Physiological Signals Research Articles

Development of Ultrathin, Breathable, Waterproof, and Durable Nanonet‐Supported Ionogel Sensors for Electrophysiological Monitoring

AbstractIonogel has gained attention as a promising material for flexible electronic skin (e‐skin), due to its exceptional conductivity, compositional stability, and biocompatibility. Nevertheless, establishing a seamless interface between ionogel and human skin remains a significant challenge. This study presents the design and fabrication of a nanonet‐supported ionogel with a thickness of ≈16 µm, marking it the thinnest ionogel reported to date. This ultrathin ionogel exhibits remarkable toughness (5.51 MJ m−3), a broad strain range (0–375%), and impressive fatigue resistance, enduring over 3000 cycles. Additionally, it offers excellent water resistance and a high water vapor transmission rate (WVTR), supporting perspiration and enhancing skin breathability. Moreover, the ultrathin ionogel has potential as a carrier in transdermal drug delivery systems (TDDS), highlighting its suitability for biomedical applications. Wearable devices incorporating this ultrathin ionogel facilitate continuous, sensitive, and highly accurate monitoring of physiological signals, positioning it as a promising material for future flexible e‐skin material.

Autoregulatory dysfunction in adult Moyamoya disease with cerebral hyperperfusion syndrome after bypass surgery

Cerebral hyperperfusion syndrome (CHS) is a serious complication after bypass surgery in Moyamoya disease (MMD), with autoregulatory dysfunction being a major pathogenesis. This study investigated the change of perioperative autoregulation and preoperative prognostic potentials in MMD with postoperative CHS. Among 26 hemispheres in 24 patients with adult MMD undergoing combined bypass, 13 hemispheres experienced postoperative CHS. Arterial blood pressure and cerebral blood flow velocity were perioperatively measured with transcranial Doppler ultrasound during resting and the Valsalva maneuver (VM). Autoregulation profiles were discovered in both the CHS and non-CHS groups using mean flow index (Mxa), VM Autoregulatory Index (VMAI), and a new metric termed VM Overshooting Index (VMOI). The CHS group had inferior autoregulation than the non-CHS group as indicated by VMOI on preoperative day 1 and postoperative 3rd day. Deteriorated autoregulation was observed via Mxa in the CHS group than in the non-CHS group on the postoperative 3rd and discharge days. Postoperative longitudinal autoregulation recovery in the CHS group was found in a logistic regression model with diminished group differences over the time course. This work represents a step forward in utilizing autoregulation indices derived from physiological signals, to predict the postoperative CHS in adult MMD.

High-strength polyvinyl alcohol/gelatin/LiCl dual-network conductive hydrogel for multifunctional sensors and supercapacitors

The synthesis of conductive hydrogels with high mechanical strength, toughness, optimal fracture growth rate and the capability to detect diverse human body movements poses a significant challenge in the realm of flexible electronics. In this study, a one-pot technique utilized effectively to fabricate conductive materials by doping LiCl into a mixture of polyvinyl alcohol (PVA) and gelatin. The PVA/gelatin/LiCl0.3(PGL) conductive hydrogel demonstrates exceptional robustness, flexibility, and resistance to deformation, enabling the monitoring of various physiological signals such as temperature and humidity. Additionally, the PGL demonstrates exceptional elongation properties (up to 1111.32 %), high lifting capacity (up to 25 kg), resistance to deformation, and sustained stability of peak signals even after 300 cycles at 50 % strain. The hydrogel electrolyte exhibits a conductivity of 2.114 S/m at 25 °C and a specific capacitance of up to 48.75 F/g, along with favorable mechanical and electrochemical characteristics. These findings suggest that the PVA/gelatin/LiCl0.3 hydrogel supercapacitor (PGLSC) conductive hydrogel shows significant potential for integration into flexible electronics and wearable technology devices.

Effects of In-vehicle Auditory Interactions on Takeover Performance in SAE L2 Semi-automated Vehicles

Commercially available automated vehicles require drivers to maintain focus on their driving environment and be prepared to fully control their vehicles (i.e., perform a takeover) when critical incidents occur (e.g., sudden automation failures). Therefore, drivers are discouraged from engaging in non-driving tasks that cause visual or manual distractions. Auditory interactions, despite being considered a safe alternative, can consume the attentional resources of drivers, causing them to respond poorly to critical situations. This study investigates (1) how varying levels of auditory interactions affect takeover performance and (2) what physiological contexts are related to the takeover performance in SAE Level 2 automated driving. For the investigation, 50 drivers wore wearable devices that collected various physiological signals and performed six different auditory tasks during L2 automated driving in a simulator-based experiment. The results showed that auditory interactions could degrade the takeover performance and that the task demand for auditory interactions nonlinearly affected the takeover performance, possibly owing to behavior changes intended to prevent the task difficulty from becoming excessively high. Additionally, physiological contexts such as pupil diameter, dispersion of eye movements, and inter-beat interval, were found to be related to the takeover performance. Subsequently, we discussed drivers’ behavior changes, practical deployment of in-situ physiological measures, and design implications for mitigating the degradation of takeover performance due to auditory tasks.

Dephosphorylation of bZIP59 by PP2A ensures appropriate shade avoidance response in Arabidopsis.

Changes in light quality and quantity experienced by many shade-intolerant plants grown in close proximity lead to transcriptional reprogramming and shade avoidance syndrome (SAS). Despite the importance of phosphorylation-dependent signaling in cellular physiology, phosphorylation events during SAS are largely unknown. Here, we examined shade-regulated phosphorylation events in Arabidopsis using quantitative phosphoproteomics. We confirmed shade-induced dephosphorylation of bZIP59, a basic region/leucine zipper motif (bZIP) transcription factor. Shade treatment promotes the nuclear localization of bZIP59, which can be mimicked by mutation of the phosphorylation sites on bZIP59. Phenotypic analysis identified that bZIP59 negatively regulated shade-induced hypocotyl elongation. bZIP59 repressed the shade-induced activation of certain growth-related genes, while shade increased the DNA binding of bZIP59. Furthermore, the protein phosphatase 2A (PP2A) mediated dephosphorylation of bZIP59. Our study characterized a previously unidentified mechanism by which the phytochrome B (phyB)-PP2A-bZIP59 regulatory module integrates shade signals and transcriptomes, broadening our knowledge of phosphorylation strategies for rapid adaptation to shade.

Disabled 2 (Dab2) Regulates Tumour Progression in Skin Squamous Cell Carcinoma.

Dab2 is an endocytic adaptor protein involved in various physiological processes and signaling pathways. Dab2 is deregulated in various cancers; however, its role in skin squamous cell carcinoma ( SCC) has not been elucidated yet. In the present study, we used the DMBA/TPA induced murine skin carcinogenesis model to examine the role of Dab2 in skin tumour progression. We generated tamoxifen inducible Dab2 conditional knockout system for our study. Loss of Dab2 led to delayed papilloma initiation and reduced papilloma burden. Delayed papilloma initiation was due to reduce proliferative potential of the papillomas due to Dab2 loss. Furthermore, while the WT papillomas progressed to SCC, the papillomas formed in Dab2 cKO mice failed to undergo malignant conversion to SCC. Dab2 cKO tumours showed reduced expression of K8, a marker for aggressive tumour. Moreover, Dab2 ckO tumours failed to undergo EMT as shown by reduced expression of Vimentin and Twist1. Dab2 cKO tumours also showed reduced expression of Sox2, a stem cell marker. Furthermore, qPCR analysis showed upregulation of Dab2 expression in the human skin cancer cell lines as compared to normal human skin keratinocytes. In patients, TCGA data analysis of skin cancer melanoma (SKCM) showed a trend where high levels of Dab2 correlated with poor overall survival. The present study shows that Dab2 promotes tumour progression in skin SCC.

Strain-insensitive fiber sensors bioinspired by spider silk with a multilevel helical structure

The emergence of wearable electronics has greatly stimulated the exploration of fibers due to their inherent wearing comfort. However, the manufacturing of conductive fibers with both exceptional mechanical strength and stable conductive properties remains a formidable challenge. Inspired by the helical structure of spider silk, we propose a strain-insensitive conductive fiber with a multilevel helical structure by helically arranging carbon fibers on the surface of helical polyurethane nanofibers, which is further employed in the fabrication of fiber sensors. The resulting fibers demonstrate outstanding strain stability with a resistance change rate (ΔR/R0) of less than 0.09 when the strain reaches 100% and a resistance change rate below 0.3 at the 200% strain level, significantly outperforming previous reports. Through experimental investigations and computational analysis, we elucidate that the underlying mechanism of the superior conductive performance and stability is attributed to the redundant helical structures and polyurethane nanofiber core layer contraction. Moreover, the fiber devices fabricated on the basis of our strain-insensitive conductive fibers exhibit distinguished performance in various health-related physiological signal monitoring tasks, such as respiration heat monitoring, voice recording, and electrochemical detection of sweat constituents.

Examining mental workload based on multiple physiological signals: review of the multi-attribute task battery (MATB) technique

Examining mental workload based on multiple physiological signals: review of the multi-attribute task battery (MATB) technique

Personalized blood pressure estimation using multiview fusion information of wearable physiological signals and transfer learning

Continuous blood pressure (BP) monitoring is crucial for individual health management, yet the significant inter-individual variations among patients pose challenges to achieving precision medicine. In response to this issue, we propose a parallel cross-hybrid architecture that integrates a convolutional neural network backbone and a Mix-Transformer backbone. This model, grounded in multi-view physiological signals and personalized fine-tuning strategies, aims to estimate BP, facilitating the capture of physiological information across diverse receptive fields and enhancing network expressive capabilities. Our proposed architecture exhibits superior performance in estimating systolic blood pressure and diastolic blood pressure, with average absolute errors of 3.94 mmHg and 2.24 mmHg, respectively. These results surpass existing baseline models and align with the standards set by the British Hypertension Society, the Association for the Advancement of Medical Instrumentation, and the Institute of Electrical and Electronics Engineers for BP measurement. Additionally, this study explores a personalized model fine-tuning strategy by adjusting specific layers and incorporating individual information, presenting an optimal solution. The model's generalization ability is validated through transfer learning across databases (public and self-made). To enhance the proposed architecture's usability in wearable devices, this study employs a knowledge distillation strategy for model lightweighting, with preliminary application in our designed real-time BP estimation system. This study provides an efficient and accurate solution for personalized BP estimation, exhibiting broad potential applications.

A Novel Labeling Method of Physiological-based Pressure Pain Assessment Among Patients With and Without Chronic Low Back Pain

Pain, especially chronic pain, is a complicated and subjective experience, threatening global healthcare as one of the most severe health problems. Traditionally, pain is assessed by Visual Analog Scale to indicate the pain intensity by the patient’s self-report, causing them to become biased by various psychosocial factors. In this study, we performed two distinct labeling methods to assess the pressure pain in Quantitative Sensory Testing and to differentiate healthy controls and chronic low back pain patients: time period labels and percentage timestamp labels. Physiological signals such as blood volume pulse and galvanic skin response were collected. The time period labeling method was to segment via fixed time windows. The percentage timestamp labeling method was to select the timestamp labels based on the percentage of the threshold or the tolerance time. Both methods demonstrate different advantages when visualizing the information of different pain states and different participant groups.

Outage Probability Analysis For Dual-Hop Asymmetric Fading WBAN

Wireless Body Area Networks (WBANs) involve wireless communication among resource constrained sensors located on the human body, which are used to capture physiological signals. A significant challenge faced by WBANs is their reliability. It is crucial to analyze the outage of the system to ensure improved reliability in the system design. This study focuses on analyzing the outage performance of dual hop WBANs under different fading scenarios, wherein the channel coefficients follow different distributions. Outage performance is evaluated using the CDF of the SNR of different fading scenarios. Furthermore, the outage performance of these fading distributions are compared to each other and a target outage probability of 10% was pre-set to evaluate the performance of the composite channels from worst to best. Results show that the minimum acceptable threshold SNRs at average SNR = 15 dB for Gamma-Weibull, Gamma-Log-Normal, Gamma-Rayleigh, Gamma–Gamma, Gamma-Rician and Gamma-Nakagami-m are - 4 dB, 2 dB, 4 dB, 6 dB, 8 dB and 10 dB respectively. The analytical outcomes for all the results for different cases are validated through Monte Carlo simulations. There is a lack of comprehensive frameworks in the literature that address all scenarios according to the authors’ knowledge.

Development of a Sexological Ontology.

This study aimed to show what role biomedical engineering can play in sexual health. A new concept of sexological ontology, an essential tool for building evidence-based models of sexual health, is proposed. This ontology should be based on properly validated mathematical models of sexual reactions identified using reliable measurements of physiological signals. This paper presents a review of the recommended measurement methods. Moreover, a general human sexual reaction model based on dynamic systems built at different levels of time × space × detail is presented, and the actual used modeling approaches are reviewed, referring to the introduced model. Lastly, examples of devices and computer programs designed for sexual therapy are described, indicating the need for legal regulation of their manufacturing, similar to that for other medical devices.

Generative technology for human emotion recognition: A scoping review

Affective computing stands at the forefront of artificial intelligence (AI), seeking to imbue machines with the ability to comprehend and respond to human emotions. Central to this field is emotion recognition, which endeavors to identify and interpret human emotional states from different modalities, such as speech, facial images, text, and physiological signals. In recent years, important progress has been made in generative models, including Autoencoder, Generative Adversarial Network, Diffusion Model, and Large Language Model. These models, with their powerful data generation capabilities, emerge as pivotal tools in advancing emotion recognition. However, up to now, there remains a paucity of systematic efforts that review generative technology for emotion recognition. This survey aims to bridge the gaps in the existing literature by conducting a comprehensive analysis of over 330 research papers until June 2024. Specifically, this survey will firstly introduce the mathematical principles of different generative models and the commonly used datasets. Subsequently, through a taxonomy, it will provide an in-depth analysis of how generative techniques address emotion recognition based on different modalities in several aspects, including data augmentation, feature extraction, semi-supervised learning, cross-domain, etc. Finally, the review will outline future research directions, emphasizing the potential of generative models to advance the field of emotion recognition and enhance the emotional intelligence of AI systems.

Improvement of Thermal Stability of Charges in Polylactic Acid Electret Films for Biodegradable Electromechanical Sensors.

Eco-friendly sensors fabricated from biocompatible and biodegradable materials are promising candidates for wearable and implantable electronics due to their environmental sustainability and biosafety. This article reports a fully biodegradable electromechanical sensor (FBES) utilizing a sandwich structure with macro ripple structured polylactic acid (PLA) electret films acting as sensitive layers and molybdenum (Mo) sheets serving as electrodes for a wearable device application. The stability of the space charge stored within the PLA film has been enhanced by introducing an internal cellular structure and improving the polarization process. A macro ripple structure of the PLA layer with higher deformation is a great guarantee for boosting the pressure sensitivity. The results indicate that inserting cell microstructures and optimizing the polarization process significantly improve the charge storage stability of PLA films by nearly 55%. This enhancement is attributed to several factors, including the extended charge drift path of the charges in cellular films, a synergy effect of surface charges, and "macroscopic" dipole charges distributed in the cells. The fabricated sensor achieves a high sensitivity of 1000 pC/kPa, a wide pressure detection range of 0.03-62.4 kPa, and satisfactory stability. Such sensors are not only sensitive to body movements but also to subtle physiological signals, satisfying the diverse needs of wearable healthcare. Importantly, all the composition materials of the sensor can be completely degraded after their service, aligning with the environmentally friendly principles of green development.

Beat-to-beat analysis of hemodynamic response to mental and psychological stress in sickle cell anemia

Abstract Vaso-occlusive crises are a hallmark symptom of sickle cell disease. Physical stressors can trigger decreased microvascular blood flow and increase the risk for vaso-occlusive crises. However, the effect of mental and psychological stressors on vascular physiology in sickle cell disease is not well-established. We hereby examined fluctuations in continuous blood pressure to evaluate hemodynamic changes in sickle cell disease patients during mental and psychological stress. Thirteen sickle cell disease subjects from the Children’s Hospital Los Angeles and 11 healthy volunteers were recruited. Continuous blood pressure was recorded during two mental tasks and one psychological stress task. Systolic beat-to-beat blood pressure variability measurements were calculated for each subject. Three very short-term blood pressure variability metrics served as outcome measures: standard deviation, coefficient of variation, and average real variability. Peripheral augmentation index was calculated from arterial waveforms. Linear mixed effects models evaluated associations between patient factors and outcome measures. Sickle cell disease patients exhibit increased systolic blood pressure variability in response to psychological stress. All subjects exhibited a decrease in systolic blood pressure variability in response to mental stress tasks. During mental stress, both groups displayed increased augmentation index, reflective of stress-induced vasoconstriction, while psychological stress in sickle cell disease patients led to both decreased mean arterial pressure and increased augementation index, suggestive of uncompensated vasoconstriction. These findings emphasize the impact of mental and psychological stressors on vascular function in sickle cell disease and the potential for monitoring physiological signals to predict vaso-occlusive crisis events.

Modeling takeover time for non-driving related tasks using hybrid deep neural networks in conditionally automated vehicles

To enhance the safety of the transition from autonomous to manual driving control, one of the key factors is quantifying the safe takeover transition, namely the takeover time. Previous studies have focused on factors influencing takeover time and predicting takeover time using traditional methods, but there is a lack of research utilizing deep learning networks and driver physiological factors for takeover time prediction. Toward this end, we propose a hybrid deep learning network model dedicated to predicting drivers’ takeover time with a dataset obtained from a bench experiment involving 46 drivers. Firstly, to address the potential information loss commonly associated with traditional manual feature extraction techniques, a convolutional neural network (CNN) is developed to automatically extract physiological spatial representations from drivers. Additionally, a double-layer bidirectional long short-term memory network (DBiLSTM) is incorporated to model the physiological time representation dimension, enabling a more refined representation of physiological signals. Then, to tackle the complexities introduced by individual driver variations, a self-attention mechanism is enabling the model to automatically prioritize critical features, thereby enhancing the accuracy and reliability of its predictions. The results demonstrate that the proposed approach surpasses traditional machine learning network models in predictive accuracy for takeover time prediction, thereby exhibiting novelty in the field of vehicle takeover control research. This provides a more reliable technical foundation for anticipating the durations of vehicle takeovers.

Oriented Alginate-Poly(vinyl alcohol) Electrospun Nanofibers for Multimodal Sensing and Gesture Language Recognition.

Flexible nanofiber sensors have gained substantial attention in extending application scenarios owing to their desirable lightweight, comfort, and breathability. Nevertheless, disorder and uneven dimension issues of nanofibers are the leading concerns in their multifunctional response, which often leads to erratic response signals as well as poor linearity. In this work, a high-performance oriented nanofiber film with a three-dimensional network consisting of alginate sodium, poly(vinyl alcohol), and poly(ethylene oxide) was successfully fabricated by a controllable directional electrospinning technique. The main properties of the nanofibers are capable of being regulated intentionally by varying the electrospinning temperature, collector rotation speed, and polymer concentrations. Based on the favorable structure orientation, the nanofiber film displays satisfied biodegradability and high mechanical strength (575.1 MPa). Being integrated with modified magnetic particles, the sensors not only display a fast response speed, high magnetic sensitivity, and exceptional recoverability in response to magnetic fields but also show favorable sensitivities and reliable long-term durability under mechanical excitations. As a wearable sensor, it can accurately perceive the physiological signals generated by the human body in real-time. Furthermore, with the assistance of a convolutional neural network model, a gesture language recognition system is developed by integrating multiple sensors to realize a high recognition accuracy (∼99.08%). This study provides a feasible strategy to manufacture high-performance multimodal sensors for wearable human-machine interaction applications.

Filiform Papillae‐Inspired Wearable Pressure Sensor with High Sensitivity and Wide Detection Range

AbstractFlexible pressure sensor (FPS) has promising applications in fields like health monitoring and human–machine interactions. The achieving of both high sensitivity and wide detection range in FPS remains highly challenging. Here, inspired by the filiform papillae on cat tongue, a FPS (noted as FPSp) with a sensitivity up to 504.5 kPa−1, a detection range from 30 Pa to 350 kPa, a fast response time of 83 ms, and a high stability over 8000 cycles is developed. The papilla‐like structure continuously shifts the location of stress concentration under increasing pressure, which avoids the accumulation of stress at papillae tips, resulting in a high sensitivity and wide detection range. Moreover, FPSp demonstrates capabilities in monitoring human physiological signals and movement status and can serve as the human‐machine interaction interface. The work not only presents a promising wearable pressure sensor but also establishes a design strategy for high‐performance wearable bioelectronics.

The relationship between cardiovagal baroreflex and cerebral autoregulation in postural orthostatic tachycardia disorder using advanced cross-correlation function

Postural orthostatic tachycardia syndrome (POTS) presents excessive orthostatic tachycardia and orthostatic intolerance. POTS is a common and therapeutically challenging condition affecting numerous people worldwide. As many disease entities can be confused with POTS, it becomes critical to identify this syndrome. Moreover, unbalanced autonomic nervous activity can induce cardiovascular diseases and influence the bio-feedback mechanism: Baroreflex (BR) and cerebral autoregulation (CA). BR and CA are important bio-mechanisms that maintain a stable circulatory system via the autonomic nervous system. Therefore, an impaired autonomic nervous system would lead to imbalanced BRS and CA. Consequently, we propose an advanced cross-correlation function (ACCF) time-domain approach to analyze baroreflex and cerebral autoregulation using physiological signals. This study assesses relation changes in BR and CA using ACCF in POTS for early clinical detection and diagnosis. The ACCF analysis results has thresholds that reveal that the BR of healthy and POTS groups present significantly different maximum CCF values (p < 0.05). The complete CCF index shows that the BR phase changes significantly into phase lag in the POTS group. Although CA analysis using the maximum CCF index was mildly weak, it did not differ in the POTS group. Thus, POTS only affects BR. An increasing sympathetic activity might induce an unbalanced baroreflex effect and increase cerebral vasomotor tone with CA. Maximum CCF value correlation coefficients between BR and CA indicated positive in POTS groups and negative in the healthy group. It could be speculated that the sympathetic nervous system compensates to improve BR function, which remains CA function. The advantage of this ACCF algorithm is that it helps observe BR and CA for early detection.

Assessment of Physiological Signals from Photoplethysmography Sensors Compared to an Electrocardiogram Sensor: A Validation Study in Daily Life.

Wearables with photoplethysmography (PPG) sensors are being increasingly used in clinical research as a non-invasive, inexpensive method for remote monitoring of physiological health. Ensuring the accuracy and reliability of PPG-derived measurements is critical, as inaccuracies can impact research findings and clinical decisions. This paper systematically compares heart rate (HR) and heart rate variability (HRV) measures from PPG against an electrocardiogram (ECG) monitor in free-living settings. Two devices with PPG and one device with an ECG sensor were worn by 25 healthy volunteers for 10 days. PPG-derived HR and HRV showed reasonable accuracy and reliability, particularly during sleep, with mean absolute error < 1 beat for HR and 6-15 ms for HRV. The relative error of HRV estimated from PPG varied with activity type and was higher than during the resting state by 14-51%. The accuracy of HR/HRV was impacted by the proportion of usable data, body posture, and epoch length. The multi-scale peak and trough detection algorithm demonstrated superior performance in detecting beats from PPG signals, with an F1 score of 89% during sleep. The study demonstrates the trade-offs of utilizing PPG measurements for remote monitoring in daily life and identifies optimal use conditions by recommending enhancements.