Research on genetic algorithm based on emotion recognition using physiological signals

Niu, Xiaowei; Chen, Liwan; Chen, Qiang

doi:10.1109/iccps.2011.6092256

Cited by 23 publications

(8 citation statements)

References 4 publications

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“…Biosignals such as heart rate (HR), blood volume pressure, external body temperature, electrodermal activity (EDA), and electroencephalography (EEG) are the physiological signals of the human body that drastically change during changes of emotional state [ 16 ]. Analyzing the change of physiological signal recordings can determine the emotional state of the human [ 17 ] by using devices that record and track the changes of the physiological signals, called biosensors [ 18 , 19 ]. Analyzing the data recordings from those devices for emotional state detection has been the research interest of recent studies.…”

Section: Introductionmentioning

confidence: 99%

Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study

Antoniou¹,

Arfaras²,

Pandria³

et al. 2020

JMIR Serious Games

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Background The role of emotion is crucial to the learning process, as it is linked to motivation, interest, and attention. Affective states are expressed in the brain and in overall biological activity. Biosignals, like heart rate (HR), electrodermal activity (EDA), and electroencephalography (EEG) are physiological expressions affected by emotional state. Analyzing these biosignal recordings can point to a person’s emotional state. Contemporary medical education has progressed extensively towards diverse learning resources using virtual reality (VR) and mixed reality (MR) applications. Objective This paper aims to study the efficacy of wearable biosensors for affect detection in a learning process involving a serious game in the Microsoft HoloLens VR/MR platform. Methods A wearable array of sensors recording HR, EDA, and EEG signals was deployed during 2 educational activities conducted by 11 participants of diverse educational level (undergraduate, postgraduate, and specialist neurosurgeon doctors). The first scenario was a conventional virtual patient case used for establishing the personal biosignal baselines for the participant. The second was a case in a VR/MR environment regarding neuroanatomy. The affective measures that we recorded were EEG (theta/beta ratio and alpha rhythm), HR, and EDA. Results Results were recorded and aggregated across all 3 groups. Average EEG ratios of the virtual patient (VP) versus the MR serious game cases were recorded at 3.49 (SD 0.82) versus 3.23 (SD 0.94) for students, 2.59 (SD 0.96) versus 2.90 (SD 1.78) for neurosurgeons, and 2.33 (SD 0.26) versus 2.56 (SD 0.62) for postgraduate medical students. Average alpha rhythm of the VP versus the MR serious game cases were recorded at 7.77 (SD 1.62) μV versus 8.42 (SD 2.56) μV for students, 7.03 (SD 2.19) μV versus 7.15 (SD 1.86) μV for neurosurgeons, and 11.84 (SD 6.15) μV versus 9.55 (SD 3.12) μV for postgraduate medical students. Average HR of the VP versus the MR serious game cases were recorded at 87 (SD 13) versus 86 (SD 12) bpm for students, 81 (SD 7) versus 83 (SD 7) bpm for neurosurgeons, and 81 (SD 7) versus 77 (SD 6) bpm for postgraduate medical students. Average EDA of the VP versus the MR serious game cases were recorded at 1.198 (SD 1.467) μS versus 4.097 (SD 2.79) μS for students, 1.890 (SD 2.269) μS versus 5.407 (SD 5.391) μS for neurosurgeons, and 0.739 (SD 0.509) μS versus 2.498 (SD 1.72) μS for postgraduate medical students. The variations of these metrics have been correlated with existing theoretical interpretations regarding educationally relevant affective analytics, such as engagement and educational focus. Conclusions These results demonstrate that this novel sensor configuration can lead to credible affective state detection and can be used in platforms like intelligent tutoring systems for providing real-time, evidence-based, affective learning analytics using VR/MR-deployed medical education resources.

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Section: Introductionmentioning

confidence: 99%

Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study

Antoniou¹,

Arfaras²,

Pandria³

et al. 2020

JMIR Serious Games

View full text Add to dashboard Cite

show abstract

“…Artifacts and interference in raw physiological signals are removed by using adaptive filter, Laplacian filter, visual inspection and Independent component analysis (ICA). Low pass IIR filter, Butterworth lowpass filter, elliptic filter, moving average filter are also used for removing noise [4] [8]. To avoid power line contamination notch filter is used.…”

Section: Preprocessingmentioning

confidence: 99%

Emotion Recognition Based on Various Physiological Signals - A Review

Londhe¹,

Borse²

2018

IJCT

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Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition.

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“…와 자기설문평가(Self Assessment)를 통하여 감정을 유추할 수 있으나 설문조사는 피험자의 주관적 생각 을 반영하는 한계점을 가진다 (Niu et al, 2011 (Jerritta et al, 2011;Khashman, 2008;Murugappan et al, 2010;Peng et al, 2014;Schaaff & Schultz, 2009;Wang & Shang, 2013 (Haag et al, 2004).…”

Section: 감정을 유발 시킨 후에 리커트 척도(Likert Scale)unclassified

Neural-network based Computerized Emotion Analysis using Multiple Biological Signals

Lee¹,

Kim²,

Yoo³

2017

KOSES

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Emotion affects many parts of human life such as learning ability, behavior and judgment. It is important to understand human nature. Emotion can only be inferred from facial expressions or gestures, what it actually is. In particular, emotion is difficult to classify not only because individuals feel differently about emotion but also because visually induced emotion does not sustain during whole testing period. To solve the problem, we acquired bio-signals and extracted features from those signals, which offer objective information about emotion stimulus. The emotion pattern classifier was composed of unsupervised learning algorithm with hidden nodes and feature vectors. Restricted Boltzmann machine (RBM) based on probability estimation was used in the unsupervised learning and maps emotion features to transformed dimensions. The emotion was characterized by non-linear classifiers with hidden nodes of a multi layer neural network, named deep belief network (DBN). The accuracy of DBN (about 94 %) was better than that of back-propagation neural network (about 40 %). The DBN showed good performance as the emotion pattern classifier.

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Research on genetic algorithm based on emotion recognition using physiological signals

Cited by 23 publications

References 4 publications

Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study

Biosensor Real-Time Affective Analytics in Virtual and Mixed Reality Medical Education Serious Games: Cohort Study

Emotion Recognition Based on Various Physiological Signals - A Review

Neural-network based Computerized Emotion Analysis using Multiple Biological Signals

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