A knowledge discovery and visualisation method for unearthing emotional states from physiological data

2020 ◽  
Author(s):  
Nectarios Costadopoulos ◽  
Md Zahidul Islam ◽  
David Tien
Keyword(s):  
Knowledge Discovery ◽  
Physiological Data ◽  
Emotional States ◽  
Visualisation Method

scholarly journals Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States

2018 ◽  
Vol 15 (12) ◽  
pp. 2907 ◽  
Author(s):  
Sunil Kumar ◽  
Ilyoung Chong
Keyword(s):  
Mental Health ◽  
Machine Learning ◽  
Correlation Analysis ◽  
Depressive Disorder ◽  
Weather Conditions ◽  
Physiological Data ◽  
Emotional States ◽  
Emotion Classification ◽  
Target Class ◽  
Bipolar Patients

Correlation analysis is an extensively used technique that identifies interesting relationships in data. These relationships help us realize the relevance of attributes with respect to the target class to be predicted. This study has exploited correlation analysis and machine learning-based approaches to identify relevant attributes in the dataset which have a significant impact on classifying a patient’s mental health status. For mental health situations, correlation analysis has been performed in Weka, which involves a dataset of depressive disorder symptoms and situations based on weather conditions, as well as emotion classification based on physiological sensor readings. Pearson’s product moment correlation and other different classification algorithms have been utilized for this analysis. The results show interesting correlations in weather attributes for bipolar patients, as well as in features extracted from physiological data for emotional states.

scholarly journals Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7854
Author(s):  
Luz Santamaria-Granados ◽  
Juan Francisco Mendoza-Moreno ◽  
Angela Chantre-Astaiza ◽  
Mario Munoz-Organero ◽  
Gustavo Ramirez-Gonzalez
Keyword(s):  
Emotion Recognition ◽  
Recommender System ◽  
Mobile Applications ◽  
Short Term Memory ◽  
Physiological Data ◽  
Emotional States ◽  
Final Phase ◽  
Short Term ◽  
Life Context ◽  
Long Short Term Memory

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

Classification of Emotional States in a Woz Scenario Exploiting Labeled and Unlabeled Bio-physiological Data

2012 ◽  
pp. 138-147 ◽  
Author(s):  
Martin Schels ◽  
Markus Kächele ◽  
David Hrabal ◽  
Steffen Walter ◽  
Harald C. Traue ◽  
...  
Keyword(s):  
Physiological Data ◽  
Emotional States

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.

scholarly journals Multimodal Approach for Emotion Recognition Using a Formal Computational Model

2013 ◽  
Vol 4 (3) ◽  
pp. 11-25 ◽  
Author(s):  
Imen Tayari Meftah ◽  
Nhan Le Thanh ◽  
Chokri Ben Amar
Keyword(s):  
Emotion Recognition ◽  
Physiological Data ◽  
Emotional States ◽  
Multimodal Approach ◽  
Sources Of Information ◽  
Recognition Method ◽  
Signal Processing Algorithms ◽  
Multimodal Emotion Recognition ◽  
Many Sources ◽  
Affective Information

Emotions play a crucial role in human-computer interaction. They are generally expressed and perceived through multiple modalities such as speech, facial expressions, physiological signals. Indeed, the complexity of emotions makes the acquisition very difficult and makes unimodal systems (i.e., the observation of only one source of emotion) unreliable and often unfeasible in applications of high complexity. Moreover the lack of a standard in human emotions modeling hinders the sharing of affective information between applications. In this paper, the authors present a multimodal approach for the emotion recognition from many sources of information. This paper aims to provide a multi-modal system for emotion recognition and exchange that will facilitate inter-systems exchanges and improve the credibility of emotional interaction between users and computers. The authors elaborate a multimodal emotion recognition method from Physiological Data based on signal processing algorithms. The authors’ method permits to recognize emotion composed of several aspects like simulated and masked emotions. This method uses a new multidimensional model to represent emotional states based on an algebraic representation. The experimental results show that the proposed multimodal emotion recognition method improves the recognition rates in comparison to the unimodal approach. Compared to the state of art multimodal techniques, the proposed method gives a good results with 72% of correct.

Recent Developments of Ambulatory Assessment Methods

2014 ◽  
Vol 25 (4) ◽  
pp. 279-287 ◽  
Author(s):  
Stefan Hey ◽  
Panagiota Anastasopoulou ◽  
André Bideaux ◽  
Wilhelm Stork
Keyword(s):  
Real Time ◽  
Information And Communication Technologies ◽  
New Technologies ◽  
Psychological Research ◽  
Ambulatory Assessment ◽  
Emotional States ◽  
Recent Developments ◽  
Potential Applications ◽  
Information And Communication ◽  
Physiological Sensors

Ambulatory assessment of emotional states as well as psychophysiological, cognitive and behavioral reactions constitutes an approach, which is increasingly being used in psychological research. Due to new developments in the field of information and communication technologies and an improved application of mobile physiological sensors, various new systems have been introduced. Methods of experience sampling allow to assess dynamic changes of subjective evaluations in real time and new sensor technologies permit a measurement of physiological responses. In addition, new technologies facilitate the interactive assessment of subjective, physiological, and behavioral data in real-time. Here, we describe these recent developments from the perspective of engineering science and discuss potential applications in the field of neuropsychology.

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.

Neuropsychology in the Real World

2014 ◽  
Vol 25 (4) ◽  
pp. 233-238 ◽  
Author(s):  
Martin Peper ◽  
Simone N. Loeffler
Keyword(s):  
Neuropsychological Assessment ◽  
Real World ◽  
Ecological Validity ◽  
Real Life ◽  
Emotional States ◽  
Context Sensitive ◽  
Traditional Assessment ◽  
Life Data ◽  
Real Life Data ◽  
Assessment And Treatment

Current ambulatory technologies are highly relevant for neuropsychological assessment and treatment as they provide a gateway to real life data. Ambulatory assessment of cognitive complaints, skills and emotional states in natural contexts provides information that has a greater ecological validity than traditional assessment approaches. This issue presents an overview of current technological and methodological innovations, opportunities, problems and limitations of these methods designed for the context-sensitive measurement of cognitive, emotional and behavioral function. The usefulness of selected ambulatory approaches is demonstrated and their relevance for an ecologically valid neuropsychology is highlighted.

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.

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