Machine Learning models for the Identification of Cognitive Tasks using Autonomic Reactions from Heart Rate Variability and Electrodermal Activity

Wearable neurofeedback acceptance model for students' stress and anxiety management in academic settings

This study investigates the technology acceptance of a proposed multimodal wearable sensing framework, named mSense, within the context of non-invasive real-time neurofeedback for student stress and anxiety management. The COVID-19 pandemic has intensified mental health challenges, particularly for students. Non-invasive techniques, such as wearable biofeedback and neurofeedback devices, are suggested as potential solutions. To explore the acceptance and intention to use such innovative devices, this research applies the Technology Acceptance Model (TAM), based on the co-creation approach. An online survey was conducted with 106 participants, including higher education students, health researchers, medical professionals, and software developers. The TAM key constructs (usage attitude, perceived usefulness, perceived ease of use, and intention to use) were validated through statistical analysis, including Partial Least Square-Structural Equation Modeling. Additionally, qualitative analysis of open-ended survey responses was performed. Results confirm the acceptance of the mSense framework for neurofeedback-based stress and anxiety management. The study contributes valuable insights into factors influencing user intention to use multimodal wearable devices in educational settings. The findings have theoretical implications for technology acceptance and practical implications for extending the usage of innovative sensors in clinical and educational environments, thereby supporting both physical and mental health.

Sex differences in autonomic functions and cognitive performance during cold-air exposure and cold-water partial immersion

Introduction

This study investigated the differences between males and females in autonomic functions and cognitive performance during cold-air exposure and cold-water partial-immersion compared to a room temperature-air environment. Although several studies have investigated the effects of cold-air or cold-water exposures on autonomic function and cognitive performance, biological sex differences are often under-researched.

Methods

Twenty-two males and nineteen females participated in the current study. Subjects completed a battery of cognitive tasks based upon those used within the Defense Automated Neurobehavioral Assessment (DANA), consisting of five subtasks that assess simple and procedural reaction time, spatial manipulation, attention, and immediate memory. In total, subjects took the battery within a 15-minute period across 30-minute intervals throughout the duration of environmental exposure. Across three separate days, subjects were exposed to three different environmental conditions: room temperature air (23°C), cold air (10°C), and cold water (15°C; in which subjects were immersed up to their necks). Room temperature and cold-air conditions consisted of five sessions (about 2.5 h), and the cold-water condition consisted of three sessions (about 1.5 h). During each experimental condition, physiological data were collected to assess autonomic function, including electrodermal activity (EDA) data and heart rate variability (HRV) derived from electrocardiogram signals.

Results

Females showed slower reaction time in spatial manipulation tasks, immediate memory, and attention during cold-air exposures compared to room temperature air, whereas the performance of males were similar or better during cold-air exposures compared to room temperature air. Cold-water immersion affected the immediate memory performance of males. Both males and females exhibited smaller EDA amplitudes during cold-air and cold-water conditions compared to room temperature air. For HRV, only male subjects exhibited significantly greater values in low-frequency and very-low-frequency components during cold air exposure compared to the normal condition.

Discussion

Sex introduces important differences in cognitive performance and autonomic functions during exposure to cold-air and cold-water. Therefore, sex should be considered when assessing the autonomic nervous system in cold environments and when establishing optimal thermal clothing for performance in operational environments. Our findings can assist with determination of operational clothing, temperature in operating environment, and personnel deployment to operational sites, particularly in settings involving both males and females.

Wearable Biosensor Technology in Education: A Systematic Review

Wearable Biosensor Technology (WBT) has emerged as a transformative tool in the educational system over the past decade. This systematic review encompasses a comprehensive analysis of WBT utilization in educational settings over a 10-year span (2012-2022), highlighting the evolution of this field to address challenges in education by integrating technology to solve specific educational challenges, such as enhancing student engagement, monitoring stress and cognitive load, improving learning experiences, and providing real-time feedback for both students and educators. By exploring these aspects, this review sheds light on the potential implications of WBT on the future of learning. A rigorous and systematic search of major academic databases, including Google Scholar and Scopus, was conducted in accordance with the PRISMA guidelines. Relevant studies were selected based on predefined inclusion and exclusion criteria. The articles selected were assessed for methodological quality and bias using established tools. The process of data extraction and synthesis followed a structured framework. Key findings include the shift from theoretical exploration to practical implementation, with EEG being the predominant measurement, aiming to explore mental states, physiological constructs, and teaching effectiveness. Wearable biosensors are significantly impacting the educational field, serving as an important resource for educators and a tool for students. Their application has the potential to transform and optimize academic practices through sensors that capture biometric data, enabling the implementation of metrics and models to understand the development and performance of students and professors in an academic environment, as well as to gain insights into the learning process.

Diagnosis Framework for Probable Alzheimer's Disease and Mild Cognitive Impairment Based on Multi-Dimensional Emotion Features

BACKGROUND

Emotion and cognition are intercorrelated. Impaired emotion is common in populations with Alzheimer's disease (AD) and mild cognitive impairment (MCI), showing promises as an early detection approach.

OBJECTIVE

We aim to develop a novel automatic classification tool based on emotion features and machine learning.

METHODS

Older adults aged 60 years or over were recruited among residents in the long-term care facilities and the community. Participants included healthy control participants with normal cognition (HC, n = 26), patients with MCI (n = 23), and patients with probable AD (n = 30). Participants watched emotional film clips while multi-dimensional emotion data were collected, including mental features of Self-Assessment Manikin (SAM), physiological features of electrodermal activity (EDA), and facial expressions. Emotional features of EDA and facial expression were abstracted by using continuous decomposition analysis and EomNet, respectively. Bidirectional long short-term memory (Bi-LSTM) was used to train classification model. Hybrid fusion was used, including early feature fusion and late decision fusion. Data from 79 participants were utilized into deep machine learning analysis and hybrid fusion method.

RESULTS

By combining multiple emotion features, the model's performance of AUC value was highest in classification between HC and probable AD (AUC = 0.92), intermediate between MCI and probable AD (AUC = 0.88), and lowest between HC and MCI (AUC = 0.82).

CONCLUSIONS

Our method demonstrated an excellent predictive power to differentiate HC/MCI/AD by fusion of multiple emotion features. The proposed model provides a cost-effective and automated method that can assist in detecting probable AD and MCI from normal aging.

Mutual Information between EDA and EEG in Multiple Cognitive Tasks and Sleep Deprivation Conditions

Sleep deprivation, a widespread phenomenon that affects one-third of normal American adults, induces adverse changes in physical and cognitive performance, which in turn increases the occurrence of accidents. Sleep deprivation is known to increase resting blood pressure and decrease muscle sympathetic nerve activity. Monitoring changes in the interplay between the central and autonomic sympathetic nervous system can be a potential indicator of human's readiness to perform tasks that involve a certain level of cognitive load (e.g., driving). The electroencephalogram (EEG) is the standard to assess the brain's activity. The electrodermal activity (EDA) is a reflection of the general state of arousal regulated by the activation of the sympathetic nervous system through sweat gland stimulation. In this work, we calculated the mutual information between EDA and EEG recordings in order to consider linear and non-linear interactions and provide an insight of the relationship between brain activity and peripheral autonomic sympathetic activity. We analyzed EEG and EDA data from ten participants performing four cognitive tasks every two hours during 24 h (12 trials). We decomposed EEG data into delta, theta, alpha, beta, and gamma spectral components, and EDA into tonic and phasic components. The results demonstrate high values of mutual information between the EDA and delta component of EEG, mainly in working memory tasks. Additionally, we found an increase in the theta component of EEG in the presence of fatigue caused by sleep deprivation, the alpha component in tasks demanding inhibition and attention, and the delta component in working memory tasks. In terms of the location of brain activity, most of the tasks report high mutual information in frontal regions in the initial trials, with a trend to decrease and become uniform for all the nine analyzed EEG channels as a consequence of the sleep deprivation effect. Our results evidence the interplay between central and sympathetic nervous activity and can be used to mitigate the consequences of sleep deprivation.

Wearable Multisensor Ring-Shaped Probe for Assessing Stress and Blood Oxygenation: Design and Preliminary Measurements

The increasing interest in innovative solutions for health and physiological monitoring has recently fostered the development of smaller biomedical devices. These devices are capable of recording an increasingly large number of biosignals simultaneously, while maximizing the user's comfort. In this study, we have designed and realized a novel wearable multisensor ring-shaped probe that enables synchronous, real-time acquisition of photoplethysmographic (PPG) and galvanic skin response (GSR) signals. The device integrates both the PPG and GSR sensors onto a single probe that can be easily placed on the finger, thereby minimizing the device footprint and overall size. The system enables the extraction of various physiological indices, including heart rate (HR) and its variability, oxygen saturation (SpO₂), and GSR levels, as well as their dynamic changes over time, to facilitate the detection of different physiological states, e.g., rest and stress. After a preliminary SpO₂ calibration procedure, measurements have been carried out in laboratory on healthy subjects to demonstrate the feasibility of using our system to detect rapid changes in HR, skin conductance, and SpO₂ across various physiological conditions (i.e., rest, sudden stress-like situation and breath holding). The early findings encourage the use of the device in daily-life conditions for real-time monitoring of different physiological states.

Machine Learning Techniques for Arousal Classification from Electrodermal Activity: A Systematic Review

This article introduces a systematic review on arousal classification based on electrodermal activity (EDA) and machine learning (ML). From a first set of 284 articles searched for in six scientific databases, fifty-nine were finally selected according to various criteria established. The systematic review has made it possible to analyse all the steps to which the EDA signals are subjected: acquisition, pre-processing, processing and feature extraction. Finally, all ML techniques applied to the features of these signals for arousal classification have been studied. It has been found that support vector machines and artificial neural networks stand out within the supervised learning methods given their high-performance values. In contrast, it has been shown that unsupervised learning is not present in the detection of arousal through EDA. This systematic review concludes that the use of EDA for the detection of arousal is widely spread, with particularly good results in classification with the ML methods found.

Current trends and opportunities in the methodology of electrodermal activity measurement

Electrodermal activity (EDA) has been measured in the laboratory since the late 1800s. Although the influence of sudomotor nerve activity and the sympathetic nervous system on EDA is well established, the mechanisms underlying EDA signal generation are not completely understood. Owing to simplicity of instrumentation and modern electronics, these measurements have recently seen a transfer from the laboratory to wearable devices, sparking numerous novel applications while bringing along both challenges and new opportunities. In addition to developments in electronics and miniaturization, current trends in material technology and manufacturing have sparked innovations in electrode technologies, and trends in data science such as machine learning and sensor fusion are expanding the ways that measurement data can be processed and utilized. Although challenges remain for the quality of wearable EDA measurement, ongoing research and developments may shorten the quality gap between wearable EDA and standardized recordings in the laboratory. In this topical review, we provide an overview of the basics of EDA measurement, discuss the challenges and opportunities of wearable EDA, and review recent developments in instrumentation, material technology, signal processing, modeling and data science tools that may advance the field of EDA research and applications over the coming years.

Trends in Heart-Rate Variability Signal Analysis

Heart rate variability (HRV) is the rate of variability between each heartbeat with respect to time. It is used to analyse the Autonomic Nervous System (ANS), a control system used to modulate the body's unconscious action such as cardiac function, respiration, digestion, blood pressure, urination, and dilation/constriction of the pupil. This review article presents a summary and analysis of various research works that analyzed HRV associated with morbidity, pain, drowsiness, stress and exercise through signal processing and machine learning methods. The points of emphasis with regards to HRV research as well as the gaps associated with processes which can be improved to enhance the quality of the research have been discussed meticulously. Restricting the physiological signals to Electrocardiogram (ECG), Electrodermal activity (EDA), photoplethysmography (PPG), and respiration (RESP) analysis resulted in 25 articles which examined the cause and effect of increased/reduced HRV. Reduced HRV was generally associated with increased morbidity and stress. High HRV normally indicated good health, and in some instances, it could signify clinical events of interest such as drowsiness. Effective analysis of HRV during ambulatory and motion situations such as exercise, video gaming, and driving could have a significant impact toward improving social well-being. Detection of HRV in motion is far from perfect, situations involving exercise or driving reported accuracy as high as 85% and as low as 59%. HRV detection in motion can be improved further by harnessing the advancements in machine learning techniques.

The Concept of Advanced Multi-Sensor Monitoring of Human Stress

Many people live under stressful conditions which has an adverse effect on their health. Human stress, especially long-term one, can lead to a serious illness. Therefore, monitoring of human stress influence can be very useful. We can monitor stress in strictly controlled laboratory conditions, but it is time-consuming and does not capture reactions, on everyday stressors or in natural environment using wearable sensors, but with limited accuracy. Therefore, we began to analyze the current state of promising wearable stress-meters and the latest advances in the record of related physiological variables. Based on these results, we present the concept of an accurate, reliable and easier to use telemedicine device for long-term monitoring of people in a real life. In our concept, we ratify with two synchronized devices, one on the finger and the second on the chest. The results will be obtained from several physiological variables including electrodermal activity, heart rate and respiration, body temperature, blood pressure and others. All these variables will be measured using a coherent multi-sensors device. Our goal is to show possibilities and trends towards the production of new telemedicine equipment and thus, opening the door to a widespread application of human stress-meters.

Color-dependent changes in humans during a verbal fluency task under colored light exposure assessed by SPA-fNIRS

Light evokes robust visual and nonvisual physiological and psychological effects in humans, such as emotional and behavioral responses, as well as changes in cognitive brain activity and performance. The aim of this study was to investigate how colored light exposure (CLE) and a verbal fluency task (VFT) interact and affect cerebral hemodynamics, oxygenation, and systemic physiology as determined by systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS). 32 healthy adults (17 female, 15 male, age: 25.5 ± 4.3 years) were exposed to blue and red light for 9 min while performing a VFT. Before and after the CLE, subjects were in darkness. We found that this long-term CLE-VFT paradigm elicited distinct changes in the prefrontal cortex and in most systemic physiological parameters. The subjects' performance depended significantly on the type of VFT and the sex of the subject. Compared to red light, blue evoked stronger responses in cerebral hemodynamics and oxygenation in the visual cortex. Color-dependent changes were evident in the recovery phase of several systemic physiological parameters. This study showed that the CLE has effects that endure at least 15 min after cessation of the CLE. This underlines the importance of considering the persistent influence of colored light on brain function, cognition, and systemic physiology in everyday life.

Machine Learning Models for the Classification of Sleep Deprivation Induced Performance Impairment During a Psychomotor Vigilance Task Using Indices of Eye and Face Tracking

High risk professions, such as pilots, police officers, and TSA agents, require sustained vigilance over long periods of time and/or under conditions of little sleep. This can lead to performance impairment in occupational tasks. Predicting impaired states before performance decrement manifests is critical to prevent costly and damaging mistakes. We hypothesize that machine learning models developed to analyze indices of eye and face tracking technologies can accurately predict impaired states. To test this we trained 12 types of machine learning algorithms using five methods of feature selection with indices of eye and face tracking to predict the performance of individual subjects during a psychomotor vigilance task completed at 2-h intervals during a 25-h sleep deprivation protocol. Our results show that (1) indices of eye and face tracking are sensitive to physiological and behavioral changes concomitant with impairment; (2) methods of feature selection heavily influence classification performance of machine learning algorithms; and (3) machine learning models using indices of eye and face tracking can correctly predict whether an individual's performance is “normal” or “impaired” with an accuracy up to 81.6%. These methods can be used to develop machine learning based systems intended to prevent operational mishaps due to sleep deprivation by predicting operator impairment, using indices of eye and face tracking.

Emotion Recognition Using Electrodermal Activity Signals and Multiscale Deep Convolutional Neural Network

In this work, an attempt has been made to classify emotional states using electrodermal activity (EDA) signals and multiscale convolutional neural networks. For this, EDA signals are considered from a publicly available "A Dataset for Emotion Analysis using Physiological Signals" (DEAP) database. These signals are decomposed into multiple-scales using the coarse-grained method. The multiscale signals are applied to the Multiscale Convolutional Neural Network (MSCNN) to automatically learn robust features directly from the raw signals. Experiments are performed with the MSCNN approach to evaluate the hypothesis (i) improved classification with electrodermal activity signals, and (ii) multiscale learning captures robust complementary features at a different scale. Results show that the proposed approach is able to differentiate various emotional states. The proposed approach yields a classification accuracy of 69.33% and 71.43% for valence and arousal states, respectively. It is observed that the number of layers and the signal length are the determinants for the classifier performance. The performance of the proposed approach outperforms the single-layer convolutional neural network. The MSCNN approach provides end-to-end learning and classification of emotional states without additional signal processing. Thus, it appears that the proposed method could be a useful tool to assess the difference in emotional states for automated decision making.

Analysis of Risk Factors and Symptoms of Burnout Syndrome in Colombian School Teachers under Statutes 2277 and 1278 Using Machine Learning Interpretation

In 2002, the Colombian ministry of education released statute 1278, for teaching professionalization, superseding statute 2277 of 1977. Although statute 1278 was intended to increase the quality of the education service and teachers’ remuneration, there is evidence that the abundant evaluations and hindered promotion system introduced by statute 1278 resulted in an impairment of the quality of life of the teachers, and a higher incidence of burnout syndrome. We used two techniques for machine learning interpretability, SHapley Additive exPlanation summary plots and predictor importance, to interpret support vector machine and decision tree machine learning models, respectively, to better understand the differences on risk factors and symptoms of burnout syndrome in school teachers under statutes 2277 and 1278. We have surveyed 54 school teachers between August and October 2018, 17 under statute 2277, and 37 under statute 1278. Among the risk factors and symptoms of burnout syndrome considered in this study, we found that the satisfaction with earnt income was the most relevant risk factor, followed by the overtime work and the perceived severity of the sanctions on lower performance. The most relevant symptoms of burnout were fatigue at the end of the day, and frequent headaches. This methodology can be potentially used in other contexts and social groups, allowing institutional authorities and policy makers to allocate resources to specific issues affecting a particular group of workers.