Two Journal Articles from the Emo-Sense Project Published!

• EmoSense: a wearable technology, uses a three-layer mechanism to convert microgestures (MGs) into digital signals, apply machine learning for MG detection, and assess stress levels based on MG frequency; a pilot study with 16 participants confirms the correlation between stress and MG frequency, as well as the link between stress and other negative emotions.
• Emo-MG Framework: achieves outstanding performance in emotion detection by comparing it to baseline and deep learning models.

BibTex

@article{fang2023emosense,
    title = {{{EmoSense}}: {{Revealing True Emotions Through Microgestures}}},
    author = {Fang, Le and Xing, Sark Pangrui and Long, Yonghao and Lee, Kun-Pyo and Wang, Stephen Jia},
    date = {2023},
    journaltitle = {Advanced Intelligent Systems},
    volume = {n/a},
    number = {n/a},
    pages = {2300050},
    doi = {10.1002/aisy.202300050},
    urldate = {2023-07-31},
    langid = {english}
}

BibTex

@article{fang2023emo,
    title = {Emo-{{MG}} Framework: {{LSTM-based}} Multi-Modal Emotion Detection throughElectroencephalography Signals and Micro Gestures},
    author = {Fang, Le and Xing, Sark Pangrui and Ma, Zhengtao and Zhang, Zhijie and Long, Yonghao and Lee, Kun-Pyo and Wang, Stephen Jia},
    date = {2023},
    journaltitle = {International Journal of Human–Computer Interaction},
    volume = {0},
    number = {0},
    eprint = {https://doi.org/10.1080/10447318.2023.2228983},
    pages = {1--17},
    publisher = {{Taylor \& Francis}},
    doi = {10.1080/10447318.2023.2228983}
}