada penyelidikan penerimaan pengguna individu terhadap DubDub di masyarakat sebagai alat monitor jantung yang efektif. Ini mengembangkan model penggunaan teknologi untuk kesehatan. Kontribusi penelitian ini ada tiga. Pertama, penelitian ini dapat membantu mengidentifikasi apakah pengguna ingin menerima DubDub atau sebaliknya. Kedua, penelitian ini akan membantu menentukan faktor apa saja yang signifikan dalam menjelaskan niat terhadap DubDub. Sebuah upaya dilakukan apakah keyakinan sikap seperti persepsi kemudahan penggunaan dan persepsi kegunaan memiliki hubungan terhadap adopsi DubDub. Ketiga, penelitian ini termasuk yang pertama menggunakan model penerimaan teknologi dalam konteks telemedicine.

An Energy-Efficient ECG Processor Based on HDWT and a Hybrid Classifier for Arrhythmia Detection

Cardiac arrhythmia (CA) is a severe cardiac disorder that results in a significant number of fatalities worldwide each year. Conventional electrocardiography (ECG) devices are often unable to detect arrhythmia symptoms during patients’ hospital visits due to their intermittent nature. This paper presents a wearable ECG processor for cardiac arrhythmia (CA) detection. The processor utilizes a Hilbert transform-based R-peak detection engine for R-peak detection, a Haar discrete wavelet transform (HDWT) unit for feature extraction, and a Hybrid ECG classifier that combines linear methods and Non-Linear Support Vector Machines (NLSVM) classifiers to distinguish between normal and abnormal heartbeats. The processor is fabricated by the CMOS 110 nm process with an area of 1.34 mm2 and validated with the MIT_BIH Database. The whole design consumes 4.08 μW with an average classification accuracy of 97.34%.

Precordial electrocardiographic recording and QT measurement from a novel wearable ring device

BackgroundThe availability of portable and wearable electrocardiography devices has increased secondary to technological development. Single-lead ECG recordings have been shown to reliably detect and characterize cardiac rhythms such as atrial fibrillation. Acquisition of precordial electrodes for full 12-lead ECG reconstruction from bipolar recordings is complicated by the absence of a body ground / Wilson central terminal electrode, however the extent of difference between standard precordial lead and those from a wearable bipolar ECG recorder has not been characterized. ObjectiveThis study characterizes precordial ECG lead set from sequential bipolar recordings from an ECG ring wearable device. MethodsIn 70 patients wearing an ECG device on their right finger, sequential precordial leads (CR1-CR6) were obtained along with chest electrodes (V1-V6). During acquisition of the modified precordial lead CR6 a full standardized 12 lead ECG capture was obtained.Using automated analysis software, signal quality was assessed, and the correlation values between the ring-derived ECG precordial leads and standard ECG leads were compared with QRS duration, QT width, and RR interval measurement. ResultsThere is high concordance in the morphology of precordial ECG leads obtained in a standard fashion and those recorded through an ECG ring. Morphologic alignment improved with increasing laterality of the precordial lead with chest to right arm ring recording (CR5, CR6) compared with anterior chest leads to right arm (CR1, CR2). Segmental measurements of QRS duration and QT segment were well aligned and of high correlation. ConclusionWearable ring based electrocardiographic technology is capable of high fidelity recording of precordial leads for non-simultaneous reconstruction of complete ECG sets. These correlate highly with surface-obtained QRS and QT duration measurement, which has significant implications for clinical applications. Uninterpretable tracings were primarily due to electrode noise from poor electrode contact.

Abstract 16611: The Feasibility of Artificial Intelligence in Interpretation of the Treadmill Test

Introduction: The treadmill test (TMT) is commonly used as a screening test to identify myocardial ischemia. The sensitivity of the TMT in diagnosing obstructive CAD was approximately 50%, with a specificity of approximately 90%. However, the challenge with TMT is the variability in interpretation. Hypothesis: We aimed to develop an Artificial intelligence (AI) to interpret TMT. The primary endpoint was the accuracy comparison between the initial assessment (of AI or cardiologist) for obstructive CAD. Methods: Out of a total of 800 TMTs performed on adults for any clinical indications in 2018-2020, Of these, we used 500 as training data, 200 as validation data, and 100 as test data. A one-dimensional convolutional block was applied to each of the 12 leads of the TMT, and then all the leads were concatenated to apply a dense layer, which was then trained to predict the presence of obstructive coronary artery disease. This deep learning model was developed based on convolutional neural networks (CNNs) using TensorFlow (Google, Mountain View, CA, USA) and Keras packages based on the Python language. TMT were simultaneously interpreted by an AI algorithm or by the cardiologist. This assessment was subsequently reviewed by blinded interventional cardiologist who provided a final report of obstructive coronary artery disease (CAD) with coronary compute tomography. Results: The mean age was 58.1 (20-78) years, 68% were male, and 33% had obstructive coronary artery disease (≥70% stenosis in at least one vessel). The performance of AI was compared with that of conventional TMT. Accuracy of AI model was comparable to cardiologist (0.720 vs. 0.680, p =0.845) F1 score was also similar between AI and cardiologist (0.440 vs. 0.384. p =0.845) Conclusions: Artificial intelligence is comparable to a cardiologist in the initial interpretation of the treadmill test. These approaches might be applicable to data obtained from wearable electrocardiography technologies during exercise.

Effects of Audio-Visual Environmental Factors on Emotion Perception of Campus Walking Spaces in Northeastern China

In the context of urban sustainable development and the creation of pedestrian-friendly campus environments, optimizing campus walking spaces has emerged as a central focus in urban planning research. Presently, research in pedestrian environments predominantly adopts a macroscopic perspective, offering limited insights into pedestrians’ subjective experiences and emotional perceptions at a micro level. Therefore, this study conducted on-site experiments in 21 walking spaces across three campuses, utilizing image semantic analysis, multifunctional sound level meter, wearable electrocardiography devices, and the Profile of Mood States (POMS) to collect data separately on audio-visual environmental factors and pedestrians’ emotional states. This study’s findings revealed significant correlations (p < 0.01) among factors such as the Green Visual Index, Spatial Enclosure Index, Sky Visibility Index, Spatial feasibility Index, and Equivalent Continuous A-weighted Sound Pressure Level with physiological and psychological alterations in pedestrians’ emotions. Additionally, the various proportions of audio-visual environmental factors also exerted significant influences on emotions (p < 0.05). The relevant conclusions can provide a reference for optimizing the audio-visual environment of walking space and promoting the sustainable development of the campus. In future research, the effects of audio-visual environments on both emotional and physiological indicators, as well as subjective evaluations, can be explored further.

Effects of real-time remote cardiac rehabilitation on exercise capacity and quality of life: a quasi-randomised controlled trial

BackgroundThe impact of real-time remote cardiac rehabilitation (CR) on health and disability-related outcomes and its correlation with physical function are unknown. We compared the effectiveness of real-time remote CR with that of hospital-based CR on physical function improvement and physical functions of improvement (Δ) to clarify the relationship between health and disability at baseline.MethodsPatients with cardiovascular diseases (CVDs) were enrolled (n = 38) in this quasi-randomised controlled trial and underwent 4 weeks of hospital-based CR, followed by 12 weeks of remote or hospital-based CR based on quasi-randomised allocation. Patients were assessed at baseline and after 12 weeks of remote or hospital-based CR using the shortened version of the World Health Organization (WHO) Quality of Life scale (WHOQOL-BREF) for subjective satisfaction, WHO Disability Assessment Schedule (WHODAS2.0-J) for objective performance, and cardiopulmonary exercise test for physical function and peak oxygen uptake (peak VO2). The trends in measured variables from baseline to the post-CR stage were analysed.ResultsSixteen patients (mean age, 72.2 ± 10.4 years) completed remote CR, and 15 patients (mean age, 77.3 ± 4.8 years) completed hospital-based CR. The post-CR physical function differed significantly between the groups (Δpeak VO2, 2.8 ± 3.0 versus 0.84 ± 1.8 mL·min−1·kg−1; p < 0.05). The differences in post-CR changes in the WHOQOL-BREF scores between the groups were insignificant. The post-CR changes in the WHODAS2.0-J scores were significantly lower in the remote CR group than in the hospital-based CR group (ΔWHODAS2.0-J score, –8.56 ± 14.2 versus 2.14 ± 7.6; p < 0.01). Forward multiple stepwise regression analysis using overall data showed that the intervention method (β = 0.339, p < 0.05), baseline cognition (β = − 0.424, p < 0.05), and social interaction level (β = 0.658, p < 0.01; WHODAS2.0-J) were significant independent contributors to Δpeak VO2 (r2 = 0.48, F = 8.13, p < 0.01).ConclusionsRemote CR considerably improved physical function and objective performance in patients with CVDs. Remote CR can be used to effectively treat stable patients who cannot visit hospitals.Trial registrationThis interventional trial was registered at the UMIN-CTR registry (trial title: Development of remote programme for cardiac rehabilitation using wearable electrocardiograph; trial ID: UMIN000041746; trial URL: https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000046564; registration date: 2020/09/09).

Factors affecting wearable ECG device adoption by general practitioners for atrial fibrillation screening: cross-sectional study.

Atrial fibrillation (AF) is a challenging cardiovascular disease worldwide. Wearable electrocardiograph devices (WEDs) have great potential to improve the detection rate of AF in primary care. However, the factors that influence general practitioners' (GPs) perception and acceptance of WEDs are not well understood. To identify factors that influence the intention of GPs to utilize WEDs in a clinical setting to screen patients for AF. The research hypotheses and questionnaire items were designed and developed based on the unified theory of acceptance and technology (UTAUT) framework. We used stratified sampling and obtained the data through an online survey. Structural equation modeling was used to analyze the collected data.Results: A total of 1,004 valid questionnaires from GPs across Sichuan province in China were collected. Three factors increased GPs' intention to utilize WEDs to screen patients for AF, including performance expectancy (β = 0.121, p = 0.004), social influence (β = 0.356, p < 0.001), and price perception (β = 0.587, p < 0.001). Perception risk (β = -0.059, p < 0.001) decreased usage intention, while effort expectancy (β = -0.079, p = 0.155) and facilitating conditions (β = -0.014, p = 0.868) did not affect usage intention. Gender (β = -0.022, p = 0.179), age (β = 0.006, p = 0.699), education level (β = -0.22, p = 0.184) and training (β = 0.007, p = 0.69) were not significantly correlated with usage intention, and these four factors had no moderating effect on the path coefficients. GPs' intention to utilize WEDs is affected by performance expectancy, price perception, perception risk and social influence. Researcher should improve the usability and perception of WEDs for screening and carry out studies to provide high-quality evidence for the security and efficacy of wearable devices.

Usefulness of wearable electrocardiography devices in patients experiencing paroxysmal cardiac-related symptoms: A case series of the “CardioSecur” system

BackgroundElectrocardiograms (ECGs) recorded with wearable devices and additional smartphone apps play an increasing role in cardiology. Case summariesWe present two cases in which it was possible to record an ECG during cardiac-related symptoms using the patients' smartphones. Previous standard resting and 24-hour ECGs had revealed no pathologies. In one case, AV nodal reentry tachycardia was detected and treated accordingly. In the second case, ischemic ECG changes were recorded in a young woman without cardiovascular risk factors during a chest pain episode. Suspecting a coronary vasomotion disorder, an invasive diagnostic procedure was performed. The acetylcholine spasm provocation test revealed coronary microvascular spasm and appropriate therapy was initiated. DiscussionSmartphone-based ECG systems which can be used by the patient independently while experiencing cardiac-related symptoms are a modern diagnostic tool. Considering the use of these systems is beneficial for early diagnosis and appropriate treatment for paroxysmal arrhythmias and coronary vasomotion disorders.

Dry Lithography Patterning of Monolayer Flexible Field Effect Transistors by 2D Mica Stamping.

Organic field-effect transistors (OFETs) based on 2D monolayer organic semiconductors (OSC) have demonstrated promising potentials for various applications, such as light emitting diode (LED) display drivers, logic circuits, and wearable electrocardiography (ECG) sensors. To date, the fabrications of this class of highly crystallized 2D organic semiconductors (OSC) are dominated by solution shearing. As these organic active layers are only a few molecular layers thick, their compatibilities with conventional thermal evaporated top electrodes or sophisticated photolithography patterning are very limited, which also restricts their device density. Here, an electrode transfer stamp and a semiconductor patterning stamp are developed to fabricate OFETs with channel lengths down to 3 µm over a large area without using any chemicals or causing any damage to the active layer. 2D 2,9-didecyldinaphtho[2,3-b:2',3'-f]thieno[3,2-b]thiophene (C10 -DNTT) monolayer OFETs developed by this new approach shows decent performance properties with a low threshold voltage (VTH ) less than 0.5 V, intrinsic mobility higher than 10 cm2 V-1 s-1 and a subthreshold swing (SS) less than 100 mV dec-1 . The proposed patterning approach is completely comparable with ultraflexible parylene substrate less than 2 µm thick. By further reducing the channel length down to 2 µm and using the monolayer OFET in an AC/DC rectifying circuit, the measured cutoff frequency is up to 17.3 MHz with an input voltage of 4 V. The newly proposed electrode transfer and patterning stamps have addressed the long-lasting compatibility problem of depositing electrodes onto 2D organic monolayer and the semiconductor patterning. It opens a new path to reduce the fabrication cost and simplify the manufacturing process of high-density OFETs for more advanced electronic or biomedical applications.

Dynamic ECG signal quality evaluation based on persistent homology and GoogLeNet method.

Cardiovascular disease is a serious health problem. Continuous Electrocardiograph (ECG) monitoring plays a vital role in the early detection of cardiovascular disease. As the Internet of Things technology continues to mature, wearable ECG signal monitors have been widely used. However, dynamic ECG signals are extremely susceptible to contamination. Therefore, it is necessary to evaluate the quality of wearable dynamic ECG signals. The topological data analysis method (TDA) with persistent homology, which can effectively capture the topological information of high-dimensional data space, has been widely studied. In this study, a brand-new quality assessment method of wearable dynamic ECG signals was proposed based on the TDA with persistent homology method. The point cloud of an ECG signal was constructed, and then the complex sequence was generated and displayed as a persistent barcode. Finally, GoogLeNet based on the transfer learning model with a 10-fold cross-validation method was used to train the classification model. A total of 12-leads ECGs Dataset and single-lead ECGs Dataset, established based on the 2011 PhysioNet/CinC challenge dataset, were both used to verify the performance of this method. In the study, 773 "acceptable" and 225 "unacceptable" signals were used as 12-leads ECGs Dataset. We relabeled 12,000 ECG signals in the challenge dataset, and treated them as single-lead ECGs Dataset after empty lead detection and balance datasets. Compared with the traditional ECG signal quality assessment method mainly based on waveform characteristics and time-frequency characteristics, the performance of the quality assessment method proposed. In this study, the classification performance of the proposed method are fairly great, mAcc = 98.04%, F1 = 98.40%, Se = 97.15%, Sp = 98.93% for 12-leads ECGs Dataset and mAcc = 98.55%, F1 = 98.62%, Se = 98.37%, Sp = 98.85% for single-lead ECGs Dataset.

Wearable Electrocardiography for Physical Activity Monitoring: Definition of Validation Protocol and Automatic Classification

Wearable devices are rapidly spreading thanks to multiple advantages. Their use is expanding in several fields, from medicine to personal assessment and sport applications. At present, more and more wearable devices acquire an electrocardiographic (ECG) signal (in correspondence to the wrist), providing potentially useful information from a diagnostic point of view, particularly in sport medicine and in rehabilitation fields. They are remarkably relevant, being perceived as a common watch and, hence, considered neither intrusive nor a cause of the so-called "white coat effect". Their validation and metrological characterization are fundamental; hence, this work aims at defining a validation protocol tested on a commercial smartwatch (Samsung Galaxy Watch3, Samsung Electronics Italia S.p.A., Milan, Italy) with respect to a gold standard device (Zephyr BioHarness 3.0, Zephyr Technology Corporation, Annapolis, MD, USA, accuracy of ±1 bpm), reporting results on 30 subjects. The metrological performance is provided, supporting final users to properly interpret the results. Moreover, machine learning and deep learning models are used to discriminate between resting and activity-related ECG signals. The results confirm the possibility of using heart rate data from wearable sensors for activity identification (best results obtained by Random Forest, with accuracy of 0.81, recall of 0.80, and precision of 0.81, even using ECG signals of limited duration, i.e., 30 s). Moreover, the effectiveness of the proposed validation protocol to evaluate measurement accuracy and precision in a wide measurement range is verified. A bias of -1 bpm and an experimental standard deviation of 11 bpm (corresponding to an experimental standard deviation of the mean of ≈0 bpm) were found for the Samsung Galaxy Watch3, indicating a good performance from a metrological point of view.

Practical R-R Interval Editing for Heart Rate Variability Analysis Using Single-Channel Wearable ECG Devices

Recent innovations in wearable devices have expanded the usage opportunities of single-channel electrocardiography (ECG) recordings in a daily life environment and enabled a variety of indirect daily activity monitoring based on heart rate variability (HRV). In general, wearable ECGs rarely undergo visual inspection by medical experts and therefore may contain noise or artifacts. Although noise/artifact-induced changes in ECG waveforms are known to cause misdetection of the QRS complex (i.e., the most distinguishable ECG components comprised of Q wave, R wave, and S wave), its complete suppression might be technically impossible. Since misdetection occurs in the QRS complex unit, we propose reframing the traditional HRV analysis flow by subdividing the R-R interval (RRI) editing into four steps in accordance with the processing detail (i.e., identification and editing) and its target unit (i.e., QRS complex or RRI). In addition, as a dubious QRS complex identification method for practical use, we utilize the amplitude at the detected point assuming the use of a single-channel wearable ECG without a reference. Initial evaluations using pseudo/real ECG datasets including ECGs with noise and artifacts show that the proposed processing/unit-based subdivision is theoretically effective for improving HRV calculation accuracy, and that the dubious QRS complex identification method for practical use also maintains this effect. Our study starting from practical HRV analysis using single-channel wearable ECG devices encourages reexamining each step in HRV analysis through the interdisciplinary research of clinical medicine and engineering/informatics that reveals the relationship of every two adjacent steps from the perspective of theory and practice.

A new approach for ECG artifact detection using fine-KNN classification and wavelet scattering features in vital health applications

In this paper, as a new application of machine learning, a K- Nearest Neighbor (KNN) classification model is proposed to recognize artifacts in Electrocardiography (ECG) signal using 3-axis accelerometer signals. A fine KNN model using wavelet scattering coefficients as features of 3-axis accelerometer sensor captured training dataset have been proposed as alternative to other machine learning methods to classify artifacts. In wearable health-monitoring devices, artifact noise results signal deformations and it is not possible to employ filtering methods all the time due to data loss. As a result, wearable ECG monitoring devices are not reliable for critical medical usages. Our proposed model classifies accelerometer sensor samples with high probability of having artifact noises in ECG signal with positive predictive value of 94.7%. High accuracy rate of the proposed model in this work gives it an opportunity to be employed in ECG monitoring wearable devices to capture patients ECG history and labeling artifact samples for noise filtration or signal reconstruction systems to cancel the noise without having signal deformations.

Continuous heart rate variability monitoring of freely moving chicken through a wearable electrocardiography recording system

Identification and quantification of stress and stress inducing factors are important components of animal welfare assessment and essential parts of poultry management. Measurement of the autonomic nervous system's influence on cardiac function using heart rate and heart rate variability (HR/HRV) indices can provide a non-invasive assessment of the welfare status of an animal. This paper presents a preliminary study showing the feasibility of continuous long-term measurement of HR/HRV indices in freely moving chicken. We developed and evaluated an electrocardiography (ECG) based HR/HRV recording system that can be used as a poultry wearable backpack for research studies. The backpack system was first validated against a commercial ECG amplifier, and the corresponding estimations of HR values matched well with each other. Then, an in vivo proof-of-concept experiment was conducted on floor-reared chickens to collect ECG data for 2 weeks. The extracted HR/HRV values show strong alignment with circadian patterns and well-defined sleep cycles. Wearable devices, like the backpack ECG system used in this study, may be best suited for application in freely moving poultry to get an insight into circadian abnormalities and sleep quality for stress and welfare management.

An electrical-mechanical cohesive zone model combining viscoelasticity and fatigue damage for soft adhesive layer in wearable sensor

The wearable biomedical devices are promising technologies in the field of human healthcare as the sensors are attached to human skin to monitor in real time physiological signal. The signal drift caused by the damage of adhesive layer between the sensor electrode and human skin is an important factor that affects the detection accuracy of the sensor. Considering the viscoelasticity and conductivity of the adhesive layer, a rate-dependent electrical–mechanical cohesive zone model (CZM) was developed to simulate the electrical signal degradation of wearable sensor electrode during cyclic loading condition. In the proposed CZM, the electrical response of the adhesive layer is related to its mechanical behavior and damage state, and the rate-dependent mechanical behavior of the adhesive layer is described by the Maxwell model. In addition, the fatigue damage evolution is also rate-dependent as the threshold of damage initiation and fracture are defined as the function of loading velocity. The proposed CZM was implemented in ABAQUS by the UEL subroutine and applied to simulate the damage and electrical signal degradation of a wearable electrocardiograph (ECG) sensor electrode in working condition. The results demonstrated that the proposed CZM is effective to characterize the rate-dependent fatigue damage behavior and electrical signal degradation behavior of the ECG electrode during cyclic loadings. It is a promising tool for predicting the electrical signal drift of wearable healthcare sensors and other similar components caused by the damage of adhesive layer, which is important for the accurate calibration of the sensors in long-term service.

Feasibility of Passive ECG Bio-sensing and EMA Emotion Reporting Technologies and Acceptability of Just-in-Time Content in a Well-being Intervention, Considerations for Scalability and Improved Uptake.

Researchers increasingly use passive sensing data and frequent self-report to implement personalized mobile health (mHealth) interventions. Yet, we know that certain populations may find these technical protocols burdensome and intervention uptake as well as treatment efficacy may be affected as a result. In the present study, we predicted feasibility (participant adherence to protocol) and acceptability (participant engagement with intervention content) as a function of baseline sociodemographic, mental health, and well-being characteristics of 99 women randomized in the personalized preventive intervention Wellness-for-Two (W-4-2), a randomized trial evaluating stress-related alterations during pregnancy and their effect on infant neurodevelopmental trajectories. The W-4-2 study used ecological momentary assessment (EMA) and wearable electrocardiograph (ECG) sensors to detect physiological stress and personalize the intervention. Participant adherence to protocols was 67% for EMAs and 52% for ECG bio-sensors. Higher baseline negative affect significantly predicted lower adherence to both protocols. Women assigned to the intervention group engaged on average with 42% of content they received. Women with higher annual household income were more likely to engage with more of the intervention content. Researchers should carefully consider tailoring of the intensity of technical intervention protocols to reduce fatigue, especially among participants with higher baseline negative affect, which may improve intervention uptake and efficacy findings at scale.

Morning surge in sympathetic nervous activity in the indoor environment during the cold winter season.

We addressed to the sympathetic nervous activation of the same people in both their houses and a highly insulated and airtight model house (model house) during the cold winter season. Eight subjects (4 males and 4 females) stayed two nights at each house and were continuously monitored for sympathetic nerve system by calculating LF (low frequency)/HF (high frequency) in the analysis of heart rate variability using a wearable electrocardiography equipment. The room temperatures were kept constant at 20 °C or more in model house, but much lower in their houses. In all subjects, the sleeping duration is longer in model house compared with that in the participants' houses. Four subjects showed a morning surge in sympathetic activity that were more intense at their houses. This morning surge in sympathetic activity in a residential setting suggests the importance of the indoor environment in the management of early morning hypertension.