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Electronics and Telecommunications Research Institute, Korea, Republic of

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24 Papers

Internal Consistency of Physiological Responses during Exposure to Emotional Stimuli using Biosensors

Topic: Detection and Estimation

Authors: Eun-Hye Jang ¹ , Ah-Young Kim ¹ , Sang-Hyeob Kim ¹ , Han-Young Yu ¹ and Jin-Hun Sohn ²

Affiliations: ¹ Electronics and Telecommunications Research Institute, Korea, Republic of , ² Chungnam National University, Korea, Republic of

Digital Object Identifier: 10.5220/0005998701100115

Proceedings of SPCS 2016/PECCS, ISBN: 978-989-758-195-3

Quick Abstract

In biomedical engineering application, mental/physical health monitoring using biosensors has been lately noticed because bio-signal acquisition by non-invasive sensors is relatively simple as well as bio-signal is less sensitive to social/cultural difference. In particular, although it is known that they are significantly correlated with human emotional state, whether the signals by various emotions are stable remains unknown. In this study, we examined the consistency of physiological responses induced by six basic emotions, happiness, sadness, anger, fear, disgust and surprise using an experiment that was repeated 10 times. Twelve college subjects participated in this experiment. For emotion induction, sixty different emotional stimuli were selected in a pilot experiment. Heart Rate (HR), Skin Conductance Level (SCL), mean of Skin Temperature (meanSKT), and mean of Photoplethysmograph (meanPPG) were measured before the presentation of stimuli as a baseline and during the presentation of the stimuli as emotional state. The results showed that physiological responses during emotional states for the 10 times the experiment was repeated were stable and consistent compared to the baseline. In particular, we could identify that physiological features such as SCL, HR, and PPG are very reliable. Our results suggest that bio-signals by six emotions are consistent over time regardless of various stimuli. This means that physiological responses are reliable and biosensors are useful tool for emotion recognition.

Related Ontology Subjects/Areas/Topics (3)

Detection and Estimation

Digital Signal Processing

Telecommunications

Relationship between Depression Level and Bio-signals by Emotional Stimuli

Topic: Biosignal Acquisition, Analysis and Processing

Authors: Eun-Hye Jang , Ah Young Kim , Sang-Hyeob Kim and Han-Young Yu

Affiliation: Electronics and Telecommunications Research Institute, Korea, Republic of

Digital Object Identifier: 10.5220/0006005301380141

Proceedings of PhyCS 2016, ISBN: 978-989-758-197-7

Quick Abstract

Recent studies in mental/physical health monitoring have noted to improve health and wellbeing with the help of Information and Communication Technology (ICT) and in particular, application of biosensors has mainly done because signal acquisition by non-invasive sensors is relatively simple as well as bio-signal is less sensitive to social/cultural difference. Prior to developing a depression monitoring system based on non-invasive bio-signals, we examined a relationship of depressive level and changes of biological features during exposure of emotional stimuli. Ninety-six subjects’ depressive level was measured by a self-rating depression scale (SDS). Electrocardiogram (ECG) and photoplethysmograph (PPG) were recorded during six baseline and emotional states (interest, joy, neutral, pain, sadness and surprise) and heart rate (HR) and pulse transit time (PTT) were extracted. Pearson’s correlation was conducted to examine the relation of depressive level and biological features. The results showed that relation of depressive level and HR is positive in emotional states and there is a negative correlation between depressive level and PTT. We identified that they are meaningful biological features related to depression.

Related Ontology Subjects/Areas/Topics (4)

Biosignal Acquisition, Analysis and Processing		Methodologies and Methods		Physiological Computing Systems
Human-Computer Interaction

A Study on Autonomic Nervous System Responses and Feauture Selection for Emotion Recognition - Emotion Recognition using Machine Learning Algorithms

Topic: Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

Authors: Byoung-Jun Park ¹ , Eun-Hye Jang ¹ , Sang-Hyeob Kim ¹ , Myung-Ae Chung ¹ and Jin-Hun Sohn ²

Affiliations: ¹ Electronics and Telecommunications Research Institute, Korea, Republic of , ² Chungnam National University, Korea, Republic of

Digital Object Identifier: 10.5220/0004731201160121

Proceedings of BIOSIGNALS 2014/BIOSTEC, ISBN: 978-989-758-011-6

Quick Abstract

This study is related with emotion recognition based on autonomic nervous system responses. Three different emotional states, fear, surprise and stress, are evoked by stimuli and the autonomic nervous system responses for the induced emotions are measured as physiological signals such as skin temperature, electrodermal activity, electrocardiogram, and photoplethysmography. Twenty-eight features are analysed and extracted from these signals. The results of one-way ANOVA toward each parameter, there are significant differences among three emotions in some features. Therefore we select eight features from 28 features for emotion recognition. The comparative results of emotion recognition are discussed in view point of feature space with the selected features. For emotion recognition, we use four machine learning algorithms, namely, linear discriminant analysis, classification and regression tree, self-organizing map and naïve bayes, and those are evaluated by only training, 10-fold cross-validation and repeated random sub-sampling validation. This can be helpful to provide the basis for the emotion recognition technique in human computer interaction as well as contribute to the standardization in emotion-specific ANS responses.

Related Ontology Subjects/Areas/Topics (3)

Biomedical Engineering

Biomedical Signal Processing

Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

Correlation between Psychological and Physiological Responses during Fear - Relationship to Perceived Intensity of Fear

Topic: Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

Authors: Eun-Hye Jang ¹ , Byoung-Jun Park ¹ , Sang-Hyeob Kim ¹ , Myung-Ae Chung ¹ and Jin-Hun Sohn ²

Affiliations: ¹ Electronics and Telecommunications Research Institute, Korea, Republic of , ² Chungnam National University, Korea, Republic of

Digital Object Identifier: 10.5220/0004728101040109

Proceedings of BIOSIGNALS 2014/BIOSTEC, ISBN: 978-989-758-011-6

Quick Abstract

The purpose of this study is to examine the physiological responses to predict the psychological level of perceived fear. Thirty male and female college students (15 male and 15 female, mean age: 22.6±1.24) participated in the experiment. EDA (electrodermal activity), ECG (electrocardiogram), and facial EMG (electromyogram) as physiological signals were measured on the subjects’ hands and face for 60sec before presentation of emotional stimulus and for 120sec during presentation of stimulus. Experimental conditions consisted of emotional condition where fear was induced by a threatening film clip and neutral condition where no emotion was provoked by a neutral film clip. After presentation of the stimulus, subjects rated their experienced emotion on the emotion assessment scale. Analysis of psychological responses was performed to examine appropriateness (label of the subjects’ experienced emotion) and effectiveness (intensity of their experienced emotion). In the analysis of physiological responses, the selected features were skin conductance level (SCL), skin conductance response (SCR), number of skin conductance response (NSCR), R-R interval (R-R), heart rate (HR), respiration (RESP), activation in the bilateral corrugators (COR), and bilateral orbicularis oris’ (ORB). The results showed that the psychological responses to stimulus were appropriate and effective. Physiological responses showed significant increases in all features except R-R and ORB during fear condition compared to baseline condition. Also, the perceived level of fear was positively correlated with SCL, SCR, and ORB. Our result offer that the users’ perceived emotion i.e., individual differences of psychological responses must be considered to recognize human emotions by physiological signals in HCI.

Related Ontology Subjects/Areas/Topics (3)

Biomedical Engineering

Biomedical Signal Processing

Physiological Processes and Bio-Signal Modeling, Non-Linear Dynamics

Delay-Aware Dynamic Wavelength Bandwidth Allocation in Time- and Wavelength-Division-Multiplexed Passive Optical Network

Topics: Passive Optical Networks; Optical Access Networks

Authors: Junsu Kim ¹ , Yong-Kyu Choi ¹ , Han Hyub Lee ² and Chang-Soo Park ¹

Affiliations: ¹ Gwangju Institue of Science and Technology (GIST), Korea, Republic of , ² Electronics and Telecommunications Research Institute (ETRI), Korea, Republic of

Digital Object Identifier: 10.5220/0005053600640068

Proceedings of OPTICS 2014/ICETE, ISBN: 978-989-758-044-4

Quick Abstract

Most research works related to dynamic wavelength bandwidth allocation in a time- and wavelength-division–multiplexed passive optical network are based on bin packing algorithm and present many advantages in terms of bandwidth utilization and energy efficiency. However, delay performance can be significantly degraded by frequent wavelength changes. In this paper, we propose a delay-aware dynamic wavelength bandwidth allocation algorithm. First, we theoretically analyze queuing delay corresponding to the tuning time, decision cycle, and the number of optical network units. And then, we show that the queuing delay is decreased by setting the limit value of the number of optical network units in a wavelength channel. In addition, we derive the threshold ratio of the tuning time to the decision cycle as 0.004.

Related Ontology Subjects/Areas/Topics (4)

Optical Access Networks		Passive Optical Networks		Telecommunications
Optical Communication Systems

Face Recognition based on Binary Images for Link Selection

Topics: Face and Expression Recognition; Features Extraction; Object and Face Recognition

Authors: Sanghun Lee ¹ , Soochang Kim ² , Young-hoon Kim ² and Chulhee Lee ¹

Affiliations: ¹ Yonsei University, Korea, Republic of , ² Electronics and Telecommunications Research Institute, Korea, Republic of

Digital Object Identifier: 10.5220/0004738605890593

Proceedings of VISAPP 2014/VISIGRAPP, ISBN: 978-989-758-004-8

Quick Abstract

A face recognition system which utilizes binary facial images and a bitwise similarity calculation method is proposed for link selection between mobile devices. As a pre-processing step, normalized differences of Gaussian and facial region estimation were used to handle illumination conditions. Binary images were used to extract facial feature sets that did not exceed 700 bytes. Scale pyramids and XNOR+AND similarity scores were used for fast feature matching between reference data sets and pre-processed test data. The proposed method achieved about an 85.9% recognition rate with a database that consisted of 135 facial images with various head poses, obtained by enrolling one reference data set per subject.

Related Ontology Subjects/Areas/Topics (3)

Computer Vision, Visualization and Computer Graphics

Features Extraction

Image and Video Analysis

Relationship between Affective Dimensions and Physiological Responses Induced by Emotional Stimuli - Base on Affective Dimensions: Arousal, Valence, Intensity and Approach

Topic: Human Factors in Physiological Computing

Authors: Eun-Hye Jang ¹ , Mi-Sook Park ² , Byoung-Jun Park ¹ , Sang-Hyeob Kim ¹ , Myung-Ae Chung ¹ and Jin-Hun Sohn ²

Affiliations: ¹ Electronics and Telecommunications Research Institute, Korea, Republic of , ² Chungnam National University, Korea, Republic of

Digital Object Identifier: 10.5220/0004728302540259

Proceedings of PhyCS 2014, ISBN: 978-989-758-006-2

Quick Abstract

In HCI, emotion recognition using physiological signals have been noticed lately because physiological signals can be simply acquired with some sensors and are less sensitive to social and cultural difference, in particular, there is strong correlation between human emotional states and physiological reactions. We have investigated the relation between affective dimensions, i.e., arousal, valence, intensity and approach, and physiological responses such as electrocardiograph (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmograph (PPG). Three hundred college students participated in the experiment. To successfully provoke basic emotions (anger, fear, sadness, boredom, interest, surprise, joy, pain, and neutral), emotion-provoking film clips were excerpted for each target emotion. Physiological signals (ECG, EDA, PPG and SKT) as emotional responses were measured during participants’ exposure to emotional stimuli and participants were asked to rate the specific emotions they had experienced on four affective dimensions, valence, arousal, intensity and approach. The result showed that there are correlations between affective dimensions and physiological responses. Contrary to valence and approach, arousal and intensity were positively related to heart rate (HR), skin conductance level (SCL) and skin conductance response (SCR), and showed negative relation to BVP and PTT. Our result suggests an availability of physiological signals for emotion recognition in HCI and can be helpful to provide the basis for the emotion recognition technique in HCI.

A Data Cube Model for Surveillance Video Indexing and Retrieval

Topics: Multimedia Databases, Indexing, Recognition and Retrieval; Semantic Analysis of Multimedia Data

Authors: Hansung Lee , Sohee Park and Jang-Hee Yoo

Affiliation: Electronics and Telecommunications Research Institute, Korea, Republic of

Digital Object Identifier: 10.5220/0004612101630168

Proceedings of SIGMAP 2013/ICETE, ISBN: 978-989-8565-74-7

Quick Abstract

We propose a novel data cube model, viz., \emph{SurvCube}, for the multi-dimensional indexing and retrieval of surveillance videos. The proposed method provides the multi-dimensional analysis of interesting objects in surveillance videos according to the chronological view, events and locations by means of data cube structure. By employing the OLAP operation on the surveillance videos, it is able to provides desirable functionalities such as 1) retrieval of objects and events at a different level of abstraction, i.e., coarse to fine grained retrieval; 2) providing the tracing of interesting object trajectories across the cameras; 3) providing the summarization of surveillance video with respect to interesting objects (and/or events) and abstract level of time and locations.

Related Ontology Subjects/Areas/Topics (5)

Multimedia		Multimedia Systems and Applications		Telecommunications
Multimedia Databases, Indexing, Recognition and Retrieval		Semantic Analysis of Multimedia Data

Architeture Design of a Network-centric Platform for Unified Communication and Collaboration Applications

Topics: Enterprise Information Systems; Communication, Collaboration and Information Sharing

Authors: Changsup Keum ¹ , Sungwon Kang ² and Hyunjoo Bae ¹

Affiliations: ¹ Electronics and Telecommunications Research Institute, Korea, Republic of , ² Korea Advanced Institute of Science and Technology, Korea, Republic of

Digital Object Identifier: 10.5220/0004168602940297

Proceedings of KMIS 2012/IC3K, ISBN: 978-989-8565-31-0

Quick Abstract

As an effective reuse paradigm, service-oriented computing provides a number of benefits. As another emerging trend, mobile computing has become a most common paradigm offering the conveniences of mobility, connectivity, ever-increasing computing power, and various sensors. However, mobile enterprise applications with complex business logics could not be easily deployed on mobile devices owing to resource limitations. An appealing approach to this issue is to deliver these enterprise applications by using a network-centric platform. In this paper, we present the architecture design of a network-centric platform that provides capabilities of UCC (unified communication and collaboration) service enablers, service creation environment, service delivery management, and service execution environment. The presented platform is developed with the design goals of wide applicability to diverse domains including unified communication and collaboration domain.

Related Ontology Subjects/Areas/Topics (6)

Artificial Intelligence		Enterprise Information Systems		Knowledge-Based Systems
Communication, Collaboration and Information Sharing		Knowledge Management and Information Sharing		Symbolic Systems

Sign Recognition with HMM/SVM Hybrid for the Visually-handicapped in Subway Stations

Topic: Pattern Recognition

Authors: Dong-jin Lee ¹ and Ho-sub Yoon ²

Affiliations: ¹ University of Science and Technology, Korea, Republic of , ² Electronics and Telecommunications Research Institute, Korea, Republic of

Digital Object Identifier: 10.5220/0004155006310634

Proceedings of NCTA 2012/IJCCI, ISBN: 978-989-8565-33-4

Quick Abstract

In this paper, we propose a sign classification system to recognize exit number and arrow signs in natural scene images. The purpose of the sign classification system is to provide assistance to a visually-handicapped person in subway stations. For automatically extracting sign candidate regions, we use Adaboost algorithm, however, our detector not only extracts sign regions, but also non-sign (noise) regions in natural scene images. Thus, we suggest a verification technique to discriminate sign regions from non-sign regions. In addition, we suggest a novel feature extraction algorithm cooperated with Hidden Markov Model. To evaluate the system, we tested a total of 20,177 sign candidate regions including the number of 8,414 non-sign regions on the captured images under several real environments in Daejeon in South Korea. We achieved an exit number and arrow sign recognition rate of each 99.5% and 99.8% and a false positive rate (FPR) of 0.3% to discriminate between sign regions and non-sign regions.

Related Ontology Subjects/Areas/Topics (13)

Artificial Intelligence	Human-Computer Interaction	Pattern Recognition
Biomedical Engineering	Methodologies and Methods	Physiological Computing Systems
Biomedical Signal Processing	Neurocomputing	Sensor Networks
Data Manipulation	Neurotechnology, Electronics and Informatics	Soft Computing
Health Engineering and Technology Applications

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