Analysing user physiological responses for affective video summarisation

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.

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

PurposeThe first step toward developing an automated construction workers performance monitoring system is to initially establish a complete and competent activity recognition solution, which is still lacking. This study aims to propose a novel approach of using labor physiological data collected through wearable sensors as means of remote and automatic activity recognition.Design/methodology/approachA pilot study is conducted against three pre-fabrication stone construction workers throughout three full working shifts to test the ability of automatically recognizing the type of activities they perform in-site through their lively measured physiological signals (i.e. blood volume pulse, respiration rate, heart rate, galvanic skin response and skin temperature). The physiological data are broadcasted from wearable sensors to a tablet application developed for this particular purpose, and are therefore used to train and assess the performance of various machine-learning classifiers.FindingsA promising result of up to 88% accuracy level for activity recognition was achieved by using an artificial neural network classifier. Nonetheless, special care needs to be taken for some activities that evoke similar physiological patterns. It is expected that blending this method with other currently developed camera-based or kinetic-based methods would yield higher activity recognition accuracy levels.Originality/valueThe proposed method complements previously proposed labor tracking methods that focused on monitoring labor trajectories and postures, by using additional rich source of information from labors physiology, for real-time and remote activity recognition. Ultimately, this paves for an automated and comprehensive solution with which construction managers could monitor, control and collect rich real-time data about workers performance remotely.

Recent research has shown that design principles inherent in a Japanese style garden can reduce measures of stress in both healthy and dementia patients. However, it was not clear how subjects' visual interaction with the scene affected their physiological responses. To address that, we developed a novel non-invasive system to collect synchronized measurements of eye gaze and physiological indicators of sympathetic neural activity: the electrocardiogram, the blood volume pulse and the galvanic skin response, as subjects view a garden environment. We characterized the visual engagement of subjects using the average fixation duration, the saccade amplitude and the gaze transition entropy. We find a statistically significant positive correlation between gaze transition entropy and mean heart rate change. Our results suggest that the visual engagement of subjects with their environment may influence their physiological responses to it: more engagement may lead to more relaxation. Our results also highlight the importance of taking into account the detailed spatio-temporal characteristics of the gaze trajectory.

For several years, our research group has been systematically investigating how changes in cerebral hemodynamics as well as systemic physiology can be induced by colored light exposure (CLE). We have observed that these physiological changes can differ between subjects for exactly the same CLE stimulus. In the present case series, we report specific physiological reactivity patterns from four subjects. A total of 36 subjects participated in the study. These subjects were exposed to blue and red light while performing a 2-back task. For the present analysis, we selected four subjects as representative examples of distinct response patterns. We simultaneously measured changes in cerebral and systemic physiological parameters using the systemic physiology augmented functional near-infrared spectroscopy (SPA-fNIRS) method. The SPA-fNIRS method included oxygenated hemoglobin ([O2Hb]) and deoxygenated hemoglobin ([HHb]) in the prefrontal cortex, mean arterial pressure (MAP), end-tidal carbon dioxide (PETCO2), skin conductance level (SCL), and respiration rate (RR). We identified four distinct patterns of physiological responses. Pattern 1: For all parameters except MAP, the subject's response to blue and red light remained consistent. Pattern 2: Similar to the previous case, there was no significant contrast between red and blue with respect to changes in cerebral hemodynamics. Similar patterns were also detected for PETCO2 and MAP, whereas completely different patterns were found for SCL. Surprisingly, an inverse correlation was found for RR. Pattern 3: Changes in most parameters exhibited mirrored effects. Consistent trends were observed for SCL and RR. Pattern 4: Mirrored patterns were observed for [O2Hb] and [HHb], the same patterns for PETCO2 and RR, and divergent trends for MAP and SCL. In our analysis of SPA-fNIRS measurements, we identified four subjects with specific physiological reactivity patterns to a combined CLE and cognitive task. Individual physiological reactivity or physiological constitution should be taken into account when analyzing physiological data on CLE to enable a more adequate interpretation of the results.

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.

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.

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.

4 physiological measures--electromyogram, respiration rate, heart rate, and skin conductance--were recorded for 11 high and 11 low hypnotizable Ss. It was hypothesized (a) that physiological responsiveness during hypnosis would vary depending on the nature of the task instructions, and (b) that high hypnotizable Ss would show more physiological responsiveness than low hypnotizable Ss. The first hypothesis was substantiated across all 4 measures. Only heart rate levels supported the second hypothesis. The results are discussed as they relate to the 2 hypotheses and to future research.

Purpose: This study evaluated the feasibility of using wearable sensors to assess listening-related stress in varying listening difficulties. The results were compared to listening effort assessed using laboratory-grade physiological measures. We hypothesized that these wearable sensors could accurately measure listening-related stress and that the results would be in the same direction as those observed with laboratory-grade equipment. Method: A within-subject repeated-measures study was conducted on 16 young adults with typical hearing ability. Participants wore three commercially available sensors to record heart rate (HR), respiration rate (RR), and galvanic skin response (GSR). Participants' listening-related stress was assessed using self-report, wearable sensors, and laboratory-grade physiological measures while listening to sentences in noise. Listening conditions were manipulated in terms of signal-to-noise ratios (SNRs; −4, 0, +4, and +8 dB SNR), sentence predictability (high and low), and stress-inducing negative feedback (flash of light and monetary punishers). Results: On average, participants reported higher arousal and task load with decreasing SNR, for low predictability, and with negative feedback. Participants had significantly higher HR and RR in difficult listening conditions, when measured with wearable sensors. However, no significant differences were observed for skin conductance. There was a good agreement between the measures for HR but not for RR and GSR. Conclusions: Although wearable sensors have potential in reflecting changes in listening effort, the results were not consistent with laboratory-grade measures. Methodological choices in both measures and design appear to affect outcomes. Future studies should focus on crafting designs appropriate to wearable devices to reflect changes in listening-related stress.

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.

Autonomous sensory meridian response (ASMR) is a physiological phenomenon characterized by tingling sensations typically starting on the scalp and spreading down the neck and spine, accompanied by feelings of relaxation and calm. Across three studies, we explored preferences for specific ASMR triggers and then examined the physiological responses those triggers elicit in the lab. In Study 1, we obtained ASMR user ratings of trigger categories to determine which were most popular. In Study 2, participants watched ASMR video clips featuring a subset of these various trigger types and rated their affective responses. In Study 3, we brought participants into the lab to view ASMR videos designed to highlight the least polarizing and most generally pleasant triggers, based on the results of Studies 1 and 2 (personal attention, object manipulation, typing, softly spoken and repetitive movements), and measured physiological responses in heart rate and galvanic skin response. Our findings showed that all trigger videos elicited significant subjective relaxation and decreased heart rate, while galvanic skin responses varied across videos. This pattern suggests that although ASMR content generally promotes physiological relaxation, certain triggers may also engage attention or arousal in different ways. Future research should focus on understanding the variability in physiological responses to different ASMR triggers and investigate how these specific triggers might be optimized for relaxation.

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

Physiological responses are the immediate response shown by livestock against climatic stress and categorized as rectal temperature, respiration rate, heart rate, sweating rate, skin temperature along with digestion and absorption of nutrients. Rectal temperature is regarded as sensitive indicator of physiological status during climatic stress in domestic species. Respiration rate is considered as most consistent biomarker amongst the different physiological responses shown by livestock’s against climatic stress. An increase in respiration rate was noticed in cattle during heat stress. Variation in heart rate and pulse rate are appraised as indicator in livestock’s against climatic stress. Various environmental variables modulate sweating rate such as ambient temperature, relative humidity, wind velocity and solar radiation. Generally, the skin temperature was increases with increase in ambient temperature. The chapter describes the physiological and endocrine responses in which helps in acquisition of thermo-tolerance to livestock’s.