Observer’s Galvanic Skin Response for Discriminating Real from Fake Smiles

Abstract: This paper demonstrates a system to discriminate real from fake smiles with high accuracy by sensing observers’ galvanic skin response (GSR). GSR signals are recorded from 10 observers, while they are watching 5 real and 5 posed or acted smile video stimuli. We investigate the effect of various feature selection methods on processed GSR signals (recorded features) and computed features (extracted features) from the processed GSR signals, by measuring classification performance using three different classifiers. A leave-one-observer-out process is implemented to reliably measure classification accuracy. It is found that simple neural network (NN) using random subset feature selection (RSFS) based on extracted features outperforms all other cases, with 96.5% classification accuracy on our two classes of smiles (real vs. fake). The high accuracy highlights the potential of this system for use in the future for discriminating observers’ reactions to authentic emotional stimuli in information systems settings such as advertising and tutoring systems

Observer’s Galvanic Skin Response for Discriminating Real from Fake Smiles

This paper demonstrates a system to discriminate real from fake smiles with high accuracy by sensing observers’ galvanic skin response (GSR). GSR signals are recorded from 10 observers, while they are watching 5 real and 5 posed or acted smile video stimuli. We investigate the effect of various feature selection methods on processed GSR signals (recorded features) and computed features (extracted features) from the processed GSR signals, by measuring classification performance using three different classifiers. A leave-one-observer-out process is implemented to reliably measure classification accuracy. It is found that simple neural network (NN) using random subset feature selection (RSFS) based on extracted features outperforms all other cases, with 96.5% classification accuracy on our two classes of smiles (real vs. fake). The high accuracy highlights the potential of this system for use in the future for discriminating observers’ reactions to authentic emotional stimuli in settings such as advertising and tutoring systems

Proficiency test analysis of a simple electro-dermal activity measurement technique for measuring an emotional task

An electrodermal activity (EDA) measurement device has the ability to measure the electrical properties of the skin. The electrical properties of the skin change based on different kinds of stimuli that produce sweat generated by sweat glands. This study aimed to examine the sensitivity of a simple acquisition technique of EDA to detect conductance changes on the skin as a response to different stimuli that caused by emotional response state. A simple measurement technique and a fabricated instrument device for comparison were used when an emotional task was applied to the participants. Four participants were chosen, consisting of 2 men and 2 women between 20-25 years old. The EDA was measured while the participants watched a short scary movie using both devices. The result signals were analyzed using Convex Optimization Approach to Electrodermal Activity (cvxEDA) algorithm. The results revealed that several participants showed a state of psychological stress during the experiment using both devices, indicating the suitability of this simple device to detect changes of the EDA signal among the participants

Do You Need to Travel? Mapping Face-to-Face Communication Objectives to Technology Affordances

Computer-mediated communications (CMC) can be used as a substitute for face-to-face (FtF) meetings but their effectiveness is highly context dependent. This paper describes a theoretical framework and initial experimental design for characterizing a travel replacement threshold. This effort begins with a use case of remote engineering maintenance training, conducted in three conditions: side-by-side (physically proximate), teleconference (using off-the-shelf software), and a custom VR/AR system designed to provide the apprentice with a virtual view of both the instructor’s larger scale lab and smaller scale workbench. The research hypotheses, experimental protocol, and dependent measures are described. The task involves an instructor demonstrating a circuit board troubleshooting task to a remote apprentice. The apprentice then completes the trained task independently, and performance and subject preferences are compared across conditions. The details of this paper, the result of extensive literature review and winnowing of variables, may assist researchers exploring CMC, training, or social communication

Cognitive State Measurement from Eye Gaze Analysis in an Intelligent Virtual Reality Driving System for Autism Intervention

Abstract-Autism Spectrum Disorder (ASD) is a group of neurodevelopmental disabilities with a high prevalence rate. While much research has focused on improving social communication deficits in ASD populations, less emphasis has been devoted to improving skills relevant for adult independent living, such as driving. In this paper, a novel virtual reality (VR)-based driving system with different difficulty levels of tasks is presented to train and improve driving skills of teenagers with ASD. The goal of this paper is to measure the cognitive load experienced by an individual with ASD while he is driving in the VR-based driving system. Several eye gaze features are identified that varied with cognitive load in an experiment participated by 12 teenagers with ASD. Several machine learning methods were compared and the ability of these methods to accurately measure cognitive load was validated with respect to the subjective rating of a therapist. Results will be used to build models in an intelligent VR-based driving system that can sense a participant's real-time cognitive load and offer driving tasks at an appropriate difficulty level in order to maximize the participant's long-term performance

Combining physiological data and subjective measurements to investigate cognitive load during complex learning

Cognitive load theory is one of the most influential theoretical explanations of cognitive processing during learning. Despite its success, attempts to assess cognitive load during learning have proven difficult. Therefore, in the current study, students’ self-reported cognitive load after the problem- solving process has been combined with measures of physiological data, namely, electrodermal activity (EDA) and skin temperature (ST) during the problem-solving process. Data was collected from 15 students during a high and low complex task about learning and teaching geometry. This study first investigated the differences between subjective and physiological data during the problem- solving process of a high and low complex task. Additionally, correlations between subjective and physiological data were examined. Finally, learning behavior that is retrieved from log-data, was related with EDA. Results reveal that the manipulation of task complexity was not reflected by physiological data. Nevertheless, when investigating individual differences, EDA seems to be related to mental effort

Wearable Neurophysiological Recordings in Middle-School Classroom Correlate With Students’ Academic Performance

The rapid development of wearable bio-sensing techniques has made it possible to continuously record neurophysiological signals in naturalistic scenarios such as the classroom. The present study aims to explore the neurophysiological correlates of middle-school students’ academic performance. The electrodermal signals (EDAs) and heart rates (HRs) were collected via wristband from 100 Grade seven students during their daily Chinese and math classes for 10 days in 2 weeks. Significant correlations were found between the academic performance as reflected by the students’ final exam scores and the EDA responses. Further regression analyses revealed significant prediction of the academic performance mainly by the transient EDA responses (R2 = 0.083, p < 0.05, with Chinese classes only; R2 = 0.030, p < 0.05, with both Chinese and math classes included). By combining the self-report data about session-based general statuses and the neurophysiological data, the explained powers of the regression models were further improved (R2 = 0.095, p < 0.05, with Chinese classes only; R2 = 0.057, p < 0.05, with both Chinese and math classes included), and the neurophysiological data were shown to have independent contributions to the regression models. In addition, the regression models became non-significant by exchanging the academic performances of the Chinese and math classes as the dependent variables, suggesting at least partly distinct neurophysiological responses for the two types of classes. Our findings provide evidences supporting the feasibility of predicting educational outputs by wearable neurophysiological recordings

Unboxing the black box of visual expertise in medicine

Visual expertise in medicine has been a subject of research since many decades. Interestingly, it has been investigated from two little related fields, namely the field that focused mainly on the visual search aspects whilst ignoring higher-level cognitive processes involved in medical expertise, and the field that mainly focused on these higher-level cognitive processes largely ignoring the relevant visual aspects. Consequently, both research lines have traditionally used different methodologies. Recently, this gap is being increasingly closed and this special issue presents methods to investigate visual expertise in medicine from both research lines, namely those investigating vision (eye tracking, pupillometry, flash preview moving window paradigm), verbalisations, brain activity, and performance measures (ROC analysis, gesture coding, expert performance approach). We discuss the benefits and drawbacks of each method and suggest directions for future research that could help to unbox the black box of visual expertise in medicine.</p

Anticipating Explicit Motor Learning by Assessing Arousal Levels using HRV and GSR

Biometrics, including heart rate variability (HRV) and galvanic skin response (GSR), are already used to gauge autonomic regulation, emotional reactivity, attention, and flow, a concentration state. Given the role of arousal seen in motor learning factors such as optimal stress, anxiety, and task engagement, this study investigates whether HRV and GSR show distinguished patterns in those who explicitly learn a hidden sequence in a motor task as compared to those who only learn implicitly. This is done using a serial reaction time task (SRTT) and the collection of electrocardiogram (ECG) and GSR data throughout the task then comparing qualitative data across subjects. HRV decrease and GSR increase are noted at serval instances of explicit motor learning emergence, and even in instances when the shift is not exaggerated, it is never found varying in the opposite direction as the hypothesized pattern. Despite a low participant sample size and a low sampling frequency for ECG and GSR, the results tentatively support the concept of using HRV and GSR to gauge whether or not a person’s current state is conducive to explicit motor learning. This biometric monitoring holds the potential for real-time biofeedback and could be useful in physical rehabilitation settings due to the relative ease of implementation.Undergraduat