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

Rattanyu, Kanlaya; Mizukawa, Makoto

doi:10.20965/jaciii.2011.p0582

Cited by 21 publications

(27 citation statements)

References 28 publications

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“…Several researches proposed system to recognize users' emotion by biological signal [14][15][16][17][18][19]. However, most of them focused on techniques to improve the performance to recognize users' emotions and a few researches provided feedback and response to users.…”

Section: Emotion Recognition Using Biological Signalsmentioning

confidence: 99%

Healthcare System Focusing on Emotional Aspect using Augmented Reality

Tivatansakul

Ohkura

2014

Transactions of Japan Society of Kansei Engineering

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Even though much recent research has proposed systems that provide assistance and services to people, these systems emphasize the support of the physical aspects at the expense of emotional aspects. However, emotional health is as important as physical health. Moreover, negative emotional health can lead to social or mental health problems. People with good emotional health can control themselves and can address their negative emotions. However, people with bad emotional health feel difficult to control and decrease their negative emotions. Therefore the negative emotional health can lead to social and mental health problems. To cope with negative emotional health in their daily life, we proposed a new healthcare system that focuses on emotional aspects. The system integrates augmented reality to display virtual objects in real environments and Kinect, which allows users to freely interact with them. We also employ biological sensors to measure and detect user emotions, and provide three services based on expected emotions: relaxation, amusement and excitement services. We designed and implemented a small prototype for our relaxation service called: a breathing control application. This application applies deep breathing techniques of stress management to supports users when they experience stress from society or work as well as other negative emotions. This application displays a virtual music box to assist them breathe deeply because virtual objects and music might increase user relaxation and decrease their stress. We also evaluated this application by performing the experiment. The stress and relaxation of the participants are measured using salivary amylase test. As the result, the application can decrease their stress.

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Section: Emotion Recognition Using Biological Signalsmentioning

confidence: 99%

Healthcare System Focusing on Emotional Aspect using Augmented Reality

Tivatansakul

Ohkura

2014

Transactions of Japan Society of Kansei Engineering

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“…Some of the widely used bio-signals include the electrocardiogram (ECG), galvanic skin response (GSR), electromyogram and respiration. Some studies have combined multiple bio-signal devices to recognize emotions, whereas other studies have used a single biosensor such as ECG for capturing data for emotion recognition [8,9,10]. Despite the increased recognition accuracy, using multiple sensors might lead to user dissatisfaction.…”

Section: Introductionmentioning

confidence: 99%

“…Despite the increased recognition accuracy, using multiple sensors might lead to user dissatisfaction. Compared to other biosensors, ECG is the most widely used biosensor because ECG signals are less noisy and they contain emotion related information [9,11].…”

Section: Introductionmentioning

confidence: 99%

An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

Dissanayake

Rajapaksha

Ragel

et al. 2019

Sensors

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Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.

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“…In Ref. 10, the writers mainly focus on emotion recognition using ECG. Authors proposed to use the ECG's interbeat features together with within-beat features in the recognition system.…”

Section: Introductionmentioning

confidence: 99%

Physiological Signal Analysis for Cognitive State Estimation

Ahmad

Islam

Munia

et al. 2012

Biomed. Eng. Appl. Basis Commun.

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The purpose of this paper is to identify inconsistency in human physiological signals based on cognitive states by measuring and analyzing bio-signals. In this paper, the cognitive states are estimated using physiological signal analysis. The parameters are electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG) and blood pressure (BP). The signals have been collected using BIOPAC system in which the subjects were induced to undergo the specific sequence of the cognitive state. For getting physiological signals during different conditions, we utilized power point slide show, video clips and question answer method which elicits mental reactions from the subjects. Data is taken before and after four tasks that encompassed the motor action (MA), thought (TH), memory related (MR) and emotion (EM). These measured values are analyzed using BIOPAC Acknowledge software. It was found that the motor action and thought states have effects on BP while MR and EM state mainly affect the ECG measurement. The decibel value and frequency found for EM state in ECG are minimum compared to relaxed state (RS) condition. Similarly, the maximum frequency and dB value is found for MR state. No significant variation was seen for MA and TH states. Thus it was decided that the MR and EM states mainly affect the ECG measurement. For BP the value increases in MA state and decreases in TH state. The MA state mainly affects the EMG signal while other states have no significant changes. The EEG mainly detects the signal of task performed by the specific brain region where the electrodes are placed. In EEG analysis, the electrodes are placed in occipital lobe region which gives mainly the variation in alpha amplitude of EEG with eyes closed and eyes opened. Alpha wave amplitudes vary with the subjects attention to mental tasks performed with eyes closed.

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

Cited by 21 publications

References 28 publications

Healthcare System Focusing on Emotional Aspect using Augmented Reality

Healthcare System Focusing on Emotional Aspect using Augmented Reality

An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition

Physiological Signal Analysis for Cognitive State Estimation

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