Analysis Of Physiological Data Research Articles

An Overview of Approaches and Methods for the Cognitive Workload Estimation in Human–Machine Interaction Scenarios through Wearables Sensors

Background: Human-Machine Interaction (HMI) has been an important field of research in recent years, since machines will continue to be embedded in many human actvities in several contexts, such as industry and healthcare. Monitoring in an ecological mannerthe cognitive workload (CW) of users, who interact with machines, is crucial to assess their level of engagement in activities and the required effort, with the goal of preventing stressful circumstances. This study provides a comprehensive analysis of the assessment of CW using wearable sensors in HMI. Methods: this narrative review explores several techniques and procedures for collecting physiological data through wearable sensors with the possibility to integrate these multiple physiological signals, providing a multimodal monitoring of the individuals’CW. Finally, it focuses on the impact of artificial intelligence methods in the physiological signals data analysis to provide models of the CW to be exploited in HMI. Results: the review provided a comprehensive evaluation of the wearables, physiological signals, and methods of data analysis for CW evaluation in HMI. Conclusion: the literature highlighted the feasibility of employing wearable sensors to collect physiological signals for an ecological CW monitoring in HMI scenarios. However, challenges remain in standardizing these measures across different populations and contexts.

Optimizing Human-Robot Teaming Performance through Q-Learning-Based Task Load Adjustment and Physiological Data Analysis.

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.

Linear and nonlinear analysis of multimodal physiological data for affective arousal recognition

Linear and nonlinear analysis of multimodal physiological data for affective arousal recognition

Regulation of a novel DsGATA1 from Dunaliella salina on the synthesis of carotenoids under red light.

Dunaliella salina is a high-quality industrial effector for carotenoid production. The mechanism by which red light regulates carotenoid synthesis is still unclear. In this study, a transcription factor of DsGATA1 with a distinct structure was discovered in D. salina. The recognition motif of DsGATA1 was comparable to that of plant and fungal GATA, despite its evolutionary proximity to animal-derived GATA. The expression of DsGATA1 in D. salina was still noticeably decreased when exposed to red light. Analysis of physiological and biochemical transcriptomic data from overexpressed, interfering, and wild-type strains of DsGATA1 revealed that DsGATA1 acts as a global regulator of D. salina carotenoid synthesis. The upregulated genes in the CBP pathway by DsGATA1 were involved in its regulation of the synthesis of carotenoids. DsGATA1 also enhanced carotenoid accumulation under red light by affecting N metabolism. DsGATA1 was found to directly bind to the promoter of nitrate reductase to activate its expression, promoting D. salina nitrate uptake and accelerating biomass accumulation. DsGATA1 affected the expression of the genes encoding GOGAT, GDH, and ammonia transporter proteins. Moreover, our study revealed that the regulation of N metabolism by DsGATA1 led to the production of NO molecules that inhibited carotenoid synthesis. However, DsGATA1 significantly enhanced carotenoid synthesis by NO scavenger removal of NO. The D. salina carotenoid accumulation under red light was elevated by 46% in the presence of overexpression of DsGATA1 and NO scavenger. Nevertheless, our results indicated that DsGATA1 could be an important target for engineering carotenoid production. KEY POINTS: • DsGATA1 with a distinct structure and recognition motif was found in D. salina • DsGATA1 enhanced carotenoid production and biomass in D. salina under red light • DsGATA1 is involved in the regulation of N metabolism and carotenoid synthesis.

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

<abstract> <p>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.</p> </abstract>

[Development of intelligent monitoring system based on Internet of Things and wearable technology and exploration of its clinical application mode].

Wearable monitoring, which has the advantages of continuous monitoring for a long time with low physiological and psychological load, represents a future development direction of monitoring technology. Based on wearable physiological monitoring technology, combined with Internet of Things (IoT) and artificial intelligence technology, this paper has developed an intelligent monitoring system, including wearable hardware, ward Internet of Things platform, continuous physiological data analysis algorithm and software. We explored the clinical value of continuous physiological data using this system through a lot of clinical practices. And four value points were given, namely, real-time monitoring, disease assessment, prediction and early warning, and rehabilitation training. Depending on the real clinical environment, we explored the mode of applying wearable technology in general ward monitoring, cardiopulmonary rehabilitation, and integrated monitoring inside and outside the hospital. The research results show that this monitoring system can be effectively used for monitoring of patients in hospital, evaluation and training of patients' cardiopulmonary function, and management of patients outside hospital.

OsbZIP53 Negatively Regulates Immunity Response by Involving in Reactive Oxygen Species and Salicylic Acid Metabolism in Rice

The basic region/leucine zipper (bZIP) transcription factors play important roles in plant development and responses to abiotic and biotic stresses. OsbZIP53 regulates resistance to Magnaporthe oryzae in rice by analyzing APIP5-RNAi transgenic plants. To further investigate the biological functions of OsbZIP53, we generated osbzip53 mutants using CRISPR/Cas9 editing and also constructed OsbZIP53 over-expression transgenic plants. Comprehensive analysis of phenotypical, physiological, and transcriptional data showed that knocking-out OsbZIP53 not only improved disease resistance by inducing a hypersensitivity response in plants, but also regulated the immune response through the salicylic acid pathway. Specifically, disrupting OsbZIP53 increased H2O2 accumulation by promoting reactive oxygen species generation through up-regulation of several respiratory burst oxidase homologs (Osrboh genes) and weakened H2O2 degradation by directly targeting OsMYBS1. In addition, the growth of osbzip53 mutants was seriously impaired, while OsbZIP53 over-expression lines displayed a similar phenotype to the wild type, suggesting that OsbZIP53 has a balancing effect on rice immune response and growth.

Causal Analysis of Physiological Sleep Data Using Granger Causality and Score-Based Structure Learning

Understanding how the human body works during sleep and how this varies in the population is a task with significant implications for medicine. Polysomnographic studies, or sleep studies, are a common diagnostic method that produces a significant quantity of time-series sensor data. This study seeks to learn the causal structure from data from polysomnographic studies carried out on 600 adult volunteers in the United States. Two methods are used to learn the causal structure of these data: the well-established Granger causality and “DYNOTEARS”, a modern approach that uses continuous optimisation to learn dynamic Bayesian networks (DBNs). The results from the two methods are then compared. Both methods produce graphs that have a number of similarities, including the mutual causation between electrooculogram (EOG) and electroencephelogram (EEG) signals and between sleeping position and SpO2 (blood oxygen level). However, DYNOTEARS, unlike Granger causality, frequently finds a causal link to sleeping position from the other variables. Following the creation of these causal graphs, the relationship between the discovered causal structure and the characteristics of the participants is explored. It is found that there is an association between the waist size of a participant and whether a causal link is found between the electrocardiogram (ECG) measurement and the EOG and EEG measurements. It is concluded that a person’s body shape appears to impact the relationship between their heart and brain during sleep and that Granger causality and DYNOTEARS can produce differing results on real-world data.

NLDyn - An open source MATLAB toolbox for the univariate and multivariate nonlinear dynamical analysis of physiological data

Background and ObjectiveWe present NLDyn, an open-source MATLAB toolbox tailored for in-depth analysis of nonlinear dynamics in biomedical signals. Our objective is to offer a user-friendly yet comprehensive platform for researchers to explore the intricacies of time series data. MethodsNLDyn integrates approximately 80 distinct methods, encompassing both univariate and multivariate nonlinear dynamics, setting it apart from existing solutions. This toolbox combines state-of-the-art nonlinear dynamical techniques with advanced multivariate entropy methods, providing users with powerful analytical capabilities. NLDyn enables analyses with or without a sliding window, and users can easily access and customize default parameters. ResultsNLDyn generates results that are both exportable and visually informative, facilitating seamless integration into research and presentations. Its ongoing development ensures it remains at the forefront of nonlinear dynamics analysis. ConclusionsNLDyn is a valuable resource for researchers in the biomedical field, offering an intuitive interface and a wide array of nonlinear analysis tools. Its integration of advanced techniques empowers users to gain deeper insights from their data. As we continually refine and expand NLDyn's capabilities, we envision it becoming an indispensable tool for the exploration of complex dynamics in biomedical signals.

The smarty4covid dataset and knowledge base as a framework for interpretable physiological audio data analysis

Harnessing the power of Artificial Intelligence (AI) and m-health towards detecting new bio-markers indicative of the onset and progress of respiratory abnormalities/conditions has greatly attracted the scientific and research interest especially during COVID-19 pandemic. The smarty4covid dataset contains audio signals of cough (4,676), regular breathing (4,665), deep breathing (4,695) and voice (4,291) as recorded by means of mobile devices following a crowd-sourcing approach. Other self reported information is also included (e.g. COVID-19 virus tests), thus providing a comprehensive dataset for the development of COVID-19 risk detection models. The smarty4covid dataset is released in the form of a web-ontology language (OWL) knowledge base enabling data consolidation from other relevant datasets, complex queries and reasoning. It has been utilized towards the development of models able to: (i) extract clinically informative respiratory indicators from regular breathing records, and (ii) identify cough, breath and voice segments in crowd-sourced audio recordings. A new framework utilizing the smarty4covid OWL knowledge base towards generating counterfactual explanations in opaque AI-based COVID-19 risk detection models is proposed and validated.

MSE model incorporating Fourier decomposition method: HRV study of ECG signals for atrial fibrillation and congestive heart failure

Traditional techniques to evaluate the complexity of biological signals forgo the numerous time scales present in such data. When these algorithms were applied to real-world datasets gathered in health and illness states, they produced inconsistent results. Sample entropy (SampEn) has been utilized extensively to evaluate the complexity of RR-interval time series. Using multi-scale Entropy analysis incorporating Sample Entropy, several research on the complexity of physiological signals have been conducted. The primary disadvantage of MSE is that coarse-graining results in a noticeable and considerable loss of information. For small scales, the method proves to be ineffective. So, for the MSE analysis of physiological data, we have employed the FDM rather than coarse-graining. This FDM, which is based on the Fourier theory, is excellent for studying nonlinear and nonstationary time series. This approach generates a TFE distribution that displays the data’s fundamental premise. This will provide a number of frequency bands with varying cut-off frequencies but consistent data across all of them. To further validate our suggested technique, we used “ANOVA analysis” to compare our results to the entropy analysis of a synthetic simulated database containing white noise (WN) and power noise (PN) signals.

Enhancing attention in autism spectrum disorder: comparative analysis of virtual reality-based training programs using physiological data

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.

Melatonin enhances drought tolerance by affecting jasmonic acid and lignin biosynthesis in wheat (Triticum aestivum L.)

Drought severely affects the yield of wheat (Triticum aestivum L.), which is mainly grown in arid and semi-arid regions. Melatonin plays an important role in various types of stress resistance in plants, including drought resistance. However, the molecular mechanism through which melatonin affects drought tolerance remains largely unknown. In this study, we revealed that melatonin (100 μM) significantly improved drought resistance during the maturation stage of Chinese Spring, Shi4185, and Hanxuan10 varieties, but not Chang6878. Further physiological, transcriptomic, and proteomic data analysis at the wheat seedling stage revealed that melatonin increased jasmonic acid (JA) content, upregulating the expression of JA genes (LOX1.5 and LOX2.1) and two transcription factors (HY5 and MYB86) under drought conditions. It also upregulated genes related to lignin biosynthesis (4CL2, P5CS1, and CCR2) as well as starch and sucrose metabolism (PME53 and SUS4). Additionally, melatonin alleviated photosynthetic and cell membrane damage caused by drought stress through maintaining low levels of hydrogen peroxide. The current results elucidate melatonin-regulated pathways in wheat and provide evidence for using melatonin as a potential biostimulant to improve wheat drought resistance under field conditions in the future.

Toxicity assessment of poultry-waste biosynthesized nanosilver in Anabas testudineus (Bloch, 1792) for responsible and sustainable aquaculture development-A multi-biomarker approach

The current study investigates the potential utilization of poultry intestines for the synthesis of stable silver nanoparticles (AgNPs) and their impact on fish physiology. The AgNPs were synthesized and characterized using various analytical techniques. The toxicity of AgNPs on Anabas testudineus was evaluated, determining a 96-h LC50 value of 25.46 mg l−1. Subsequently, fish were exposed to concentrations corresponding to 1/10th, 1/25th, 1/50th, and 1/100th of the estimated LC50 for a duration of 60 days in a sub-acute study. A comprehensive range of biomarkers, including haematological, serum, oxidative stress, and metabolizing markers, were analyzed to assess the physiological responses of the fish. Additionally, histopathological examinations were conducted, and the accumulation of silver in biomarker organs was measured. The results indicate that silver tends to bioaccumulate in all biomarker organs in a dose- and time-dependent manner, except for the muscle tissue, where accumulation initially increased and subsequently decreased, demonstrating the fish's inherent ability for natural attenuation. Analysis of physiological data and integrated biomarker responses reveal that concentrations of 1/10th, 1/25th, and 1/50th of the LC50 can induce stress in the fish, while exposure to 1/100th of the LC50 shows minimal to no stress response. Overall, this study provides valuable insights into the toxicity and physiological responses of fish exposed to poultry waste biosynthesized AgNPs, offering potential applications in aquaculture while harnessing their unique features.

Artificial warming affects sugar signals and flavonoid accumulation to improve female willows growth faster than males.

Increasing global warming is severely affecting tree growth and development. However, research on the sex-specific responses of dioecious trees to warming is scarce. Here, male and female Salix paraplesia were selected for artificial warming (an increase of 4°C relative to ambient temperature) to investigate the effects on morphological, physiological, biochemical and molecular responses. The results showed that warming significantly promoted the growth of female and male S. paraplesia, but females grew faster than males. Warming affected photosynthesis, chloroplast structures, peroxidase activity, proline, flavonoids, nonstructural carbohydrates (NSCs) and phenolic contents in both sexes. Interestingly, warming increased flavonoid accumulation in female roots and male leaves but inhibited it in female leaves and male roots. The transcriptome and proteome results indicated that differentially expressed genes and proteins were significantly enriched in sucrose and starch metabolism and flavonoid biosynthesis pathways. The integrative analysis of transcriptomic, proteomic, biochemical and physiological data revealed that warming changed the expression of SpAMY, SpBGL, SpEGLC and SpAGPase genes, resulting in the reduction of NSCs and starch and the activation of sugar signaling, particularly SpSnRK1s, in female roots and male leaves. These sugar signals subsequently altered the expression of SpHCTs, SpLAR and SpDFR in the flavonoid biosynthetic pathway, ultimately leading to the differential accumulation of flavonoids in female and male S. paraplesia. Therefore, warming causes sexually differential responses of S. paraplesia, with females performing better than males.

A Disentangled VAE-BiLSTM Model for Heart Rate Anomaly Detection.

Cardiovascular diseases (CVDs) remain a leading cause of death globally. According to the American Heart Association, approximately 19.1 million deaths were attributed to CVDs in 2020, in particular, ischemic heart disease and stroke. Several known risk factors for CVDs include smoking, alcohol consumption, lack of regular physical activity, and diabetes. The last decade has been characterized by widespread diffusion in the use of wristband-style wearable devices which can monitor and collect heart rate data, among other information. Wearable devices allow the analysis and interpretation of physiological and activity data obtained from the wearer and can therefore be used to monitor and prevent potential CVDs. However, these data are often provided in a manner that does not allow the general user to immediately comprehend possible health risks, and often require further analytics to draw meaningful conclusions. In this paper, we propose a disentangled variational autoencoder (β-VAE) with a bidirectional long short-term memory network (BiLSTM) backend to detect in an unsupervised manner anomalies in heart rate data collected during sleep time with a wearable device from eight heterogeneous participants. Testing was performed on the mean heart rate sampled both at 30 s and 1 min intervals. We compared the performance of our model with other well-known anomaly detection algorithms, and we found that our model outperformed them in almost all considered scenarios and for all considered participants. We also suggest that wearable devices may benefit from the integration of anomaly detection algorithms, in an effort to provide users more processed and straightforward information.

The Relationship between Course Evaluation and Academic Achievement of University Students Using Latent Profile Analysis

This study was conducted with the purpose of deriving a heterogeneous potential profile through the results of university lecture evaluation, which is students' perception of class and the product of professor-student interaction in the classroom, and identified the factors that affect it. In addition, the degree of learning flow for each potential profile was investigated and the difference was verified. For the analysis, 83,069 cases were used because of the university A course evaluation organized in the second semester of 2020, and a total of 12,919 subjects were studied. As a result of analyzing the aspects of course evaluation through class plan, content delivery, communication, response, and evaluation system, that were the sub-factors of course evaluation, the miscellaneous material profiles were classified in four. It was named as the upper group. As factors determining the latent profile using physiological data analysis. It was discovered that significant differences existed between student features (grade, major field), professor features (position), and lecture variables (category of accomplishment, lecture size). Students with lesser grades have a greater chance of succeeding quickly in the top group than do those in the humanities and social sciences, science, or engineering professions. The likelihood of being in the upper group in a course assessment as well as the likelihood of being in the upper group with higher course evaluation outcomes for general education lectures as opposed to major lectures and smaller lecture sizes increases with decreasing professor status. The level of academic obligation was then examined by potential profile based on the course evaluation outline, and the results revealed that the greater the course evaluation result, the greater the level of educational obligation. This is a significant study because it examines the variables that affect the outcomes of the university's course evaluations, which are done at the end of every semester, as well as the relationship between the outcomes of the course evaluations and academic commitment. This study established a scientific basis for colleges to prepare measures to improve the quality of education through lecture evaluation and emphasized the importance of preparing concrete measures to improve students' learning outcomes in college education.

Exploring Digital Biomarkers of Illness Activity in Mood Episodes: Hypotheses Generating and Model Development Study

Depressive and manic episodes within bipolar disorder (BD) and major depressive disorder (MDD) involve altered mood, sleep, and activity, alongside physiological alterations wearables can capture. Firstly, we explored whether physiological wearable data could predict (aim 1) the severity of an acute affective episode at the intra-individual level and (aim 2) the polarity of an acute affective episode and euthymia among different individuals. Secondarily, we explored which physiological data were related to prior predictions, generalization across patients, and associations between affective symptoms and physiological data. We conducted a prospective exploratory observational study including patients with BD and MDD on acute affective episodes (manic, depressed, and mixed) whose physiological data were recorded using a research-grade wearable (Empatica E4) across 3 consecutive time points (acute, response, and remission of episode). Euthymic patients and healthy controls were recorded during a single session (approximately 48 h). Manic and depressive symptoms were assessed using standardized psychometric scales. Physiological wearable data included the following channels: acceleration (ACC), skin temperature, blood volume pulse, heart rate (HR), and electrodermal activity (EDA). Invalid physiological data were removed using a rule-based filter, and channels were time aligned at 1-second time units and segmented at window lengths of 32 seconds, as best-performing parameters. We developed deep learning predictive models, assessed the channels' individual contribution using permutation feature importance analysis, and computed physiological data to psychometric scales' items normalized mutual information (NMI). We present a novel, fully automated method for the preprocessing and analysis of physiological data from a research-grade wearable device, including a viable supervised learning pipeline for time-series analyses. Overall, 35 sessions (1512 hours) from 12 patients (manic, depressed, mixed, and euthymic) and 7 healthy controls (mean age 39.7, SD 12.6 years; 6/19, 32% female) were analyzed. The severity of mood episodes was predicted with moderate (62%-85%) accuracies (aim 1), and their polarity with moderate (70%) accuracy (aim 2). The most relevant features for the former tasks were ACC, EDA, and HR. There was a fair agreement in feature importance across classification tasks (Kendall W=0.383). Generalization of the former models on unseen patients was of overall low accuracy, except for the intra-individual models. ACC was associated with "increased motor activity" (NMI>0.55), "insomnia" (NMI=0.6), and "motor inhibition" (NMI=0.75). EDA was associated with "aggressive behavior" (NMI=1.0) and "psychic anxiety" (NMI=0.52). Physiological data from wearables show potential to identify mood episodes and specific symptoms of mania and depression quantitatively, both in BD and MDD. Motor activity and stress-related physiological data (EDA and HR) stand out as potential digital biomarkers for predicting mania and depression, respectively. These findings represent a promising pathway toward personalized psychiatry, in which physiological wearable data could allow the early identification and intervention of mood episodes.

Cognitive physiological data analysis based on the XGBoost algorithm to realize positive perceptual sample classification

Designers refer to existing product cases and innovate products to develop new products. However, when designers screen product cases, there is no user participation, which leads to the lack of user-side knowledge and emotional drive that is very important for design. Therefore, it is necessary to play the role of user emotional knowledge in promoting the whole design process. This paper proposes the concept of the positive perceptual sample, which applies the knowledge emotion integration of designers and users to the screening sample case stage at the beginning of the design process. This study is based on the lack of user-side knowledge and emotional drive of reference cases and integrates user emotion into the reference case screening process. Then, in the emotion measurement process, users’ cognitive data in the screening process are obtained through the eye-brain fusion cognitive experiment. Finally, the XGBoost algorithm is used to process feature index data to realize the classification and recognition of cognitive data and applied to the positive perceptual classification of products. The results show that the classification accuracy of physiological cognitive data with user emotional representation by the XGBoost algorithm is 90.87%. The results of cognitive data classification are applied to the screening of positive perceptual samples, and the satisfaction rate is 98.35%. The results show that the method proposed in this paper provides a new source of ideas for obtaining positive perceptual samples and can be applied to new product development.

Complexity and Entropy in Physiological Signals (CEPS): Resonance Breathing Rate Assessed Using Measures of Fractal Dimension, Heart Rate Asymmetry and Permutation Entropy.

As technology becomes more sophisticated, more accessible methods of interpretating Big Data become essential. We have continued to develop Complexity and Entropy in Physiological Signals (CEPS) as an open access MATLAB® GUI (graphical user interface) providing multiple methods for the modification and analysis of physiological data. To demonstrate the functionality of the software, data were collected from 44 healthy adults for a study investigating the effects on vagal tone of breathing paced at five different rates, as well as self-paced and un-paced. Five-minute 15-s recordings were used. Results were also compared with those from shorter segments of the data. Electrocardiogram (ECG), electrodermal activity (EDA) and Respiration (RSP) data were recorded. Particular attention was paid to COVID risk mitigation, and to parameter tuning for the CEPS measures. For comparison, data were processed using Kubios HRV, RR-APET and DynamicalSystems.jl software. We also compared findings for ECG RR interval (RRi) data resampled at 4 Hz (4R) or 10 Hz (10R), and non-resampled (noR). In total, we used around 190-220 measures from CEPS at various scales, depending on the analysis undertaken, with our investigation focused on three families of measures: 22 fractal dimension (FD) measures, 40 heart rate asymmetries or measures derived from Poincaré plots (HRA), and 8 measures based on permutation entropy (PE). FDs for the RRi data differentiated strongly between breathing rates, whether data were resampled or not, increasing between 5 and 7 breaths per minute (BrPM). Largest effect sizes for RRi (4R and noR) differentiation between breathing rates were found for the PE-based measures. Measures that both differentiated well between breathing rates and were consistent across different RRi data lengths (1-5 min) included five PE-based (noR) and three FDs (4R). Of the top 12 measures with short-data values consistently within ± 5% of their values for the 5-min data, five were FDs, one was PE-based, and none were HRAs. Effect sizes were usually greater for CEPS measures than for those implemented in DynamicalSystems.jl. The updated CEPS software enables visualisation and analysis of multichannel physiological data using a variety of established and recently introduced complexity entropy measures. Although equal resampling is theoretically important for FD estimation, it appears that FD measures may also be usefully applied to non-resampled data.