Physiological Signals Research Articles

Skin-core-fiber-based fabric integrated with pressure sensing and deep learning for posture recognition

The aggravation of subhealth caused by contemporary lifestyles boosts the innovation of sensing systems that can acquire and analyze physiological signals in a real-time, high-efficiency, and even smart way. However, reliable and universal fabrication for these smart sensing units in a bulk or film form remains challenging and thus restricts their widespread applications. Herein, we developed a smart fabric system for accurate real-time sitting posture recognition based on the combination of wet-spun skin-core aerogel fibers, a signal conversion module, and deep learning model. The sensing fibers show a high sensitivity of 23.59 kPa-1, fast response/recovery time of 45/40 ms, and excellent stability over 10,000 cycles, suggesting good pressure-sensing ability. After being assembled them with a signal conversion module, the resultant fabric sensing system could capture physiological signals from human motions and sitting posture in a precise and real-time fashion. Such systems function well even integrated into different parts of clothing and provide long-term wearing comfort. When introducing a deep learning model, 98% sensing accuracy is demonstrated. This fabric sensing system not only provides reliable solutions to subhealth status monitoring and unhealth lifestyle correction, but also inspires the mass production of next-generation smart textiles.

Improving baseline reduction for emotion recognition based on electroencephalogram signals

The baseline reduction method has been widely used to define electroencephalogram (EEG) signal patterns. However, because the baseline signal in this approach contains artifacts, the baseline reduction approach cannot perform optimally. As a result, decreasing artifacts in the baseline signal is critical. The mean, Gaussian, and Savitzky-Golay filters will be compared in this study to minimize artifacts in the baseline signal. Three secondary datasets are utilized to evaluate these approaches' capacity to remove artifacts. These three strategies are also tested with the convolution neural network classification algorithms. When applied to the dataset for emotion analysis using physiological signals (DEAP) and a dataset for multimodal research of affect, personality traits, and mood on individuals and groups (AMIGOS) datasets, the mean filter can increase baseline reduction performance based on twenty-four test scenarios. On the data readiness for machine learning research (DREAMER) dataset, however, the Gaussian filter is preferable. The relative difference approach was employed in this study's baseline reduction process to generate EEG signal patterns that are easy to recognize throughout the classification phase, which impacts increasing accuracy.

A vision-enabled fatigue-sensitive human digital twin towards human-centric human-robot collaboration

Within a Human-centric Human-Robot Collaboration (HHRC) system, monitoring, assessing, and optimizing for an operator’s well-being is essential to creating an efficient and comfortable working environment. Currently, monitoring systems are used for independent assessment of human factors. However, the rise of the Human Digital Twin (HDT) has provided the framework for synchronizing multiple operator well-being assessments to create a comprehensive understanding of the operator’s performance and health. Within manufacturing, an operator’s dynamic well-being can be attributed to their physical and cognitive fatigue across the assembly process. As such, we apply non-invasive video understanding techniques to extract relevant assembly process information for automatic physical fatigue assessment. Our novelty involves a video-based fatigue estimation method, in which the boundary-aware dual-stream MS-TCN combined with an LSTM is proposed to detect the operation type, operation repetitions, and the target arm performing each task in an assembly process video. The detected results are then input into our physical fatigue profile to automatically assess the operator’s localized physical fatigue impact. The assembly process of a real-world bookshelf is recorded and tested against, with our algorithm results showing superiority in operation segmentation and target arm detection as opposed to other recent action segmentation models. In addition, we integrate a cognitive fatigue assessment tool that captures operator physiological signals in real-time for body response detection caused by stress. This provides a more robust HDT of the operator for an HHRC system.

On-body sensing technologies and signal processing techniques in addressing safety of human machine collaboration

AbstractSafety is a challenge in human machine collaboration despite of the advantages in achieving efficiency, cost reduction and productivity in a collaborative scenario between human and machine/robot. During collaboration with machines, the user might not be able to follow the collaborative tasks as expected due to the cognitive burden causing potential safety concerns such as collision. Addressing this challenge, the aim of this paper is to explore the potential of on-body sensing systems in study of user experience and the psychological condition during the collaboration between machines and human. As the psychological condition is reflected in physiological signals, sensing technologies and signal processing techniques to extract features from physiological signals are explored with applicability in human machine collaboration scenarios. An experiment is designed utilising an industrial collaborative robot arm while quantitative and qualitative data is gathered for this purpose exploring the problem to study user experience and impact of mental strain and cognitive workload on user performance and experience during human machine collaboration. Results show that an adaptive machine to user experience measured by on-body sensing systems during the collaboration has the potential to address safety in human machine collaboration while improving performance and user experience.

A non-invasive heart rate prediction method using a convolutional approach.

The research focuses on leveraging convolutional neural networks (CNNs) to enhance the analysis of physiological signals, specifically photoplethysmogram (PPG) data which is a valuable tool for non-invasive heart rate prediction. Recognizing the global challenge of heart failure, the study seeks to provide a rapid, accurate, and non-invasive alternative to traditional, uncomfortable blood pressure cuffs. To achieve more accurate and efficient heart rate estimates, a k-fold CNN model with an optimal number of convolutional layers is employed. While the findings show promising results, the study addresses potential sources of error in cuffless PPG-based heart rate measurement, including motion artifacts and skin color variations, emphasizing the need for validation against gold standard methods. The research optimizes a CNN model with optimal layers, operating on 1D arrays of 8-s data slices and employing k-fold cross-validation to mitigate probabilistic uncertainties. Finally, the model yields a remarkable minimum absolute error (MAE) rate of 6.98 beats per minute (bpm), marking a significant 10% improvement over recent studies. The study also advances medical diagnostics and data analysis, then lays a strong foundation for developing cost-effective, reliable devices that offer a more comfortable and efficient way of predicting heart rate.

Ultrasensitive, Fast-Response, and Stretchable Temperature Microsensor Based on a Stable Encapsulated Organohydrogel Film for Wearable Applications.

Ionic conductive hydrogel-based temperature sensors have emerged as promising candidates due to their good stretchability and biocompatibility. However, the unsatisfactory sensitivity, sluggish response/recovery speed, and poor environmental stability limit their applications for accurate long-term health monitoring and robot perception, especially in extreme environments. To address these concerns, here, the stretchable temperature sensors based on a double-side elastomer-encapsulated thin-film organohydrogel (DETO) architecture are proposed with impressive performance. It is found that the water-polyol binary solvent, organohydrogel film, and sandwiched device structure play important roles in the temperature sensing performance. By modifying the composition of binary solvent and thicknesses of organohydrogel and elastomer films, the DETO microsensors realize a thickness of only 380 μm, unprecedented temperature sensitivity (37.96%/°C), fast response time (6.01 s) and recovery time (10.53 s), wide detection range (25-95.7 °C), and good stretchability (40% strain), which are superior to those of conventional hydrogel-based sensors. Furthermore, the device displays good environmental stability with negligible dehydration and prolonged operation duration. With these attributes, the wearable sensor is exploited for the real-time monitoring of various physiological signals such as human skin temperature and respiration patterns as well as temperature perception for robots.

Frontal EEG correlation based human emotion identification and classification.

Humans express their feelings and intentions of their actions or communication through emotions. Recent advancements in technology involve machines in human communication in day-to-day life. Thus, understanding of human emotions by machines will be very helpful in assisting the user in a far better way. Various physiological and non-physiological signals can be used to make the machines to recognize the emotion of a person. The identification of emotional content in the signals is crucial to understand emotion and the machines act with emotional intelligence at appropriate times, thus providing a better human machine interaction with emotion identification system and mental health monitoring for psychiatric patients. This work includes the creation of an emotion EEG dataset, the development of an algorithm for identifying the emotion elicitation segments in the EEG signal, and the classification of emotions from EEG signals. The EEG signals are divided into 3s segments, and the segments with emotional content are selected based on the decrease in correlation between the frontal electrodes. The selected segments are validated with the facial expressions of the subjects in the appropriate time segments of the face video. EEGNet is used to classify the emotion from the EEG signal. The classification accuracy with the selected emotional EEG segments is higher compared to the accuracy using all the EEG segments. In subject-specific classification, an average accuracy of 80.87% is obtained from the network trained with selected EEG segments, and 70.5% is obtained from training with all EEG segments. In subject-independent classification, the accuracy of classification is 67% and 63.8% with and without segment selection, respectively. The proposed method of selection of EEG segments is validated using the DEAP dataset, and classification accuracies and F1-scores of subject dependent and subject-independent methods are presented.

Optimizing Driver Vigilance Recognition: Examining the Characterization and Cumulative Effect of Physiological Signals Across Manual and Automated Driving and Durations

Optimizing Driver Vigilance Recognition: Examining the Characterization and Cumulative Effect of Physiological Signals Across Manual and Automated Driving and Durations

Physiological response to pain in patients with temporomandibular joint disorders during physiotherapy.

Pain assessment in patients with temporomandibular joint (TMJ) disorders during physiotherapy is a challenging yet desired task. Our study addresses two issues. First, we compare the pain perception in patients with TMJ disorders and a control group. The hand dynamometer is verified as an alternate pain self-report device in the second stage. For the dynamometer validation, we employed a cuff-pressure test to induce pain of various intensities. In the second stage, physiological signals (electrodermal activity and blood volume pulse) have been acquired during the physiotherapy in a control group and patients suffering from TMJ disorder. The linear mixed-effects modeling is carried out to find differences in pain and no pain states, considering subjects' health condition. The main result of the present pilot study is that physiological reactions to painful physiotherapy do not differ between patients with chronic pain and the control group. This finding concludes that studies on TMJ disorder procedures may also be performed on healthy subjects.

Evaluation of Firefighter Training Effectiveness Based on Human Physiological Signals and Improved Transfer Learning

Evaluation of Firefighter Training Effectiveness Based on Human Physiological Signals and Improved Transfer Learning

Abstract 4139162: Optical coherence tomography measures mechanical contractile properties of the beating murine heart

Introduction: Optical imaging techniques can characterize properties of tissue at high temporal and spatial resolution, offering unique insights into electrically excitable tissues such as nerve and muscle. We hypothesized that the gated high-speed optical coherence tomography (OCT) could profile the contractile properties of mouse myocardial tissue and facilitate investigations of microscale mechanical function in the beating heart. Methods: C57BL/6 male mice (n=6) underwent a sternotomy while anesthetized and ventilated and were imaged at baseline with OCT. Another cohort of C57BL/6 male mice (n=6) were sacrificed and the hearts were ex vivo perfused with crystalloid using a Langendorff system. These hearts were imaged at baseline and after inhibition of contractile function with blebbistatin. OCT was performed using a custom tissue stabilizer and a Thorlabs TEL 321 OCT system, which was synchronized to physiologic signals using custom hardware and software and cardiac pacing protocols (Fig. 1A and 1B). OCT cross-sectional B-scan images (Fig. 1C) were obtained throughout multiple cardiac cycles in both in vivo and ex vivo preparations. Data were processed in MATLAB and ImageJ using the workflow in Fig. 1D. Results: Highly reproducible cross-sectional mean intensity changes were observed over the contractile cycle in vivo and ex vivo (Fig. 1E, left and middle). With mechanical contraction inhibited by blebbistatin, this cyclical intensity extinguished, linking the optical scattering change measured by OCT to myocardial contraction (Fig. 1E, right). B-scan curves averaged over multiple mice and temporally aligned with the ECG waveform (Fig. 1F) demonstrate a signal intensity waveform that reproducibly varies with the contractile cycle for both in vivo and ex vivo preparations. In vivo and ex vivo baseline waveforms both had peak intensity during systole and minimum in end-diastole (Fig. 1F). Reproducibility in ex vivo beating heart preparations without blood perfusion provides evidence that myocardial mechanical properties, not red blood cell flow, generate the intensity changes. Conclusion: Gated high-speed OCT imaging can quantify the mechanical contraction wave in the myocardium. Combined with gated OCT angiography, OCT techniques will provide high resolution imaging of both blood flow and mechanical contractility of the beating heart in small animal models of heart disease.

Abstract Su806: Enhancing Cardiac Arrest Registries and Research – A Framework for Handling Defibrillator Data

Background: Cardiac arrest is the most time-sensitive medical condition, and survival probability is highly dependent on early, high-quality cardiopulmonary resuscitation (CPR) measures. Resuscitation registries are established tools for continuous quality improvement and serve as a source for research. Yet they lack comprehensive information on the quality of CPR measures themselves—such as the depth, rate, and release of chest compressions—and are limited in the temporal resolution of these measures. However, data on both are contained in the recordings of defibrillators. Aims: To develop a framework for healthcare providers to gain immediate case-specific feedback, reduce documentation efforts and make fine-grained information accessible for research. Approach: An open-source Python module named CPRDAT (Kern et al. Zenodo, https://doi.org/10.5281/zenodo.10057964) was designed. This module is capable of importing and extracting data from various defibrillators as well as data recorded by VitalRecorder ( VitalDB , Department of Anesthesiology and Pain Medicine, Seoul National University College of Medicine, Seoul, Korea) into a uniform data object. Methods were implemented to automatically process, analyze, and aggregate these time-series of physiological signals, with particular attention to CPR. To reduce manual annotation workload, classification algorithms were developed. Plotting routines were developed to display the signals interactively in web applications, allowing healthcare providers to interact with the data through zooming, scaling, shifting, concatenating, merging and labelling. Results: The module was implemented into the German Resuscitation Registry, offering users a manufacturer-agnostic, data-driven, case-specific debriefing tool. Extracted information on CPR measures is stored in the registry. The framework, able to manage multiple time-series data sources, was further implemented in other settings, such as the analysis of laboratory data of a CPR swine model. Conclusions: The new module dramatically enhances the accessibility of physiologic time-series recordings. More fine-grained data will provide deeper insights into processes and CPR physiology. Finally, the open-source Python module abolishes barriers in research.

Mitochondrial calcium uptake orchestrates vertebrate pigmentation via transcriptional regulation of keratin filaments.

Mitochondria regulate several physiological functions through mitochondrial Ca2+ dynamics. However, role of mitochondrial Ca2+ signaling in melanosome biology remains unknown. Here, we show that pigmentation requires mitochondrial Ca2+ uptake. In vitro gain and loss of function studies demonstrate that mitochondrial Ca2+ uniporter (MCU) is crucial for melanogenesis while MCU rheostat, MCUb negatively control melanogenesis. Zebrafish, MCU+/- and MCUb-/- mice models show that MCU complex drives pigmentation in vivo. Mechanistically, MCU silencing activates transcription factor NFAT2 to induce expression of keratin (5, 7, and 8) filaments. Interestingly, keratin5 in turn augments mitochondrial Ca2+ uptake and potentiates melanogenesis by regulating melanosome biogenesis and maturation. Hence this signaling module acts as a negative feedback loop that fine-tunes both mitochondrial Ca2+ signaling and pigmentation. Notably, mitoxantrone, an FDA approved drug that inhibits MCU, reduces pigmentation thereby highlighting therapeutic potential of targeting mitochondrial Ca2+ uptake for clinical management of pigmentary disorders. Taken together, we reveal an MCU-NFAT2-Keratin5 driven signaling axis that acts as a critical determinant of mitochondrial Ca2+ uptake and pigmentation. Given the vital role of mitochondrial Ca2+ signaling and keratin filaments in cellular physiology, this feedback loop could be operational in a variety of other patho-physiological processes.

Exploring the relationship between cardiac awareness and balance

SummaryPostural balance requires the interplay between several physiological signals. Indirect evidence suggests that the perception of signals arising from the autonomic nervous system might play a role (e.g. cardiac awareness). Here, we tested this hypothesis by investigating the relationship between postural control and cardiac awareness (i.e. interoception) in a sample of N = 70 healthy individuals. Postural control was measured using a medical robotic device, while cardiac awareness was evaluated using the heartbeat counting task. A within-subject design included two platform configurations (static, unstable) and two visual feedback conditions (eyes open, eyes closed). For each condition, we measured the sway area and the range of oscillation of the platform, as well as the range of oscillation and the quantity of movement of participants’ trunk. In the “platform unstable, eyes closed” condition, participants with higher cardiac awareness demonstrated a significantly smaller sway area and reduced oscillations of both the platform and their trunk. These findings hint at a potential link between interoception and postural control, suggesting that the perception of internal body signals might sustain balance.

Neural Dynamics of Creative Movements During the Rehearsal and Performance of “LiveWire"

This report contains a description of physiological and motion data, recorded simultaneously and in synchrony using the hyperscanning method from two professional dancers using wireless mobile brain-body imaging (MoBI) technology during rehearsals and public performances of “LiveWire” - a new composition comprised of five choreographed music and dance sections inspired by neuroscience principles. Brain and ocular activity were measured using 28-channel scalp electroencephalography (EEG), and 4-channel electrooculography (EOG), respectively; and head motion was recorded using an inertial measurement unit (IMU) placed on the forehead of each dancer. Video recordings were obtained for each session to allow for tagging of physiological and motion signals and for behavioral analysis. Data recordings were collected from 10 sessions over a 4-month period, in which the dancers rehearsed or performed (in front of an audience) choreographed expressive movements. A detailed explanation of the experimental set-up, the steps carried out for data collection, and an explanation on the usage are provided in this report.

MCNN-CMCA: A multiscale convolutional neural networks with cross-modal channel attention for physiological signal-based mental state recognition

Human mental state recognition (MSR) has significant implications for human-machine interactions. Although mental state recognition models based on single-modality signals, such as electroencephalogram (EEG) or peripheral physiological signals (PPS), have achieved encouraging progress, methods leveraging multimodal physiological signals still need to be explored. In this study, we present MCNN-CMCA, a generic model that employs multiscale convolutional neural networks (CNNs) with cross-modal channel attention to realize physiological signals-based MSR. Specifically, we first design an innovative cross-modal channel attention mechanism that adaptively adjusting the weights of each signal channel, effectively learning both intra-modality and inter-modality correlation and expanding the channel information to the depth dimension. Additionally, the study utilizes multiscale temporal CNNs for obtaining short-term and long-term time-frequency features across different modalities. Finally, the multimodal fusion module integrates the representations of all physiological signals and the classification layer implements sparse connections by setting the mask weights to 0. We evaluate the proposed method on the SEED-VIG, DEAP, and self-made datasets, achieving superior results compared to existing state-of-the-art methods. Furthermore, we conduct ablation studies to demonstrate the effectiveness of each component in the MCNN-CMCA and show the use of multimodal physiological signals outperforms single-modality signals.

Physiological signal analysis and open science using the Julia language and associated software

In this mini review, we propose the use of the Julia programming language and its software as a strong candidate for reproducible, efficient, and sustainable physiological signal analysis. First, we highlight available software and Julia communities that provide top-of-the-class algorithms for all aspects of physiological signal processing despite the language’s relatively young age. Julia can significantly accelerate both research and software development due to its high-level interactive language and high-performance code generation. It is also particularly suited for open and reproducible science. Openness is supported and welcomed because the overwhelming majority of Julia software programs are open source and developed openly on public platforms, primarily through individual contributions. Such an environment increases the likelihood that an individual not (originally) associated with a software program would still be willing to contribute their code, further promoting code sharing and reuse. On the other hand, Julia’s exceptionally strong package manager and surrounding ecosystem make it easy to create self-contained, reproducible projects that can be instantly installed and run, irrespective of processor architecture or operating system.

Stress Level Assessment by a Multi-Parametric Wearable Platform: Relevance of Different Physiological Signals

Stress Level Assessment by a Multi-Parametric Wearable Platform: Relevance of Different Physiological Signals

Mechanisms of rhizosphere plant-microbe interactions: molecular insights into microbial colonization

The rhizosphere, as the “frontline” of plant life, connects plant roots, rhizosphere microorganisms, and surrounding soil, plays a crucial role in plant growth and health, particularly in sustainable agriculture. Despite the well-established contribution of plant-microbe interactions to plant health, the specific molecular mechanisms remain insufficiently understood. This review aims to summarize the physiological adjustments and signal modulation that both plants and microorganisms undergo within this unique ecological niche to ensure successful colonization. By analyzing key processes such as chemotaxis, root attachment, immune evasion, and biofilm formation, we uncover how plants precisely modulate root exudates to either recruit or repel specific microorganisms, thereby shaping their colonization patterns. These findings provide new insights into the complexity of plant-microbe interactions and suggest potential directions for future research in sustainable agriculture.

Alpha Traveling Waves during Working Memory: Disentangling Bottom-up Gating and Top-down Gain Control.

While previous works established the inhibitory role of alpha oscillations during working memory maintenance, it remains an open question whether such an inhibitory control is a top-down process. Here, we attempted to disentangle this issue by considering the spatio-temporal component of waves in the alpha band, i.e., alpha traveling waves. We reanalyzed two pre-existing and open-access EEG datasets (N = 180, 90 males, 80 females, 10 unknown) where participants performed lateralized, visual delayed match-to-sample working memory tasks. In the first dataset, the distractor load was manipulated (2, 4, or 6), whereas in the second dataset, the memory span varied between 1, 3, and 6 items. We focused on the propagation of alpha waves on the anterior-posterior axis during the retention period. Our results reveal an increase in alpha-band forward waves as the distractor load increased, but also an increase in forward waves and a decrease in backward waves as the memory set size increased. Our results also showed a lateralization effect: alpha forward waves exhibited a more pronounced increase in the hemisphere contralateral to the distractors, whereas the reduction in backward waves was stronger in the hemisphere contralateral to the targets. In short, the forward waves were regulated by distractors, whereas targets inversely modulated backward waves. Such a dissociation of goal-related and goal-irrelevant physiological signals suggests the co-existence of bottom-up and top-down inhibitory processes: alpha forward waves might convey a gating effect driven by distractor load, while backward waves may represent direct top-down gain control of downstream visual areas.Significance Statement When exploring the functional role of alpha band neural oscillations during working memory, mostly amplitude modulations have been considered so far, with relatively limited exploration of spatial-temporal dynamics of this rather global brain oscillatory signature. The present study seeks to address this gap by examining the directionality of alpha wave propagation during working memory retention. Our findings offer novel insights into the well-established inhibitory role of alpha waves, demonstrating that this function is manifested differently according to their propagation directions: forward waves seem to facilitate bottom-up gating, while backward waves might mediate top-down gain control.