Awareness and Readiness of Malaysian University Students for Emotion Recognition System

Emotion Recognition System (ERS) identifies human emotion like happiness, sadness, anger, disgust and fear. These emotions can be detected via various modalities such as facial expression analysis, voice intonation, and physiological signals like the brain’s electroencephalogram (EEG) and heart’s electrocardiogram (ECG). The emotion recognition system allows machines to recognized human emotions and reacts to it. It offers broad areas of application, from smart home automation to entertainment recommendation system to driving assistance and to automated security system. It is a promising and interesting field to be explored especially as we are moving towards industrial revolution 5.0. Therefore, a survey was conducted on the awareness and readiness of the usage of emotion recognition system among Malaysian youths, specifically among university students. The findings are presented here. Overall, positive orientation towards the technology is observed among the participants and they are ready for its adoption

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Emotion Recognition System by a Neural Network Based Facial Expression Analysis

Automatika ◽

10.7305/automatika.54-2.73 ◽

2013 ◽

Vol 54 (2) ◽

pp. 263-272 ◽

Cited By ~ 17

Author(s):

Damir Filko ◽

Goran Martinović

Keyword(s):

Neural Network ◽

Facial Expression ◽

Emotion Recognition ◽

Expression Analysis ◽

Recognition System ◽

Facial Expression Analysis

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A Review of Emotion Recognition Based on EEG using DEAP Dataset

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset218352 ◽

2021 ◽

pp. 298-303

Author(s):

Rama Chaudhary ◽

Ram Avtar Jaswal

Keyword(s):

Facial Expression ◽

Emotion Recognition ◽

Facial Expressions ◽

Physiological Signals ◽

Emotional States ◽

Modern Time ◽

Human Machine Interaction ◽

Electrocardiogram Ecg ◽

Electroencephalogram Eeg ◽

Machine Interaction

In modern time, the human-machine interaction technology has been developed so much for recognizing human emotional states depending on physiological signals. The emotional states of human can be recognized by using facial expressions, but sometimes it doesn’t give accurate results. For example, if we detect the accuracy of facial expression of sad person, then it will not give fully satisfied result because sad expression also include frustration, irritation, anger, etc. therefore, it will not be possible to determine the particular expression. Therefore, emotion recognition using Electroencephalogram (EEG), Electrocardiogram (ECG) has gained so much attraction because these are based on brain and heart signals respectively. So, after analyzing all the factors, it is decided to recognize emotional states based on EEG using DEAP Dataset. So that, the better accuracy can be achieved.

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Smart MM

Cognitive Computing in Technology-Enhanced Learning - Advances in Educational Technologies and Instructional Design ◽

10.4018/978-1-5225-9031-6.ch011 ◽

2019 ◽

pp. 225-251

Author(s):

Meenakshi Tripathi ◽

Saatvik Shah ◽

Prashant Bahal ◽

Harsh Sharma ◽

Ritika Gupta

Keyword(s):

Artificial Intelligence ◽

Emotion Recognition ◽

Real Time ◽

Management System ◽

Recommendation System ◽

Information Gain ◽

Recognition System ◽

Media Management ◽

User Friendly ◽

Made In

Rapid advancements have been made in the field of artificial intelligence in recent years. This has resulted in its adoption in various technologies from medicine to search engines. Existing media management systems have however not yet fully leveraged the power of artificial intelligence (AI) to give users enhanced information apart from basic media metadata. This chapter proposes a smart movie management system which works majorly offline and uses AI to deliver optimum information to the users on four vital tasks. These tasks are multilevel phrase level review polarity, plot and review keywords, a content-based recommendation system, and an emotion recognition system. The complete system works in near-real time with a user-friendly presentation to maximize a user's information gain.

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Emotion Recognition Based on ECG Signals for Service Robots in the Intelligent Space During Daily Life

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2011.p0582 ◽

2011 ◽

Vol 15 (5) ◽

pp. 582-591 ◽

Cited By ~ 9

Author(s):

Kanlaya Rattanyu ◽

◽

Makoto Mizukawa ◽

Keyword(s):

Emotion Recognition ◽

Feature Space ◽

Healthcare Services ◽

Recognition System ◽

Service Robots ◽

Emotional States ◽

Ecg Signals ◽

Post Hoc ◽

Electrocardiogram Ecg

This paper presents our approach for emotion recognition based on Electrocardiogram (ECG) signals. We propose to use the ECG’s inter-beat features together with within-beat features in our recognition system. In order to reduce the feature space, post hoc tests in the Analysis of Variance (ANOVA) were employed to select the set of eleven most significant features. We conducted experiments on twelve subjects using the International Affective Picture System (IAPS) database. RF-ECG sensors were attached to the subject’s skin to monitor the ECG signal via wireless connection. Results showed that our eleven feature approach outperforms the conventional three feature approach. For simultaneous classification of six emotional states: anger, fear, disgust, sadness, neutral, and joy, the Correct Classification Ratio (CCR) showed significant improvement from 37.23% to over 61.44%. Our system was able to monitor human emotion wirelessly without affecting the subject’s activities. Therefore it is suitable to be integrated with service robots to provide assistive and healthcare services.

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