Comparing Two Emotion Models for Deriving Affective States from Physiological Data

2008 ◽  
pp. 35-50 ◽  
Author(s):  
Antje Lichtenstein ◽  
Astrid Oehme ◽  
Stefan Kupschick ◽  
Thomas Jürgensohn
Keyword(s):  
Physiological Data ◽  
Affective States ◽  
Emotion Models

BioCrystal

2015 ◽  
Vol 7 (3) ◽  
pp. 20-41 ◽  
Author(s):  
Asta Roseway ◽  
Yuliya Lutchyn ◽  
Paul Johns ◽  
Elizabeth Mynatt ◽  
Mary Czerwinski
Keyword(s):  
Interpersonal Communication ◽  
Future Development ◽  
Quantitative Data ◽  
Physiological Data ◽  
Self Awareness ◽  
Affective States ◽  
Practical Applications ◽  
High Utility ◽  
Biofeedback Device

In this paper the authors present the BioCrystal – a biofeedback device that uses physiological data to evaluate user's affective states in real-time and signals the states via an ambient display. The authors evaluated the BioCrystal during a 2-week, in situ multi-method study during which ten users collected over 115 hours of usable data. Users' comments suggested high utility of such a biofeedback device for self-awareness, stress-management and interpersonal communication. Quantitative data confirmed that the BioCrystal met the criteria of an ambient display, and significantly improved users' ability to control their stress. The authors discuss practical applications and suggest directions for future development.

scholarly journals Progression of Cognitive-Affective States During Learning in Kindergarteners: Bringing Together Physiological, Observational and Performance Data

2020 ◽  
Vol 10 (7) ◽  
pp. 177
Author(s):  
Priyashri Kamlesh Sridhar ◽  
Suranga Nanayakkara
Keyword(s):  
Data Sources ◽  
Physiological Data ◽  
Affective States ◽  
Multiple Sources ◽  
Multiple Data ◽  
Combining Data ◽  
And Performance ◽  
And Mathematics ◽  
Future Work ◽  
Physiological Markers

It has been shown that combining data from multiple sources, such as observations, self-reports, and performance with physiological markers offers better insights into cognitive-affective states during the learning process. Through a study with 12 kindergarteners, we explore the role of utilizing insights from multiple data sources, as a potential arsenal to supplement and complement existing assessments methods in understanding cognitive-affective states across two main pedagogical approaches—constructionist and instructionist—as children explored learning a chosen Science, Technology, Engineering and Mathematics (STEM) concept. We present the trends that emerged across pedagogies from different data sources and illustrate the potential value of additional data channels through case illustrations. We also offer several recommendations for such studies, particularly when collecting physiological data, and summarize key challenges that provide potential avenues for future work.

scholarly journals DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks

2019 ◽  
Author(s):  
Marco Maier ◽  
Daniel Elsner ◽  
Chadly Marouane ◽  
Meike Zehnle ◽  
Christoph Fuchs
Keyword(s):  
User Experience ◽  
Affective State ◽  
Professional Sports ◽  
Low Flow ◽  
Optimal Experience ◽  
Physiological Data ◽  
Flow State ◽  
Affective States ◽  
High Productivity ◽  
Human In The Loop

Flow is an affective state of optimal experience, total immersion and high productivity. While often associated with (professional) sports, it is a valuable information in several scenarios ranging from work environments to user experience evaluations, and we expect it to be a potential reward signal for human-in-the-loop reinforcement learning systems. Traditionally, flow has been assessed through questionnaires which prevents its use in online, real-time environments. In this work, we present our findings towards estimating a user's flow state based on physiological signals measured using wearable devices. We conducted a study with participants playing the game Tetris in varying difficulty levels, leading to boredom, stress, and flow. Using an end-to-end deep learning architecture, we achieve an accuracy of 67.50% in recognizing high flow vs. low flow states and 49.23% in distinguishing all three affective states boredom, flow, and stress.

scholarly journals Towards a Physiological Computing Infrastructure for Researching Students’ Flow in Remote Learning

2021 ◽  
Author(s):  
Maximilian Xiling Li ◽  
Mario Nadj ◽  
Alexander Maedche ◽  
Dirk Ifenthaler ◽  
Johannes Wöhler
Keyword(s):  
Supervised Machine Learning ◽  
Physiological Data ◽  
Raspberry Pi ◽  
Affective States ◽  
Real Time Measurement ◽  
Learning Journey ◽  
Browsing Behavior ◽  
Future Work ◽  
Computing Infrastructure ◽  
Remote Learning

AbstractWith the advent of physiological computing systems, new avenues are emerging for the field of learning analytics related to the potential integration of physiological data. To this end, we developed a physiological computing infrastructure to collect physiological data, surveys, and browsing behavior data to capture students’ learning journey in remote learning. Specifically, our solution is based on the Raspberry Pi minicomputer and Polar H10 chest belt. In this work-in-progress paper, we present preliminary results and experiences we collected from a field study with medical students using our developed infrastructure. Our results do not only provide a new direction for more effectively capturing different types of data in remote learning by addressing the underlying challenges of remote setups, but also serve as a foundation for future work on developing a less obtrusive, (near) real-time measurement method based on the classification of cognitive-affective states such as flow or other learning-relevant constructs with the captured data using supervised machine learning.

Affect Prediction for Emotional Design: A Comparison Study of Physiological and Subjective Self-Report Data

2011 ◽  
Author(s):  
Feng Zhou ◽  
Xingda Qu ◽  
Jianxin Roger Jiao ◽  
Martin G. Helander
Keyword(s):  
User Satisfaction ◽  
Value Added ◽  
Self Report ◽  
Physiological Data ◽  
Physiological Measures ◽  
Emotional Design ◽  
Affective States ◽  
Subjective Measures ◽  
Report Data ◽  
Self Report Data

Emotional design has attracted much attention due to its important role in the development of products and services towards high value-added user satisfaction and performance enhancement. However, how to predict users’ affective states in real time and without having to interrupt the user is critical to emotional design. This study compared affect prediction between using physiological measures and using self-report subjective measures. Specifically, an experiment was designed to elicit seven different affective states using standardized affective pictures as visual stimuli. Each stimulus was presented for 6 seconds and multiple physiological signals were measured, including facial electromyography, respiration rate, electroencephalography, and skin conductance response. Subjective ratings were also recorded immediately after stimulus presentation. Three data mining methods (i.e., decision rules, k-NN, and decomposition tree) based on the rough set theory were applied to construct prediction models from physiological measures and subjective measures, respectively. We obtained the highest mean prediction rate at 73.69% for physiological models and 52.43% for subjective models, respectively, across the 7 affective states. It demonstrates that physiological data are able to predict better result than subjective self-report data did and that physiological computing offers great potential for the development of emotional design.

scholarly journals Effects of Emotion on Pain Reports, Tolerance and Physiology

2002 ◽  
Vol 7 (1) ◽  
pp. 21-30 ◽  
Author(s):  
Leslie E Carter ◽  
Daniel W McNeil ◽  
Kevin E Vowles ◽  
John T Sorrell ◽  
Cynthia L Turk ◽  
...  
Keyword(s):  
Pain Tolerance ◽  
Physiological Data ◽  
Emotional States ◽  
Affective States ◽  
Verbal Reports ◽  
Emotion Induction ◽  
Before And After ◽  
Student Volunteers ◽  
Response Systems ◽  
Negative Affective States

The effects of specific emotional states on a laboratory pain task were tested by examining the behavioural, verbal and psychophysiological responses of 80 student volunteers (50% female). Participants were assigned to one of four Velten-style emotion-induction conditions (ie, anxiety, depression, elation or neutral). The sexes of experimenters were counterbalanced. Overt escape behaviour (ie, pain tolerance), pain threshold and severity ratings, verbal reports of emotion and physiological measures (ie, electrocardiogram, corrugator and trapezium electromyogram) were recorded. A pressure pain task was given before and after the emotion induction. As predicted, those who participated in the anxiety or depression condition showed reduced pain tolerance after induction of these negative emotions; pain severity ratings became most pronounced in the depression condition. Emotion induction did not have a discernable effect on pain tolerance or severity ratings in the elation condition. A pattern of participant and experimenter sex effects, as well as trials effects, was seen in the physiological data. The influence of negative affective states (ie, anxiety and depression) on acute pain are discussed along with the unique contributions of behavioural, verbal and physiological response systems in understanding the interactions of pain and emotions.

Group Experiences and Individual Differences in Stuttering

2019 ◽  
Vol 62 (12) ◽  
pp. 4335-4350 ◽  
Author(s):  
Seth E. Tichenor ◽  
J. Scott Yaruss
Keyword(s):  
Individual Differences ◽  
Support Groups ◽  
Support Group ◽  
Affective States ◽  
Life Outcomes ◽  
Self Help ◽  
Wide Range ◽  
Group Experiences ◽  
History Of ◽  
Adults Who Stutter

Purpose This study explored group experiences and individual differences in the behaviors, thoughts, and feelings perceived by adults who stutter. Respondents' goals when speaking and prior participation in self-help/support groups were used to predict individual differences in reported behaviors, thoughts, and feelings. Method In this study, 502 adults who stutter completed a survey examining their behaviors, thoughts, and feelings in and around moments of stuttering. Data were analyzed to determine distributions of group and individual experiences. Results Speakers reported experiencing a wide range of both overt behaviors (e.g., repetitions) and covert behaviors (e.g., remaining silent, choosing not to speak). Having the goal of not stuttering when speaking was significantly associated with more covert behaviors and more negative cognitive and affective states, whereas a history of self-help/support group participation was significantly associated with a decreased probability of these behaviors and states. Conclusion Data from this survey suggest that participating in self-help/support groups and having a goal of communicating freely (as opposed to trying not to stutter) are associated with less negative life outcomes due to stuttering. Results further indicate that the behaviors, thoughts, and experiences most commonly reported by speakers may not be those that are most readily observed by listeners.

A Tale of Two Colons and Two Cancers

Swiss Surgery ◽  
2003 ◽  
Vol 9 (1) ◽  
pp. 3-7 ◽  
Author(s):  
Gervaz ◽  
Bühler ◽  
Scheiwiller ◽  
Morel
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Adjuvant Chemotherapy ◽  
Splenic Flexure ◽  
Chromosomal Instability ◽  
Proximal Colon ◽  
Colorectal Carcinogenesis ◽  
Physiological Data ◽  
Central Hypothesis ◽  
Group Stratification

The central hypothesis explored in this paper is that colorectal cancer (CRC) is a heterogeneous disease. The initial clue to this heterogeneity was provided by genetic findings; however, embryological and physiological data had previously been gathered, showing that proximal (in relation to the splenic flexure) and distal parts of the colon represent distinct entities. Molecular biologists have identified two distinct pathways, microsatellite instability (MSI) and chromosomal instability (CIN), which are involved in CRC progression. In summary, there may be not one, but two colons and two types of colorectal carcinogenesis, with distinct clinical outcome. The implications for the clinicians are two-folds; 1) tumors originating from the proximal colon have a better prognosis due to a high percentage of MSI-positive lesions; and 2) location of the neoplasm in reference to the splenic flexure should be documented before group stratification in future trials of adjuvant chemotherapy in patients with stage II and III colon cancer.

Heart Rate Variability, Homeostasis, and Brain Function

2012 ◽  
Vol 26 (4) ◽  
pp. 178-203 ◽  
Author(s):  
Francesco Riganello ◽  
Sergio Garbarino ◽  
Walter G. Sannita
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Vegetative State ◽  
Communication Disorders ◽  
Affective States ◽  
Experimental Conditions ◽  
Cardiovascular Research ◽  
Sensory Stimuli ◽  
Additional Information ◽  
Brain Injured

Measures of heart rate variability (HRV) are major indices of the sympathovagal balance in cardiovascular research. These measures are thought to reflect complex patterns of brain activation as well and HRV is now emerging as a descriptor thought to provide information on the nervous system organization of homeostatic responses in accordance with the situational requirements. Current models of integration equate HRV to the affective states as parallel outputs of the central autonomic network, with HRV reflecting its organization of affective, physiological, “cognitive,” and behavioral elements into a homeostatic response. Clinical application is in the study of patients with psychiatric disorders, traumatic brain injury, impaired emotion-specific processing, personality, and communication disorders. HRV responses to highly emotional sensory inputs have been identified in subjects in vegetative state and in healthy or brain injured subjects processing complex sensory stimuli. In this respect, HRV measurements can provide additional information on the brain functional setup in the severely brain damaged and would provide researchers with a suitable approach in the absence of conscious behavior or whenever complex experimental conditions and data collection are impracticable, as it is the case, for example, in intensive care units.

An Alternative to Self-Reports of Trait and State Affect

2014 ◽  
Vol 30 (3) ◽  
pp. 231-237 ◽  
Author(s):  
Markus Quirin ◽  
Regina C. Bode
Keyword(s):  
Self Report ◽  
Factorial Validity ◽  
Artificial Language ◽  
Cognitive Representation ◽  
Affective States ◽  
Novel Variants ◽  
Self Reports ◽  
State Affect ◽  
Affective Experiences ◽  
Automatic Activation

Self-report measures for the assessment of trait or state affect are typically biased by social desirability or self-delusion. The present work provides an overview of research using a recently developed measure of automatic activation of cognitive representation of affective experiences, the Implicit Positive and Negative Affect Test (IPANAT). In the IPANAT, participants judge the extent to which nonsense words from an alleged artificial language express a number of affective states or traits. The test demonstrates appropriate factorial validity and reliabilities. We review findings that support criterion validity and, additionally, present novel variants of this procedure for the assessment of the discrete emotions such as happiness, anger, sadness, and fear.

Sign in / Sign up

Export Citation Format

Share Document