On the Interaction between Gaze Behavior and Physiological Responses when Viewing Garden Scenes.

Analysing user physiological responses for affective video summarisation

This paper explores a novel method for anxiety detection in older adults using simple wristband sensors such as electrodermal activity (EDA) and photoplethysmogram (PPG) and a context-based feature. The proposed method for anxiety detection combines features from a single physiological signal with an experimental context-based feature to improve the performance of the anxiety detection model. The experimental data for this work are obtained from a year-long experiment on 41 healthy older adults (26 females and 15 males) in the age range 60–80 with mean age \(73.36 \pm 5.25\) during a Trier Social Stress Test (TSST) protocol. The anxiety level ground truth was obtained from State–Trait Anxiety Inventory (STAI), which is regarded as the gold standard to measure perceived anxiety. EDA and blood volume pulse (BVP) signals were recorded using a wrist-worn EDA and PPG sensor, respectively. 47 features were computed from EDA and BVP signal, out of which a final set of 24 significantly correlated features were selected for analysis. The phases of the experimental study are encoded as unique integers to generate the context feature vector. A combination of features from a single sensor with the context feature vector is used for training a machine learning model to distinguish between anxious and not-anxious states. Results and analysis showed that the EDA and BVP machine learning models that combined the context feature along with the physiological features achieved 3.37% and 6.41% higher accuracy, respectively, than the models that used only physiological features. Further, end-to-end processing of EDA and BVP signals was simulated for real-time anxiety level detection. This work demonstrates the practicality of the proposed anxiety detection method in facilitating long-term monitoring of anxiety in older adults using low-cost consumer devices.

This study addresses the question whether galvanic skin response (GSR) and blood volume pulse (BVP) of untrained and unaided observers can be used to identify real and posed smiles from different sets of smile videos or smile images. Observers were shown smile face videos/images, either singly or paired, with the intention to recognise each viewed as real or posed smiles. We created four experimental situations, namely single images (SI), single videos (SV), paired images (PI), and paired videos (PV). The GSR and BVP signals were recorded and processed. Our machine learning classifiers reached the highest accuracy of 93.3%, 87.6%, 92.0%, 91.7% for PV, PI, SV, and SI respectively. Finally, PV and SI were found to be the easiest and hardest way to identify real and posed smiles respectively. Overall, we demonstrated that observers’ subconscious physiological signals (GSR and BVP) are able to identify real and posed smiles at a good accuracy.KeywordsPhysiological signalsAffective computingClassificationMachine learningSmile

Assessing visuospatial abilities related to visual scanning in cerebral visual impairment using the star cancellation test combined with wearable eye tracking.

This dataset provides demographic (age and sex), psychometric, and physiological data from 28 participants (20 males, 8 females) aged 19–27 years. Psychometric assessments include four questionnaires: the Emotion Regulation Questionnaire, Self-Assessment Manikin, and two perception-related scales (EmFacs and Uncanny Valley). Physiological signals recorded include electroencephalography, blood volume pulse, galvanic skin response, and skin temperature. Emotion induction was conducted using two virtual environments designed to elicit distinct emotional states: happiness (an outdoor natural forest) and anger (a subway car), with emotional stimuli delivered through speech by a digital human. Physiological responses were recorded at baseline and during immersion in the virtual environments. This dataset can support research on affective computing, human-computer interaction, and emotion recognition.

Rhythm perception and synchronisation is musical ability with neural basis defined as the ability to perceive rhythm in music and synchronise body movements with it. The study aimed to check the errors of synchronisation and physiological response as a reaction of the subjects to metrorhythmic stimuli of synchronous and pseudosynchronous stimulation (synchronisation with an externally controlled rhythm, but in reality controlled or produced tone by tapping) Nineteen subjects without diagnosed motor disorders participated in the study. Two tests were performed, where the electromyography signal and reaction time were recorded using the NORAXON system. In addition, physiological signals such as electrodermal activity and blood volume pulse were measured using the Empatica E4. Study 1 consisted of adapting the finger tapping test in pseudosynchrony with a given metrorhythmic stimulus with a selection of preferred, choices of decreasing and increasing tempo. Study 2 consisted of metrorhythmic synchronisation during the heel stomping test. Numerous correlations and statistically significant parameters were found between the response of the subjects with respect to their musical education, musical and sports activities. Most of the differentiating characteristics shown evidence of some group division in the undertaking of musical activities. The use of detailed analyses of synchronisation errors can contribute to the development of methods to improve the rehabilitation process of subjects with motor dysfunction, and this will contribute to the development of an expert system that considers personalised musical preferences.

BackgroundIn our Dynamic Pathways, account, we hypothesized that childhood stuttering reflects an impairment in speech sensorimotor control that is conditioned by cognitive, linguistic, and emotional factors. The purpose of this study was to investigate potential differences in levels of sympathetic arousal during performance of speech and non-speech tasks between children who do and do not stutter.MethodsSeventy-two preschool-aged children participated in the study, 47 children who stutter (CWS; 38 boys) and 25 children who do not stutter (CWNS; 18 boys). We recorded skin conductance and blood pulse volume (BPV) signals, indices of sympathetic arousal, during higher/lower load speech tasks (structured sentence production and picture description) and non-speech tasks (jaw wagging and forceful blowing). We included a measure that reflects children’s attitudes about their communication skills and a parent-report assessment of temperament.ResultsWe found no significant differences between preschool CWS and CWNS in phasic skin conductance response amplitude or frequency, BPV, and pulse rate for any of the experimental tasks. However, compared to CWNS, CWS had, on average, significantly higher skin conductance levels (SCL), indexing slowly changing tonic sympathetic activity, across both speech and non-speech experimental conditions. We found distinctive task-related profiles of sympathetic arousal in both groups of preschool children. Most children produced the highest levels of sympathetic arousal in the physically demanding blowing task rather than in speech, as seen in previous studies of adults. We did not find differences in temperament between the two groups of preschool children nor a relationship among behavioral indices of temperament and communication attitude and physiological measures of sympathetic arousal.ConclusionWe did not find that atypically high, speech-related sympathetic arousal is a significant factor in early childhood stuttering. Rather, CWS had higher, on average, task-related tonic SCLs across speech and non-speech tasks. A relationship among behavioral measures of temperament and physiological measures of sympathetic arousal was not confirmed. Key questions for future experiments are how the typical coupling of sympathetic and speech sensorimotor systems develops over childhood and adolescence and whether task related developmental profiles follow a different course in children who continue to stutter.

Chronic pain is prevalent and disproportionately impacts adults with a lower quality of life. Although subjective self-reporting is the "gold standard" for pain assessment, tools are needed to objectively monitor and account for inter-individual differences. This study introduced a novel framework to objectively classify pain intensity levels using physiological signals during Quantitative Sensory Testing sessions. Twenty-four participants participated in the study wearing physiological sensors (blood volume pulse (BVP), galvanic skin response (GSR), electromyography (EMG), respiration rate (RR), skin temperature (ST), and pupillometry). This study employed two analysis plans. Plan 1 utilized a grid search methodology with a 10-fold cross-validation framework to optimize time windows (1-5 s) and machine learning hyperparameters for pain classification tasks. The optimal time windows were identified as 3 s for the pressure session, 2 s for the pinprick session, and 1 s for the cuff session. Analysis Plan 2 implemented a leave-one-out design to evaluate the individual contribution of each sensor modality. By systematically excluding one sensor's features at a time, the performance of these sensor sets was compared to the full model using Wilcoxon signed-rank tests. BVP emerged as a critical sensor, significantly influencing performance in both pinprick and cuff sessions. Conversely, GSR, RR, and pupillometry demonstrated stimulus-specific sensitivity, significantly contributing to the cuff session but with limited influence in other sessions. EMG and ST showed minimal impact across all sessions, suggesting they are non-critical and suitable for reducing sensor redundancy. These findings advance the design of sensor configurations for personalized pain management. Future research will focus on refining sensor integration and addressing stimulus-specific physiological responses.

Reading Chinese, a kind of ideogram, relies more on visual cognition. The visuospatial cognitive deficit of Chinese dyslexia is an interesting topic that has received much attention. The purpose of current research was to explore the visuopatial cognitive characteristics of Chinese dyslexic children by studying their eye movements via a picture searching test. According to the diagnostic criteria defined by ICD-10, twenty-eight dyslexic children (mean age (10.12 +/- 1.42) years) were enrolled from the Clinic of Children Behavioral Disorder in the third affiliated hospital of Sun Yat-sen University. And 28 normally reading children (mean age (10.06 +/- 1.29) years), 1:1 matched by age, sex, grade and family condition were chosen from an elementary school in Guangzhou as a control group. Four groups of pictures (cock, accident, canyon, meditate) from Picture Vocabulary Test were chosen as eye movement experiment targets. All the subjects carried out the picture searching task and their eye movement data were recorded by an Eyelink II High-Speed Eye Tracker. The duration time, average fixation duration, average saccade amplitude, fixation counts and saccade counts were compared between the two groups of children. The dyslexic children had longer total fixation duration and average fixation duration (F = 7.711, P < 0.01; F = 4.520, P < 0.05), more fixation counts and saccade counts (F = 7.498, P < 0.01; F = 11.040, P < 0.01), and a smaller average saccade amplitude (F = 29.743, P < 0.01) compared with controls. But their performance in the picture vocabulary test was the same as those of the control group. The eye movement indexes were affected by the difficulty of the pictures and words, all eye movement indexes, except saccade amplitude, had a significant difference within groups (P < 0.05). Chinese dyslexic children have abnormal eye movements in picture searching, applying slow fixations, more fixations and small and frequent saccades. Their abnormal eye movement mode reflects the poor ability and strategy of visual information processing.

Differing associations of depersonalization with physiological response during rest and breath-focused mindfulness in a trauma-exposed female population

Fibromyalgia is a musculoskeletal disorder characterized by chronic, widespread muscle pain. This condition is associated with disturbed sleep, which has a direct impact on patient quality of life. Patient-reported outcomes are frequently used to assess sleep quality, but show modest correlations with objective measures of sleep, such as polysomnography. Working towards our goal of an automated ambulatory system of assessing sleep quality, we use features from blood volume pulse (BVP) and electrodermal activity (EDA) collected with a wearable device during sleep. We compare these measurements between individuals with fibromyalgia who experienced poor sleep and individuals in a control group who experienced refreshing sleep. By applying Learning Using Concave and Convex Kernels (LUCCK) and Support Vector Machines (SVM), we achieve mean Area Under the Receiver Operating Characteristic Curve (AUC) of 0.6573 and 0.6526, respectively, by using BVP data for classifying individuals to the two groups.

7th International Conference on Affective Computing and Intelligent Interaction, ACII 2017, San Antonio, United States, 23-26 October 2017

This study investigates the effects of a virtual biophilic residential environment on seniors’ physiological and subjective responses to evaluate its potential to promote healing and recovery. Thirty seniors were exposed to three different scales (units, buildings, complexes) of virtual biophilic residential environments that combined both physical and digital biophilic elements. Physiological responses, including heart rate, heart rate variability, and galvanic skin response, were measured alongside self-reported levels of satisfaction and immersion. The primary objective was to assess the effectiveness of physical and digital design interventions at each residential scale. The findings revealed that the virtual biophilic residential environment reduced physiological stress in seniors, with the most significant impact observed at the unit scale. Digital design interventions further enhance stress relief benefits, indicating that integrating physical and digital elements in biophilic residential environments can positively influence seniors’ stress levels. Additionally, significant correlations were identified between physiological responses and subjective perceptions of immersion and satisfaction. This study is valuable as an initial comparative analysis of the effectiveness of physical and digital approaches in biophilic design. This paper is a preliminary study and is significant in that it systematizes virtual environment research from an age-friendly perspective and expands approaches to biophilic design.

Biosensing technologies, which monitor physiological responses such as Heart Rate Variability (HRL), Skin Conductance Level (SCL), and Electroencephalogram (EEG) activity, offer a novel approach to enhancing the dissemination of health information in ideological and political education (IPE). In this context, health information encompasses topics such as mental health, stress management, and healthy lifestyle practices, all crucial to students’ overall well-being. Traditional health education methods cannot often capture real-time physiological and emotional responses, which can improve engagement and learning outcomes. This research explores the effectiveness of biosensing technology in enhancing the dissemination of health information within IPE. It examines how physiological data can be utilized to assess student engagement, emotional responses, and learning outcomes related to health. A mixed-methods approach was adopted, combining quantitative data from wearable biosensors (heart rate monitors, Galvanic Skin Response (GSR) sensors, EEG headsets) with qualitative feedback from students. Physiological data were preprocessed using signal filtering techniques, such as the Savitzky-Golay Filter, and features such as heart rate variability, skin conductance, and EEG alpha waves were extracted using the Kalman Filter (KF). A Modified Runge-Kutta Optimizer Integrated with Deep Belief Networks (MRKO-DBN) classifier was employed to predict student engagement based on these features. The research revealed that physiological responses, particularly heart rate variability and skin conductance, were strongly correlated with student engagement. The MRKO-DBN model achieved accuracy in predicting engagement. Qualitative feedback further confirmed that Biosensing technology significantly improved students’ engagement. Integrating Biosensing technology into health education within ideological and political contexts offers significant potential for enhancing student engagement and learning outcomes. By providing real-time, personalized feedback, it fosters a more interactive and responsive learning environment.

A hybrid transposed attention based deep learning model for wearable and explainable stress recognition