@inproceedings{300f7b6500684550937a6272f6b9da38,
title = "A Real-Time Emotion Recognition System Based on an AI System-On-Chip Design",
abstract = "In this paper, we developed and integrated a realtime emotion recognition system using an AI system-on-chip design. The emotion recognition platform combined three different physiological signals, Electroencephalogram (EEG), electrocardiogram (ECG), and photoplethysmogram (PPG) as the classification resources. A 3-To-1 Bluetooth piconet was deployed to transmit all physiological signals on a single platform access point and to make use of low power wireless technologies. The system then integrated an AI computing chip with a convolution neural network (CNN) structure to classify three emotions, happiness, anger, and sadness. The average accuracy for a subject-independent classification reached 72.66%. The proposed system was integrated with the RISC-V processor and AI SOC to implement real-Time monitoring and classification on edge. ",
keywords = "affective computing, convolutional neural network, Emotion recognition, multimodal analysis, physiological signals",
author = "Li, {Wei Chih} and Yang, {Cheng Jie} and Fang, {Wai Chi}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.; 17th International System-on-Chip Design Conference, ISOCC 2020 ; Conference date: 21-10-2020 Through 24-10-2020",
year = "2020",
month = oct,
day = "21",
doi = "10.1109/ISOCC50952.2020.9333072",
language = "English",
series = "Proceedings - International SoC Design Conference, ISOCC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "29--30",
booktitle = "Proceedings - International SoC Design Conference, ISOCC 2020",
address = "United States",
}