Information System for Monitoring the Emotional State of a Student With Special Needs Using AI

2020 ◽  
Author(s):  
Vasyl Andrunyk ◽  
Olesia Yaloveha
Keyword(s):  
Information System ◽  
Special Needs ◽  
Emotional State

Narrative and the Phases of Care

2017 ◽  
Author(s):  
Lisa Freitag
Keyword(s):  
Special Needs ◽  
Emotional State ◽  
Children's Rights ◽  
Children’S Rights ◽  
Political Scientist ◽  
Children With Special Needs ◽  
Moral Boundaries ◽  
Parental Autonomy

In her book Moral Boundaries political scientist and ethicist Joan Tronto suggested a new way of evaluating care, as an action rather than an emotional state. She approached care in four phases, each of which has an associated virtue guiding how a caregiver might act. This chapter evaluates the four phases of care and their virtues (attentiveness, responsibility, competence, and responsiveness), extending their use to the care of children with special needs. The applicability of Tronto’s phases and their virtues is demonstrated by applying them to a complex parent narrative: Vicky Forman’s This Lovely Life. Forman’s narrative, examined with principle ethics, offers an unsolvable dilemma of children’s rights vs. parental autonomy. Examination using Tronto’s theories offers a new way to analyze parent caregiving and raises ethical questions not previously considered. This provides the groundwork for evaluation of the moral work involved in extreme caregiving.

scholarly journals USE OF FACIAL EMOTION RECOGNITION IN E-LEARNING SYSTEMS

2017 ◽  
Vol 60 (4) ◽  
pp. 95 ◽  
Author(s):  
Uğur Ayvaz ◽  
Hüseyin Gürüler ◽  
Mehmet Osman Devrim
Keyword(s):  
Information System ◽  
Emotional State ◽  
Virtual Classroom ◽  
Learning Technologies ◽  
Learning Systems ◽  
Computer Usage ◽  
Formal Learning ◽  
Emotional States ◽  
Information Accessibility ◽  
E Learning

Since the personal computer usage and internet bandwidth are increasing, e-learning systems are also widely spreading. Although e-learning has some advantages in terms of information accessibility, time and place flexibility compared to the formal learning, it does not provide enough face-to-face interactivity between an educator and learners. In this study, we are proposing a hybrid information system, which is combining computer vision and machine learning technologies for visual and interactive e-learning systems. The proposed information system detects emotional states of the learners and gives feedback to an educator about their instant and weighted emotional states based on facial expressions. In this way, the educator will be aware of the general emotional state of the virtual classroom and the system will create a formal learning-like interactive environment. Herein, several classification algorithms were applied to learn instant emotional state and the best accuracy rates were obtained using kNN and SVM algorithms.

From Volunteering to Academic Programming: A Case Example

2016 ◽  
Vol 1 (17) ◽  
pp. 7-11 ◽  
Author(s):  
Cindy Gill ◽  
Sneha Bharadwaj ◽  
Nancy Quick ◽  
Sarah Wainscott ◽  
Paula Chance
Keyword(s):  
Special Needs ◽  
Cultural Relevance ◽  
Master's Program ◽  
Speech Language Pathology ◽  
Community Based ◽  
Charitable Organization ◽  
Language Pathology ◽  
The University

A speech-language pathology master's program that grew out of a partnership between the University of Zambia and a U.S.-based charitable organization, Connective Link Among Special needs Programs (CLASP) International, has just been completed in Zambia. The review of this program is outlined according to the suggested principles for community-based partnerships, a framework which may help evaluate cultural relevance and sustainability in long-term volunteer efforts (Israel, Schulz, Parker, & Becker, 1998).

Effect of Emotions in a Lexical Decision Task

2017 ◽  
Vol 76 (2) ◽  
pp. 71-79 ◽  
Author(s):  
Hélène Maire ◽  
Renaud Brochard ◽  
Jean-Luc Kop ◽  
Vivien Dioux ◽  
Daniel Zagar
Keyword(s):  
Lexical Decision ◽  
Lexical Decision Task ◽  
Emotional State ◽  
Control Group ◽  
Decision Task ◽  
Attentional Orienting ◽  
Emotional States ◽  
Sequence Effect ◽  
Emotion Induction ◽  
Before And After

Abstract. This study measured the effect of emotional states on lexical decision task performance and investigated which underlying components (physiological, attentional orienting, executive, lexical, and/or strategic) are affected. We did this by assessing participants’ performance on a lexical decision task, which they completed before and after an emotional state induction task. The sequence effect, usually produced when participants repeat a task, was significantly smaller in participants who had received one of the three emotion inductions (happiness, sadness, embarrassment) than in control group participants (neutral induction). Using the diffusion model ( Ratcliff, 1978 ) to resolve the data into meaningful parameters that correspond to specific psychological components, we found that emotion induction only modulated the parameter reflecting the physiological and/or attentional orienting components, whereas the executive, lexical, and strategic components were not altered. These results suggest that emotional states have an impact on the low-level mechanisms underlying mental chronometric tasks.

Associations Between Self-Report of Emotional State and the EEG Patterns in Affective Disorders Patients

2010 ◽  
Vol 24 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Miroslaw Wyczesany ◽  
Jan Kaiser ◽  
Anton M. L. Coenen
Keyword(s):  
State Energy ◽  
Emotional State ◽  
Spectral Power ◽  
Bipolar Affective Disorder ◽  
Affective State ◽  
Manic Episode ◽  
High Energy ◽  
Self Report ◽  
Major Depressive ◽  
High Tension

The study determines the associations between self-report of ongoing emotional state and EEG patterns. A group of 31 hospitalized patients were enrolled with three types of diagnosis: major depressive disorder, manic episode of bipolar affective disorder, and nonaffective patients. The Thayer ADACL checklist, which yields two subjective dimensions, was used for the assessment of affective state: Energy Tiredness (ET) and Tension Calmness (TC). Quantitative analysis of EEG was based on EEG spectral power and laterality coefficient (LC). Only the ET scale showed relationships with the laterality coefficient. The high-energy group showed right shift of activity in frontocentral and posterior areas visible in alpha and beta range, respectively. No effect of ET estimation on prefrontal asymmetry was observed. For the TC scale, an estimation of high tension was related to right prefrontal dominance and right posterior activation in beta1 band. Also, decrease of alpha2 power together with increase of beta2 power was observed over the entire scalp.

Feel Thine Own Self – Mood Congruency Evaluation of Emotional State Adjectives

2020 ◽  
pp. 1-17
Author(s):  
Szczepan J. Grzybowski ◽  
Miroslaw Wyczesany ◽  
Jan Kaiser
Keyword(s):  
Mood Induction ◽  
Semantic Analysis ◽  
Emotional State ◽  
Time Window ◽  
Event Related Potential ◽  
Negativity Bias ◽  
Positive Potential ◽  
Semantic Access ◽  
Current Mood ◽  
Component Time

Abstract. The goal of the study was to explore event-related potential (ERP) differences during the processing of emotional adjectives that were evaluated as congruent or incongruent with the current mood. We hypothesized that the first effects of congruence evaluation would be evidenced during the earliest stages of semantic analysis. Sixty mood adjectives were presented separately for 1,000 ms each during two sessions of mood induction. After each presentation, participants evaluated to what extent the word described their mood. The results pointed to incongruence marking of adjective’s meaning with current mood during early attention orientation and semantic access stages (the P150 component time window). This was followed by enhanced processing of congruent words at later stages. As a secondary goal the study also explored word valence effects and their relation to congruence evaluation. In this regard, no significant effects were observed on the ERPs; however, a negativity bias (enhanced responses to negative adjectives) was noted on the behavioral data (RTs), which could correspond to the small differences traced on the late positive potential.

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