scholarly journals Datasets for Cognitive Load Inference Using Wearable Sensors and Psychological Traits

2020 ◽  
Vol 10 (11) ◽  
pp. 3843 ◽  
Author(s):  
Martin Gjoreski ◽  
Tine Kolenik ◽  
Timotej Knez ◽  
Mitja Luštrek ◽  
Matjaž Gams ◽  
...  
Keyword(s):  
Neural Network ◽  
Cognitive Load ◽  
Personality Traits ◽  
Task Difficulty ◽  
Affective Computing ◽  
Wearable Sensors ◽  
Psychological Traits ◽  
Skin Response ◽  
Nasa Tlx ◽  
Psychological Tasks

This study introduces two datasets for multimodal research on cognitive load inference and personality traits. Different to other datasets in Affective Computing, which disregard participants’ personality traits or focus only on emotions, stress, or cognitive load from one specific task, the participants in our experiments performed seven different tasks in total. In the first dataset, 23 participants played a varying difficulty (easy, medium, and hard) game on a smartphone. In the second dataset, 23 participants performed six psychological tasks on a PC, again with varying difficulty. In both experiments, the participants filled personality trait questionnaires and marked their perceived cognitive load using NASA-TLX after each task. Additionally, the participants’ physiological response was recorded using a wrist device measuring heart rate, beat-to-beat intervals, galvanic skin response, skin temperature, and three-axis acceleration. The datasets allow multimodal study of physiological responses of individuals in relation to their personality and cognitive load. Various analyses of relationships between personality traits, subjective cognitive load (i.e., NASA-TLX), and objective cognitive load (i.e., task difficulty) are presented. Additionally, baseline machine learning models for recognizing task difficulty are presented, including a multitask learning (MTL) neural network that outperforms single-task neural network by simultaneously learning from the two datasets. The datasets are publicly available to advance the field of cognitive load inference using commercially available devices.

scholarly journals Tracking eye-gaze in smart home systems (SHS): first insights from eye-tracking and self-report measures

2021 ◽  
Author(s):  
Federico Cassioli ◽  
Laura Angioletti ◽  
Michela Balconi
Keyword(s):  
Eye Tracking ◽  
Personality Traits ◽  
Smart Home ◽  
Near Infrared ◽  
Tracking System ◽  
Eye Gaze ◽  
Self Report ◽  
Visual Exploration ◽  
Living Room ◽  
Psychological Traits

AbstractHuman–computer interaction (HCI) is particularly interesting because full-immersive technology may be approached differently by users, depending on the complexity of the interaction, users’ personality traits, and their motivational systems inclination. Therefore, this study investigated the relationship between psychological factors and attention towards specific tech-interactions in a smart home system (SHS). The relation between personal psychological traits and eye-tracking metrics is investigated through self-report measures [locus of control (LoC), user experience (UX), behavioral inhibition system (BIS) and behavioral activation system (BAS)] and a wearable and wireless near-infrared illumination based eye-tracking system applied to an Italian sample (n = 19). Participants were asked to activate and interact with five different tech-interaction areas with different levels of complexity (entrance, kitchen, living room, bathroom, and bedroom) in a smart home system (SHS), while their eye-gaze behavior was recorded. Data showed significant differences between a simpler interaction (entrance) and a more complex one (living room), in terms of number of fixation. Moreover, slower time to first fixation in a multifaceted interaction (bathroom), compared to simpler ones (kitchen and living room) was found. Additionally, in two interaction conditions (living room and bathroom), negative correlations were found between external LoC and fixation count, and between BAS reward responsiveness scores and fixation duration. Findings led to the identification of a two-way process, where both the complexity of the tech-interaction and subjects’ personality traits are important impacting factors on the user’s visual exploration behavior. This research contributes to understand the user responsiveness adding first insights that may help to create more human-centered technology.

scholarly journals Using Wearable Sensors and a Convolutional Neural Network for Catch Detection in American Football

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6722
Author(s):  
Bernhard Hollaus ◽  
Sebastian Stabinger ◽  
Andreas Mehrle ◽  
Christian Raschner
Keyword(s):  
Neural Network ◽  
Degrees Of Freedom ◽  
Wearable Sensors ◽  
Professional Sports ◽  
American Football ◽  
Data Logging ◽  
Sensor Platform ◽  
Magnetometer Data ◽  
Audio Data ◽  
New Type

Highly efficient training is a must in professional sports. Presently, this means doing exercises in high number and quality with some sort of data logging. In American football many things are logged, but there is no wearable sensor that logs a catch or a drop. Therefore, the goal of this paper was to develop and verify a sensor that is able to do exactly that. In a first step a sensor platform was used to gather nine degrees of freedom motion and audio data of both hands in 759 attempts to catch a pass. After preprocessing, the gathered data was used to train a neural network to classify all attempts, resulting in a classification accuracy of 93%. Additionally, the significance of each sensor signal was analysed. It turned out that the network relies most on acceleration and magnetometer data, neglecting most of the audio and gyroscope data. Besides the results, the paper introduces a new type of dataset and the possibility of autonomous training in American football to the research community.

scholarly journals Cognitive load reduction strategies in questionnaire design

2021 ◽  
pp. 147078532098679
Author(s):  
Kylie Brosnan ◽  
Bettina Grün ◽  
Sara Dolnicar
Keyword(s):  
Cognitive Load ◽  
Data Quality ◽  
Survey Data ◽  
Task Difficulty ◽  
Questionnaire Design ◽  
Load Reduction ◽  
The Past ◽  
Load Theory ◽  
Learning Tasks ◽  
Reduction Strategies

Survey data quality suffers when respondents have difficulty completing complex tasks in questionnaires. Cognitive load theory informed the development of strategies for educators to reduce the cognitive load of learning tasks. We investigate whether these cognitive load reduction strategies can be used in questionnaire design to reduce task difficulty and, in so doing, improve survey data quality. We find that this is not the case and conclude that some of the traditional survey answer formats, such as grid questions, which have been criticized in the past lead to equally good data and do not frustrate respondents more than alternative formats.

scholarly journals Educational Attainment and Personality Are Genetically Intertwined

2017 ◽  
Vol 28 (11) ◽  
pp. 1631-1639 ◽  
Author(s):  
René Mõttus ◽  
Anu Realo ◽  
Uku Vainik ◽  
Jüri Allik ◽  
Tõnu Esko
Keyword(s):  
Educational Attainment ◽  
Personality Traits ◽  
Personality Trait ◽  
Factor Model ◽  
Five Factor Model ◽  
Molecular Genetic ◽  
Maximum Amount ◽  
Biological Processes ◽  
Psychological Traits ◽  
Polygenic Scores

Heritable variance in psychological traits may reflect genetic and biological processes that are not necessarily specific to these particular traits but pertain to a broader range of phenotypes. We tested the possibility that the personality domains of the five-factor model and their 30 facets, as rated by people themselves and their knowledgeable informants, reflect polygenic influences that have been previously associated with educational attainment. In a sample of more than 3,000 adult Estonians, education polygenic scores (EPSs), which are interpretable as estimates of molecular-genetic propensity for education, were correlated with various personality traits, particularly from the neuroticism and openness domains. The correlations of personality traits with phenotypic educational attainment closely mirrored their correlations with EPS. Moreover, EPS predicted an aggregate personality trait tailored to capture the maximum amount of variance in educational attainment almost as strongly as it predicted the attainment itself. We discuss possible interpretations and implications of these findings.

The effects of cognitive load on query reformulation: mental demand, temporal demand and frustration

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kyoungsik Na
Keyword(s):  
Cognitive Load ◽  
User Interfaces ◽  
Information Search ◽  
Information Seeking ◽  
Empirical Support ◽  
Query Reformulation ◽  
Content Type ◽  
Mental Demand ◽  
Significant Difference ◽  
Nasa Tlx

PurposeThis study explores the effects of cognitive load on the propensity to reformulate queries during information seeking on the web.Design/methodology/approachThis study employs an experimental design to analyze the effect of manipulations of cognitive load on the propensity for query reformulation between experimental and control groups. In total, three affective components that contribute to cognitive load were manipulated: mental demand, temporal demand and frustration.FindingsA significant difference in the propensity of query reformulation behavior was found between searchers exposed to cognitive load manipulations and searchers who were not exposed. Those exposed to cognitive load manipulations made half as many search query reformulations as searchers not exposed. Furthermore, the National Aeronautical and Space Administration Task Load Index (NASA-TLX) cognitive load scores of searchers who were exposed to the three cognitive load manipulations were higher than those of searchers who were not exposed indicating that the manipulation was effective. Query reformulation behavior did not differ across task types.Originality/valueThe findings suggest that a dual-task method and NASA-TLX assessment serve as good indicators of cognitive load. Because the findings show that cognitive load hinders a searcher's interaction with information search tools, this study provides empirical support for reducing cognitive load when designing information systems or user interfaces.

scholarly journals A Novel Neural Network Model for Blood Pressure Estimation Using Photoplethesmography without Electrocardiogram

2018 ◽  
Vol 2018 ◽  
pp. 1-9 ◽  
Author(s):  
Ludi Wang ◽  
Wei Zhou ◽  
Ying Xing ◽  
Xiaoguang Zhou
Keyword(s):  
Neural Network ◽  
Blood Pressure ◽  
Mean Absolute Error ◽  
Wearable Sensors ◽  
Absolute Error ◽  
Noninvasive Estimation ◽  
Treatment Of Hypertension ◽  
The Mean ◽  
Artificial Neural Network Ann ◽  
Blood Pressure Estimation

The prevention, evaluation, and treatment of hypertension have attracted increasing attention in recent years. As photoplethysmography (PPG) technology has been widely applied to wearable sensors, the noninvasive estimation of blood pressure (BP) using the PPG method has received considerable interest. In this paper, a method for estimating systolic and diastolic BP based only on a PPG signal is developed. The multitaper method (MTM) is used for feature extraction, and an artificial neural network (ANN) is used for estimation. Compared with previous approaches, the proposed method obtains better accuracy; the mean absolute error is 4.02 ± 2.79 mmHg for systolic BP and 2.27 ± 1.82 mmHg for diastolic BP.

scholarly journals Affective computing in virtual reality: emotion recognition from brain and heartbeat dynamics using wearable sensors

2018 ◽  
Vol 8 (1) ◽  
Author(s):  
Javier Marín-Morales ◽  
Juan Luis Higuera-Trujillo ◽  
Alberto Greco ◽  
Jaime Guixeres ◽  
Carmen Llinares ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Emotion Recognition ◽  
Affective Computing ◽  
Wearable Sensors
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