Classification of human state emotion from physiological signal pattern using pulse sensor based on learning vector quantization

Marzuki, Akhmad; Rumpa, Lantana Dioren; Wibawa, Adhi Dharma; Purnomo, Mauridhi Hery

doi:10.1109/isitia.2016.7828646

Cited by 7 publications

(4 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…al. only used PR signal to propose emotion recognition [6]. Also, our previous work used SpO2 and PR signals for emotion recognition in elders [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Hakim

Kristanto

Khoirunnisaa

et al. 2020

KINETIK

Self Cite

View full text Add to dashboard Cite

Emotion recognition has been special topics which are frequently discussed by researchers and practitioners in the past decade. SpO2 and Pulse rate signals are two of the limited physiological signals used in a study of emotion recognition. In this study, both of the physiological signals were utilized for emotion recognition in elders by using a combination of two feature extraction methods. Multiscale Entropy was used for feature extraction of signals and then reduced dimension of the feature vector by using Multiclass Fisher’s Linear Discriminant Analysis. The dimensional reduction process was grouped into three experimental schemes, namely a dimensional reduction by using only SpO2 signal, the second scheme by using pulse rate signal and the third scheme by multimodal signals (a combination of SpO2 and Pulse rate signals). The three schemes were then classified into three emotion classes (happy, sad and angry emotions) using Support Vector Machine and Linear Discriminant Analysis Methods. The Classification used Support Vector Machine with an input feature of the third scheme showed an optimal performance with an accuracy score of 95.24%. This result showed better accuracy than previous works.

show abstract

“…al. only used PR signal to propose emotion recognition [6]. Also, our previous work used SpO2 and PR signals for emotion recognition in elders [7].…”

Section: Introductionmentioning

confidence: 99%

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Hakim

Kristanto

Khoirunnisaa

et al. 2020

KINETIK

Self Cite

View full text Add to dashboard Cite

show abstract

“…Then, the signal was extracted in alpha waves 9-14 Hz with five steps for getting into 12 points. Beta wave extraction was after four steps (15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30)(31)(32), gave 36 points. After wavelet filtering, each second, using wavelet extraction we reduced 128 points into 56 points data.…”

Section: B Wavelet Extractionmentioning

confidence: 99%

“…EEG signals involve a great deal of information about the function of the brain, which may reflect a state of mind, such as level of attention [2], relax condition [3], mental activity [4], human grasping [5] [6], human attention [7], alertness level [8], or emotional conditions [9], [1], [10], and [11], [12]- [18]. Several studies on identification of emotional states through EEG signals are the response of sound stimulation [19], after watching movies [20], watching ads [21], listening to music [11], playing video game [12] , and watching videos [22], [23]. Moreover, wireless EEG provide comfort so emotional identification of brain signals can be an intermediate device in the development of Brain Computer Interface (BCI) [24] [25].…”

Section: Introductionmentioning

confidence: 99%

“…This research focuses on identifying emotion state. Prior research on EEG based emotion identification using statistical [38], Support Vector Machine (SVM) [39], Multilayer Perceptron [40] and Learning Vector Quantization (LVQ) [23]. Although there are varieties of emotional states to describe the human's feelings, until now only limited types of emotions can be recognized using EEG.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

EEG Based Emotion Monitoring Using Wavelet and Learning Vector Quantization

Djamal¹,

Lodaya²

2017

EECSI

View full text Add to dashboard Cite

Emotional identification is necessary for example in Brain Computer Interface (BCI) application and when emotional therapy and medical rehabilitation take place. Some emotional states can be characterized in the frequency of EEG signal, such excited, relax and sad. The signal extracted in certain frequency useful to distinguish the three emotional state. The classification of the EEG signal in real time depends on extraction methods to increase class distinction, and identification methods with fast computing. This paper proposed human emotion monitoring in real time using Wavelet and Learning Vector Quantization (LVQ). The process was done before the machine learning using training data from the 10 subjects, 10 trial, 3 classes and 16 segments (equal to 480 sets of data). Each data set processed in 10 seconds and extracted into Alpha, Beta, and Theta waves using Wavelet. Then they become input for the identification system using LVQ three emotional state that is excited, relax, and sad. The results showed that by using wavelet we can improve the accuracy of 72% to 87% and number of training data variation increased the accuracy. The system was integrated with wireless EEG to monitor emotion state in real time with change each 10 seconds. It takes 0.44 second, was not significant toward 10 seconds.

show abstract

Heart Rate Variability (HRV) during anger emotion stimulation: features for affective

Rumpa

Toding

Jefriyanto

et al. 2021

IOP Conf. Ser.: Mater. Sci. Eng.

View full text Add to dashboard Cite

Recent studies have revealed the relationship between human Emotion and health. Researchers are trying to find the best features to distinguish human Emotion through its psychophysiology signals in the Affective Computing study. Humans have six basic emotions, such as Anger, Sadness, Happy, Surprise, Fear, and Disgust. Emotions affect human ANS (Autonomous Nervous System). The heart is one of the human inner organs affected by the ANS (working under ANS). Heart rate is one of the Psychophysiological signals that changed depends on human Emotion. This study tried to analyze the difference between human heart rate during Anger and Normal Emotion Stimulation. Video Stimuli were used to Evoke Emotion. 15 Participant’s heartbeats were recorded using an ECG sensor from E-Health Sensor Platform v2.0. We used HRV (Heart Rate Variability) MeanRR feature as a comparison. Some studies said that experiencing long-term Anger Emotions can cause serious illness to the human body. There are some differences in the MeanRR feature between ANGER State and NORMAL State. The findings of this investigation complement those of earlier studies about HRVi. This research supports the idea that HRV can be used as a feature to detect human affect state.

show abstract

Classification of human state emotion from physiological signal pattern using pulse sensor based on learning vector quantization

Cited by 7 publications

References 6 publications

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

Multi-scale Entropy and Multiclass Fisher’s Linear Discriminant for Emotion Recognition Based on Multimodal Signal

EEG Based Emotion Monitoring Using Wavelet and Learning Vector Quantization

Heart Rate Variability (HRV) during anger emotion stimulation: features for affective

Contact Info

Product

Resources

About