Human Emotion Recognition Using Multi-Modal Biological Signals Based On Time Lag-Considered Correlation Maximization

Moroto, Yuya; Maeda, Kazuo; Ogawa, Takahiro; Haseyama, Miki

doi:10.1109/icassp43922.2022.9746128

Cited by 1 publication

(1 citation statement)

References 28 publications

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“…Tatarian et al (2022) provide a multimodal interaction that focuses on proxemics of interpersonal navigating, gaze mechanics, kinesics, and social conversation, examining the impact of multimodal actions on relative social IQ using both subjective and objective assessments in a seven-minute encounter with 105 participants. Moroto et al (2022) develop a recognition approach that considers the time delay to get genuinely near the reality of the occurring mechanism of feelings, with experimental findings demonstrating the usefulness of taking into account the time lag between gazing and brain function data. He et al (2022) present a unique multimodal M2NN model using the merging of EEG and fNIRS inputs to increase the recognition speed and generalization capacity of MI, combining spatial-temporal extraction of features, multimodal feature synthesis, and MTL.…”

Section: Major Application Areas Of Multimodal Hrimentioning

confidence: 99%

Recent advancements in multimodal human–robot interaction

Yang

et al. 2023

Front. Neurorobot.

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Robotics have advanced significantly over the years, and human–robot interaction (HRI) is now playing an important role in delivering the best user experience, cutting down on laborious tasks, and raising public acceptance of robots. New HRI approaches are necessary to promote the evolution of robots, with a more natural and flexible interaction manner clearly the most crucial. As a newly emerging approach to HRI, multimodal HRI is a method for individuals to communicate with a robot using various modalities, including voice, image, text, eye movement, and touch, as well as bio-signals like EEG and ECG. It is a broad field closely related to cognitive science, ergonomics, multimedia technology, and virtual reality, with numerous applications springing up each year. However, little research has been done to summarize the current development and future trend of HRI. To this end, this paper systematically reviews the state of the art of multimodal HRI on its applications by summing up the latest research articles relevant to this field. Moreover, the research development in terms of the input signal and the output signal is also covered in this manuscript.

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Section: Major Application Areas Of Multimodal Hrimentioning

confidence: 99%

Recent advancements in multimodal human–robot interaction

Yang

et al. 2023

Front. Neurorobot.

View full text Add to dashboard Cite

show abstract

Human Emotion Recognition Using Multi-Modal Biological Signals Based On Time Lag-Considered Correlation Maximization

Cited by 1 publication

References 28 publications

Recent advancements in multimodal human–robot interaction

Recent advancements in multimodal human–robot interaction

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