Search

Objective. Physiological data are often low quality and thereby compromises the effectiveness of related health monitoring. The primary goal of this study is to develop a robust foundation model that can effectively handle low-quality issue in physiological data.Approach. We introduce SiamQuality, a self-supervised learning approach using convolutional neural networks (CNNs) as the backbone. SiamQuality learns to generate similar representations for both high and low quality photoplethysmography (PPG) signals that originate from similar physiological states. We leveraged a substantial dataset of PPG signals from hospitalized intensive care patients, comprised of over 36 million 30 s PPG pairs.Main results. After pre-training the SiamQuality model, it was fine-tuned and tested on six PPG downstream tasks focusing on cardiovascular monitoring. Notably, in tasks such as respiratory rate estimation and atrial fibrillation detection, the models performance exceeded the state-of-the-art by 75% and 5%, respectively. The results highlight the effectiveness of our model across all evaluated tasks, demonstrating significant improvements, especially in applications for heart monitoring on wearable devices.Significance. This study underscores the potential of CNNs as a robust backbone for foundation models tailored to physiological data, emphasizing their capability to maintain performance despite variations in data quality. The success of the SiamQuality model in handling real-world, variable-quality data opens new avenues for the development of more reliable and efficient healthcare monitoring technologies.

Lymph Nodes (LNs) are crucial to the immune and lymphatic systems, filtering harmful substances and regulating lymph transport. LNs consist of a lymphoid compartment (LC) that forms a porous bulk region, and a subcapsular sinus (SCS), which is a free-fluid region. Mathematical and mechanical challenges arise in understanding lymph flow dynamics. The highly vascularized lymph node connects the lymphatic and blood systems, emphasizing its essential role in maintaining the fluid balance in the body. In this work, we describe a mathematical model in a steady setting to describe the lymph transport in a lymph node. We couple the fluid flow in the SCS governed by an incompressible Stokes equation with the fluid flow in LC, described by a model obtained by means of asymptotic homogenisation technique, taking into account the multiscale nature of the node and the fluid exchange with the blood vessels inside it. We solve this model using numerical simulations and we analyze the lymph transport inside the node to elucidate its regulatory mechanisms and significance. Our results highlight the crucial role of the microstructure of the lymph node in regularising its fluid balance. These results can pave the way to a better understanding of the mechanisms underlying the lymph node's multiscale functionalities which can be significantly affected by specific physiological and pathological conditions, such as those characterising malignant tissues. [ABSTRACT FROM AUTHOR]

Copyright of Digital Education is the property of Haiyan Publishing Co. Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study explores the impact of Indoor Environment Quality (IEQ) on the health and productivity of office workers in an office building in Fujisawa, Kanagawa, Japan. Previous studies have shown that IEQ can affect the physiological responses of occupants, such as of skin temperature, heart rate, and metabolic rate, which are indicators of health and productivity. However, most studies took place in controlled laboratory environments, which may not accurately represent real-life experiences. The study collected subjective and objective data from actual occupied office space, including on perceptions of IEQ, health, and productivity, and measurements of IEQ parameters such as on the thermal environment, light environment, indoor air quality, and acoustics. The study used correlation and linear regression methods to examine the relationship between IEQ, physiological data, and subjective responses to health and productivity. The stable thermal environment and low physical intensity of office work may contribute to the weak correlation between physiological data, thermal responses, and health–productivity variables. The results of this study can provide insights into how IEQ affects the psychological responses, well-being, and performance of office workers in real-world settings. [ABSTRACT FROM AUTHOR]

This paper provides a new method for stress management-focused predictive modeling of healthcare workers' well-being via cloud computing and machine learning. The need for proactive measures to track and assist healthcare workers' mental health is highlighted by the rising expectations placed on them. Using various data sources, our system compiles information from surveys, social media, electronic health records, and wearable devices into a single location for analysis. Predictive models that predict healthcare workers' stress levels and well-being are developed using gradient boosting, a strong machine learning (ML) technique. This work is suitable for gradient boosting due to its resilience to overfitting and capacity to process many kinds of data. Healthcare organizations may improve the health of their employees by using our technology to detect stress patterns and identify the causes of that stress. It can use specific treatments and support systems to alleviate that stress. Widespread adoption and real-time monitoring are made possible by the scalability, flexibility, and accessibility of cloud computing infrastructure. This method shows promise in the direction of proactive solutions driven by data for controlling the stress of healthcare workers and improving their general well-being. [ABSTRACT FROM AUTHOR]

Abstract Objectives This study aimed to assess the feasibility and acceptability of using EMA questionnaires and physiological data via wristbands to measure day-to-day occupational stress, musculoskeletal pain, and mental health among university employees (N = 23), across 10 work days. Adherence to the study protocol as well as participant experiences (via semi-structured interviews) with the protocol were used to assess feasibility and acceptability of the method. Results Adherence to the study protocol was excellent. Participants wore the wristband for a mean of 9.7 days. Participants completed a mean of 24.5 EMAs (out of 30). Semi-structured interviews with participants revealed that a small number of participants had difficulties uploading data from the wristband. The timing of EMAs was challenging for some participants, resulting in missed EMAs, raising questions about whether EMA frequency and timing could be changed to improve adherence. Some EMA items were difficult to answer due to the nature of participants’ roles and the work undertaken. Overall, the protocol was feasible and acceptable but highlighted future potential changes including using a different physiological data collection tool, reducing the number of EMAs, adjusting EMA timings, and reviewing EMA items.

Objectives: This study aimed to assess the feasibility and acceptability of using EMA questionnaires and physiological data via wristbands to measure day-to-day occupational stress, musculoskeletal pain, and mental health among university employees (N = 23), across 10 work days. Adherence to the study protocol as well as participant experiences (via semi-structured interviews) with the protocol were used to assess feasibility and acceptability of the method. Results: Adherence to the study protocol was excellent. Participants wore the wristband for a mean of 9.7 days. Participants completed a mean of 24.5 EMAs (out of 30). Semi-structured interviews with participants revealed that a small number of participants had difficulties uploading data from the wristband. The timing of EMAs was challenging for some participants, resulting in missed EMAs, raising questions about whether EMA frequency and timing could be changed to improve adherence. Some EMA items were difficult to answer due to the nature of participants' roles and the work undertaken. Overall, the protocol was feasible and acceptable but highlighted future potential changes including using a different physiological data collection tool, reducing the number of EMAs, adjusting EMA timings, and reviewing EMA items. [ABSTRACT FROM AUTHOR]

Objective: In this work we intend to design a system to classify human arousal at five levels (i.e., five stress levels) using four peripheral bio signals including photo-plethysmography measurements (PPG), galvanic skin response (GSR), thorax respiration (TR) and abdominal respiration (AR). Method: A total of 98 young people voluntarily participated in this study, including 65 men and 33 women with an average age of 24.48 ± 4.26 years. We induced five levels of mental stress in subjects through the Stroop test. A range of physiological features from different analysis domains, including statistical, frequency, and geometrical analyzes, as well as recurrence quantification analysis (RQA) and detrended fluctuation analysis (DFA) were extracted to find out the best arousal-related features and to correlate them with arousal states. Classification of the five arousal levels is performed by a simple naïve Bayes classifier. Results: Accuracies of 58.45%, 57.1% and 69.13% were obtained using linear features, nonlinear features and a combination of them, respectively. The combination of linear and nonlinear features resulted in the largest average accuracy of 69.13%, ICC of 88.12% and F1 of 69.43% values in the classification of five levels of mental stress. Conclusion: This paper suggested that combining linear and nonlinear dynamic methods for the analysis of physiological data could help improve the accuracy of the recognition of arousal levels. However, it should be noted that combining multiple modalities (here, PPG, GSR and respiration modalities) by equally weighting them may not always be a good approach to improve accuracy. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Medical Equipment Journal is the property of Chinese Medical Equipment Journal Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

AbstractThis paper presents a one-dimensional model that describes fluid flow in lymphangions, the segments of lymphatic vessels between valves, using quasilinear hyperbolic systems. The model incorporates a phenomenological pressure-cross-sectional area relationship based on existing literature. Numerical solutions of the differential equations align with known results, offering insights into lymphatic flow dynamics. This model enhances the understanding of lymph movement through the lymphatic system, driven by lymphangion contractions. [ABSTRACT FROM AUTHOR]

Introduction: The preferences of people with profound intellectual and multiple disabilities (PIMD) often remain unfulfilled since it stays challenging to decode their idiosyncratic behavior resulting in a negative impact on their quality of life (QoL). Physiological data (i.e. heart rate (variability) and motion data) might be the missing piece for identifying emotions of people with PIMD, which positively affects their QoL. Method: Machine learning (ML) processes and statistical analyses are integrated to discern and predict the potential relationship between physiological data and emotional states (i.e. numerical emotional states, descriptive emotional states and emotional arousal) in everyday interactions and activities of two participants with PIMD. Results: Emotional profiles were created enabling a differentiation of the individual emotional behavior. Using ML classifiers and statistical analyses, the results regarding the phases partially confirm previous research, and the findings for the descriptive emotional states were good and even better for the emotional arousal. Conclusion: The results show the potential of the emotional profiles especially for practitioners and the possibility to get a better insight into the emotional experience of people with PIMD including physiological data. This seems to be the missing piece to better recognize emotions of people with PIMD with a positive impact on their QoL. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Distance Education (1672-0008) is the property of Zhejiang Open University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Abstract With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias. The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git .

Previous studies have solely focused on establishing Machine Learning (ML) models for automated detection of stress arousal. However, these studies do not recognize stress appraisal and presume stress is a negative mental state. Yet, stress can be classified according to its influence on individuals; the way people perceive a stressor determines whether the stress reaction is considered as eustress (positive stress) or distress (negative stress). Thus, this study aims to assess the potential of using an ML approach to determine stress appraisal and identify eustress and distress instances using physiological and behavioral features. The results indicate that distress leads to higher perceived stress arousal compared to eustress. An XGBoost model that combined physiological and behavioral features using a 30 second time window had 83.38% and 78.79% F1-scores for predicting eustress and distress, respectively. Gender-based models resulted in an average increase of 2-4% in eustress and distress prediction accuracy. Finally, a model to predict the simultaneous assessment of eustress and distress, distinguishing between pure eustress, pure distress, eustress-distress coexistence, and the absence of stress achieved a moderate F1-score of 65.12%. The results of this study lay the foundation for work management interventions to maximize eustress and minimize distress in the workplace. [ABSTRACT FROM AUTHOR]

AbstractMetaphorical auditory displays are increasingly recognized for data presentation and self-reflection. This article presents the design and evaluation of RainMind, a web-based soundscape application that represents physiological data with dynamic natural sounds for daily stress reflection. Based on different combinations of auditory display with visualization, we identified three modes: the Visual-Aided Mode (VAM), the Audio-Aided Mode (AAM), and the Audio-Visual Mode (AVM). Through a within-subject study involving 30 participants, we conducted a mixed-methods evaluation to assess the task load, engagement, and user experience among the three modes. The findings indicated that the combination of dynamic natural soundscapes with visualization (AVM) contributes to a lower task load compared to the other two modes. Moreover, dynamic natural soundscapes as metaphors for stress data significantly enhanced engagement and user experience of self-reflection compared to static natural sounds. Based on our study, we discuss the potential of leveraging dynamic natural soundscapes as a new way of data-driven self-reflection. [ABSTRACT FROM AUTHOR]

With the outbreak of COVID-19 in 2020, countries worldwide faced significant concerns and challenges. Various studies have emerged utilizing Artificial Intelligence (AI) and Data Science techniques for disease detection. Although COVID-19 cases have declined, there are still cases and deaths around the world. Therefore, early detection of COVID-19 before the onset of symptoms has become crucial in reducing its extensive impact. Fortunately, wearable devices such as smartwatches have proven to be valuable sources of physiological data, including Heart Rate (HR) and sleep quality, enabling the detection of inflammatory diseases. In this study, we utilize an already-existing dataset that includes individual step counts and heart rate data to predict the probability of COVID-19 infection before the onset of symptoms. We train three main model architectures: the Gradient Boosting classifier (GB), CatBoost trees, and TabNet classifier to analyze the physiological data and compare their respective performances. We also add an interpretability layer to our best-performing model, which clarifies prediction results and allows a detailed assessment of effectiveness. Moreover, we created a private dataset by gathering physiological data from Fitbit devices to guarantee reliability and avoid bias. The identical set of models was then applied to this private dataset using the same pre-trained models, and the results were documented. Using the CatBoost tree-based method, our best-performing model outperformed previous studies with an accuracy rate of 85% on the publicly available dataset. Furthermore, this identical pre-trained CatBoost model produced an accuracy of 81% when applied to the private dataset. You will find the source code in the link: https://github.com/OpenUAE-LAB/Covid-19-detection-using-Wearable-data.git. [ABSTRACT FROM AUTHOR]

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot's ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots' MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots' cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots' SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots. [ABSTRACT FROM AUTHOR]

The transition to Industry 4.0 and 5.0 underscores the need for integrating humans into manufacturing processes, shifting the focus towards customization and personalization rather than traditional mass production. However, human performance during task execution may vary. To ensure high human–robot teaming (HRT) performance, it is crucial to predict performance without negatively affecting task execution. Therefore, to predict performance indirectly, significant factors affecting human performance, such as engagement and task load (i.e., amount of cognitive, physical, and/or sensory resources required to perform a particular task), must be considered. Hence, we propose a framework to predict and maximize the HRT performance. For the prediction of task performance during the development phase, our methodology employs features extracted from physiological data as inputs. The labels for these predictions—categorized as accurate performance or inaccurate performance due to high/low task load—are meticulously crafted using a combination of the NASA TLX questionnaire, records of human performance in quality control tasks, and the application of Q-Learning to derive task-specific weights for the task load indices. This structured approach enables the deployment of our model to exclusively rely on physiological data for predicting performance, thereby achieving an accuracy rate of 95.45% in forecasting HRT performance. To maintain optimized HRT performance, this study further introduces a method of dynamically adjusting the robot's speed in the case of low performance. This strategic adjustment is designed to effectively balance the task load, thereby enhancing the efficiency of human–robot collaboration. [ABSTRACT FROM AUTHOR]

BackgroundHigh-voltage workers often experience fatigue due to the physically demanding nature of climbing in dynamic and complex environments, which negatively impacts their motor and mental abilities. Effective monitoring is necessary to ensure safety.MethodsThis study proposed an experimental method to quantify fatigue in climbing operations. We collected subjective fatigue (using the RPE scale) and objective fatigue data, including systolic blood pressure (SBP), diastolic blood pressure (DBP), blood oxygen saturation (SpO2), vital capacity (VC), grip strength (GS), response time (RT), critical fusion frequency (CFF), and heart rate (HR) from 33 high-voltage workers before and after climbing tasks. The XGBoost algorithm was applied to establish a fatigue identification model.ResultsThe analysis showed that the physiological indicators of SpO2, VC, GS, RT, and CFF can effectively evaluate fatigue in climbing operations. The XGBoost fatigue identification model, based on subjective fatigue and the five physiological indicators, achieved an average accuracy of 89.75%.ConclusionThis study provides a basis for personalized management of fatigue in climbing operations, enabling timely detection of their fatigue states and implementation of corresponding measures to minimize the likelihood of accidents.

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.

Abstract Background Upper and lower extremity muscle strength can be used to predict health outcomes. However, the difference between the relation of upper extremity muscle and of lower extremity muscle with physiological factors is unclear. This study aimed to evaluate the association between physiological data and muscle strength, measured using grip and leg extension strength, among Japanese adults. Methods We conducted a cross-sectional study of 2,861 men and 6,717 women aged ≥ 20 years living in Miyagi Prefecture, Japan. Grip strength was measured using a dynamometer. Leg extension strength was measured using a hydraulic isokinetic leg press machine. Anthropometry and physiological data, including blood pressure, calcaneal ultrasound bone status, pulmonary function, carotid echography, and blood information, were assessed. We used a general linear model adjusted for age, body composition, and smoking status to evaluate the association between muscle strength and physiological factors. Results Grip and leg extension strength were positively associated with bone area ratio, vital capacity, forced vital capacity, forced expiratory volume in one second, and estimated glomerular filtration rate, and negatively associated with waist circumference and percentage body fat mass in both the sexes. Diastolic blood pressure was positively associated with grip strength in both the sexes and leg extension strength in men, but not women. High-density lipoprotein cholesterol and red blood cell counts were positively associated with grip and leg extension strength in women, but not men. In both the sexes, pulse rate, total cholesterol, and uric acid were consistently associated with only leg extension strength, but not grip strength. In women, glycated hemoglobin demonstrated negative and positive associations with grip and leg extension strength, respectively. Conclusions Grip and leg extension strength demonstrated similar associations with anthropometry, pulmonary function, and estimated glomerular filtration rate, but the associations with the other factors were not always consistent.

This study explores the impact of Indoor Environment Quality (IEQ) on the health and productivity of office workers in an office building in Fujisawa, Kanagawa, Japan. Previous studies have shown that IEQ can affect the physiological responses of occupants, such as of skin temperature, heart rate, and metabolic rate, which are indicators of health and productivity. However, most studies took place in controlled laboratory environments, which may not accurately represent real-life experiences. The study collected subjective and objective data from actual occupied office space, including on perceptions of IEQ, health, and productivity, and measurements of IEQ parameters such as on the thermal environment, light environment, indoor air quality, and acoustics. The study used correlation and linear regression methods to examine the relationship between IEQ, physiological data, and subjective responses to health and productivity. The stable thermal environment and low physical intensity of office work may contribute to the weak correlation between physiological data, thermal responses, and health–productivity variables. The results of this study can provide insights into how IEQ affects the psychological responses, well-being, and performance of office workers in real-world settings.

Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches. [ABSTRACT FROM AUTHOR]

AbstractThe evaluation of human responses in multimedia experiences using physiological data has a well-established presence in the academic literature. However, this field is currently undergoing transformative changes, driven by the accessibility of diverse and cost-effective devices, innovative software analysis methods, and the emergence of novel application domains such as Virtual and Augmented Reality and mulsemedia. To address the imperative of contextualizing these evolving trends in a contemporary context, this paper presents a systematic review with the objective of delineating the array of physiological data utilized in assessing Quality of Experience (QoE) and User Experience (UX) in multimedia studies. It also examines the devices employed for data collection and the analytical techniques applied to interpret the acquired data. While our review exposes both constraints and promising discoveries in these domains, it also emphasizes the escalating significance and practicality of leveraging physiological data in user assessments, especially as the boundaries between the physical and digital domains continue to blur. [ABSTRACT FROM AUTHOR]

One of the main paradigms of Industry 5.0 is represented by human-robot collaboration (HRC), which aims to support humans in production processes. However, working entire shifts in close contact with a robotic system may introduce new hazards from a cognitive ergonomics perspective. This paper presents a methodological approach to monitor the evolution of the operator's psychophysical state noninvasively in shifts of a repetitive assembly process, focusing on stress, mental workload, and fatigue. Through the use of non-invasive biosensors, it is possible to obtain objective information, even in real time, on the operator's cognitive load and stress in a naturalistic manner (i.e., without interrupting or hindering the process). In the HRC setting, recognition of the operator's psychophysical state is the first step in supporting his or her well-being and can provide clues to improve collaboration. The proposed method was applied to a case study aimed at comparing shifts performed both manually and with a cobot of a repetitive assembly process. The results showed significant differences in terms of process performance evolution and psychophysical state of the operator. In particular, the presence of the cobot resulted in fewer process failures, stress and cognitive load especially in the first phase of the work shift. The case study analyzed also showed the adequacy of noninvasively collected physiological data in providing important information on the evolution of the operator's stress, cognitive load, and fatigue. [ABSTRACT FROM AUTHOR]

IntroductionIt is unknown if physiological changes associated with chronic pain could be measured with inexpensive physiological sensors. Recently, acute pain and laboratory-induced pain have been quantified with physiological sensors.ObjectivesTo investigate the extent to which chronic pain can be quantified with physiological sensors.MethodsData were collected from chronic pain sufferers who subjectively rated their pain on a 0 to 10 visual analogue scale, using our recently developed pain meter. Physiological variables, including pulse, temperature, and motion signals, were measured at head, neck, wrist, and finger with multiple sensors. To quantify pain, features were first extracted from 10-second windows. Linear models with recursive feature elimination were fit for each subject. A random forest regression model was used for pain score prediction for the population-level model.ResultsPredictive performance was assessed using leave-one-recording-out cross-validation and nonparametric permutation testing. For individual-level models, 5 of 12 subjects yielded intraclass correlation coefficients between actual and predicted pain scores of 0.46 to 0.75. For the population-level model, the random forest method yielded an intraclass correlation coefficient of 0.58. Bland-Altman analysis shows that our model tends to overestimate the lower end of the pain scores and underestimate the higher end.ConclusionThis is the first demonstration that physiological data can be correlated with chronic pain, both for individuals and populations. Further research and more extensive data will be required to assess whether this approach could be used as a "chronic pain meter" to assess the level of chronic pain in patients.

Deep-learning algorithms hold promise in processing physiological signal data, including electrocardiograms (ECGs) and electroencephalograms (EEGs). However, healthcare often requires long-term monitoring, posing a challenge to traditional deep-learning models. These models are generally trained once and then deployed, which limits their ability to adapt to the dynamic and evolving nature of healthcare scenarios. Continual learning—known for its adaptive learning capabilities over time—offers a promising solution to these challenges. However, there remains an absence of consolidated literature, which reviews the techniques, applications, and challenges of continual learning specific to physiological signal analysis, as well as its future directions. Bridging this gap, our review seeks to provide an overview of the prevailing techniques and their implications for smart healthcare. We delineate the evolution from traditional approaches to the paradigms of continual learning. We aim to offer insights into the challenges faced and outline potential paths forward. Our discussion emphasizes the need for benchmarks, adaptability, computational efficiency, and user-centric design in the development of future healthcare systems. [ABSTRACT FROM AUTHOR]

Cognitive load has been widely studied to help understand human performance. It is desirable to monitor user cognitive load in applications such as automation, robotics, and aerospace to achieve operational safety and to improve user experience. This can allow efficient workload management and can help to avoid or to reduce human error. However, tracking cognitive load in real time with high accuracy remains a challenge. Hence, we propose a framework to detect cognitive load by non-intrusively measuring physiological data from the eyes and heart. We exemplify and evaluate the framework where participants engage in a task that induces different levels of cognitive load. The framework uses a set of classifiers to accurately predict low, medium and high levels of cognitive load. The classifiers achieve high predictive accuracy. In particular, Random Forest and Naive Bayes performed best with accuracies of 91.66% and 85.83% respectively. Furthermore, we found that, while mean pupil diameter change for both right and left eye were the most prominent features, blinking rate also made a moderately important contribution to this highly accurate prediction of low, medium and high cognitive load. The existing results on accuracy considerably outperform prior approaches and demonstrate the applicability of our framework to detect cognitive load. [ABSTRACT FROM AUTHOR]

Anthropomorphic appearance is a key factor to affect users' attitudes and emotions. This research aimed to measure emotional experience caused by robots' anthropomorphic appearance with three levels – high, moderate, and low – using multimodal measurement. Fifty participants' physiological and eye-tracker data were recorded synchronously while they observed robot images that were displayed in random order. Afterward, the participants reported subjective emotional experiences and attitudes towards those robots. The results showed that the images of the moderately anthropomorphic service robots induced higher pleasure and arousal ratings, and yielded significantly larger pupil diameter and faster saccade velocity, than did the low or high robots. Moreover, participants' facial electromyography, skin conductance, and heart-rate responses were higher when observing moderately anthropomorphic service robots. An implication of the research is that service robots' appearance should be designed to be moderately anthropomorphic; too many human-like features or machine-like features may disturb users' positive emotions and attitudes. Practitioner Summary: This research aimed to measure emotional experience caused by three types of anthropomorphic service robots using a multimodal measurement experiment. The results showed that moderately anthropomorphic service robots evoked more positive emotion than high and low anthropomorphic robots. Too many human-like features or machine-like features may disturb users' positive emotions. [ABSTRACT FROM AUTHOR]

Research in severe traumatic brain injury (TBI) has historically been limited by studies with relatively small sample sizes that result in low power to detect small, yet clinically meaningful outcomes. Data sharing and integration from existing sources hold promise to yield larger more robust sample sizes that improve the potential signal and generalizability of important research questions. However, curation and harmonization of data of different types and of disparate provenance is challenging. We report our approach and experience integrating multiple TBI data sets containing collected physiological data, including both expected and unexpected challenges encountered in the integration process. Our harmonized data set included data on 1536 patients from the Citicoline Brain Injury Treatment Trial (COBRIT), Effect of erythropoietin and transfusion threshold on neurological recovery after traumatic brain injury: a randomized clinical trial (EPO Severe TBI), BEST-TRIP, Progesterone for the Treatment of Traumatic Brain Injury III Clinical Trial (ProTECT III), Transforming Research and Clinical Knowledge in Traumatic brain Injury (TRACK-TBI), Brain Oxygen Optimization in Severe Traumatic Brain Injury Phase-II (BOOST-2), and Ben Taub General Hospital (BTGH) Research Database studies. We conclude with process recommendations for data acquisition for future prospective studies to aid integration of these data with existing studies. These recommendations include using common data elements whenever possible, a standardized recording system for labeling and timing of high-frequency physiological data, and secondary use of studies in systems such as Federal Interagency Traumatic Brain Injury Research Informatics System (FITBIR), to engage investigators who collected the original data. [ABSTRACT FROM AUTHOR]

This paper addresses the need for collecting and labeling affect-related data in ecological settings. Collecting the annotations in the wild is a very challenging task, which, however, is crucial for the creation of datasets and emotion recognition models. We propose a novel solution to collect and annotate such data: a questionnaire based on the appraisal theory, that is accessible through an open-source mobile application. Our approach exploits a commercially available wearable physiological sensor connected to a smartphone. The app detects potentially relevant events from the physiological data, and prompts the users to report their emotions using a novel questionnaire based on the Ortony, Clore, and Collins (OCC) Model. The questionnaire is designed to gather information about the appraisal process concerning the significant event. The app guides a user through the reporting process by posing a series of questions related to the event. As a result, the annotated data can be used, e.g., to develop emotion recognition models. In the paper, we analyze users' reports. To validate the questionnaire, we asked 22 individuals to use the app and the sensor for a week. The analysis of the collected annotations shed new light on self-assessment in terms of appraisals. We compared a proposed method with two commonly used methods for reporting affect-related events: (1) a two-dimensional model of valence and arousal, and (2) a forced-choice list of 22 labels. According to the results, appraisal-based reports largely corresponded to the self-reported values of arousal and valence, but they differed substantially from the labels provided with a forced-choice list. In the latter case, when using the forced-choice list, individuals primarily selected labels of basic emotions such as anger or joy. However, they reported a greater variety of emotional states when using appraisal theory for self-assessment of the same events. Thus, proposed approach aids participants to focus on potential causes of their states, facilitating more precise reporting. We also found that regardless of the reporting mode (mandatory vs. voluntary reporting), the ratio between positive and negative reports remained stable. The paper concludes with a list of guidelines to consider in future data collections using self-assessment.

Because stress has such a powerful impact on human health, we must be able to identify it automatically in our everyday lives. The human activity recognition (HAR) system use data from several kinds of sensors to try to recognize and evaluate human actions automatically recognize and evaluate human actions. Using the multimodal dataset DEAP (Database for Emotion Analysis using Physiological Signals), this paper presents deep learning (DL) technique for effectively detecting human stress. The combination of vision-based and sensor-based approaches for recognizing human stress will help us achieve the increased efficiency of current stress recognition systems and predict probable actions in advance of when fatal. Based on visual and EEG (Electroencephalogram) data, this research aims to enhance the performance and extract the dominating characteristics of stress detection. For the stress identification test, we utilized the DEAP dataset, which included video and EEG data. We also demonstrate that combining video and EEG characteristics may increase overall performance, with the suggested stochastic features providing the most accurate results. In the first step, CNN (Convolutional Neural Network) extracts feature vectors from video frames and EEG data. Feature Level (FL) fusion that combines the features extracted from video and EEG data. We use XGBoost as our classifier model to predict stress, and we put it into action. The stress recognition accuracy of the proposed method is compared to existing methods of Decision Tree (DT), Random Forest (RF), AdaBoost, Linear Discriminant Analysis (LDA), and KNearest Neighborhood (KNN). When we compared our technique to existing state-of-the-art approaches, we found that the suggested DL methodology combining multimodal and heterogeneous inputs may improve stress identification. [ABSTRACT FROM AUTHOR]

BackgroundThe ability to maintain attention is crucial for achieving success in various aspects of life, including academic pursuits, career advancement, and social interactions. Attention deficit disorder (ADD) is a common symptom associated with autism spectrum disorder (ASD), which can pose challenges for individuals affected by it, impacting their social interactions and learning abilities. To address this issue, virtual reality (VR) has emerged as a promising tool for attention training with the ability to create personalized virtual worlds, providing a conducive platform for attention-focused interventions. Furthermore, leveraging physiological data can be instrumental in the development and enhancement of attention-training techniques for individuals.MethodsIn our preliminary study, a functional prototype for attention therapy systems was developed. In the current phase, the objective is to create a framework called VR-PDA (Virtual Reality Physiological Data Analysis) that utilizes physiological data for tracking and improving attention in individuals. Four distinct training strategies such as noise, score, object opacity, and red vignette are implemented in this framework. The primary goal is to leverage virtual reality technology and physiological data analysis to enhance attentional capabilities.ResultsOur data analysis results revealed that reinforcement training strategies are crucial for improving attention in individuals with ASD, while they are not significant for non-autistic individuals. Among all the different strategies employed, the noise strategy demonstrates superior efficacy in training attention among individuals with ASD. On the other hand, for Non-ASD individuals, no specific training proves to be effective in enhancing attention. The total gazing time feature exhibited benefits for participants with and without ASD.DiscussionThe results consistently demonstrated favorable outcomes for both groups, indicating an enhanced level of attentiveness. These findings provide valuable insights into the effectiveness of different strategies for attention training and emphasize the potential of virtual reality (VR) and physiological data in attention training programs for individuals with ASD. The results of this study open up new avenues for further research and inspire future developments.