Comparison of Subjective Facial Emotion Recognition and “Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection” between Patients with Schizophrenia and Healthy Participants

Akiyama, Toshiya; Matsumoto, Kazuyuki; Osaka, Kyoko; Tanioka, Ryuichi; Betriana, Feni; Zhao, Yueren; Kai, Yoshihiro; Miyagawa, Misao; Yasuhara, Yuko; Ito, Hironobu; Soriano, Gil P.; Tanioka, Tetsuya

doi:10.3390/healthcare10122363

Cited by 2 publications

(1 citation statement)

References 36 publications

(41 reference statements)

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“…This requires robots to have a high degree of multimodal perception, as they need to understand the mental moods, goals, and character aspects of humans in order to provide appropriate feedback [7]. Along with facial recognition through convolutional neural networks, several studies have used bio-signals such as heart rate variability, electroencephalogram signals, and eye modality [8] [9]. Others have used visual-audio signals, lexicographic data, and questionnaire-based data [10].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Emotions Using Multimodal Data: A Case Study

Akiyama,

Osaka,

Ito

et al. 2023

JBM

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In this case study, we hypothesized that sympathetic nerve activity would be higher during conversation with PALRO robot, and that conversation would result in an increase in cerebral blood flow near the Broca's area. The facial expressions of a human subject were recorded, and cerebral blood flow and heart rate variability were measured during interactions with the humanoid robot. These multimodal data were time-synchronized to quantitatively verify the change from the resting baseline by testing facial expression analysis, cerebral blood flow, and heart rate variability. In conclusion, this subject indicated that sympathetic nervous activity was dominant, suggesting that the subject may have enjoyed and been excited while talking to the robot (normalized High Frequency < normalized Low Frequency: 0.22 ± 0.16 < 0.78 ± 0.16). Cerebral blood flow values were higher during conversation and in the resting state after the experiment than in the resting state before the experiment. Talking increased cerebral blood flow in the frontal region. As the subject was left-handed, it was confirmed that the right side of the brain, where the Broca's area is located, was particularly activated (Left < right: 0.15 ± 0.21 < 1.25 ± 0.17). In the sections where a "happy" facial emotion was recognized, the examiner-judged "happy" faces and the MTCNN "happy" results were also generally consistent.

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Section: Introductionmentioning

confidence: 99%

Analysis of Emotions Using Multimodal Data: A Case Study

Akiyama,

Osaka,

Ito

et al. 2023

JBM

Self Cite

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Efficient social media sentiment analysis using confidence interval-based classification of online product brands

Guesmi,

Al-Janfawi,

Guesmi

et al. 2023

Int. j. adv. appl. sci.

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This paper presents an efficient method for categorizing the sentiments of Internet users, with a focus on social media users, using a confidence interval to estimate the reliability of sentiment predictions. The classification is based on the sentiments expressed in their posts, which are divided into positive, negative, and neutral categories. The paper presents an analysis table that analyzes sentiments and opinions about online product brands. The process includes two steps: 1) analyzing sentiments from text data using machine learning techniques, and 2) describing a five-step sentiment and opinion classification process that includes data collection, preprocessing, algorithm application, validation, and visualization. The proposed solution is implemented using Python, along with the scikit-learn, NumPy, pandas, and Dash libraries, and leverages the use of confidence intervals to assess the accuracy and reliability of the sentiment analysis model.

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Comparison of Subjective Facial Emotion Recognition and “Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection” between Patients with Schizophrenia and Healthy Participants

Cited by 2 publications

References 36 publications

Analysis of Emotions Using Multimodal Data: A Case Study

Analysis of Emotions Using Multimodal Data: A Case Study

Efficient social media sentiment analysis using confidence interval-based classification of online product brands

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