Journal
2020 17TH INTERNATIONAL SOC DESIGN CONFERENCE (ISOCC 2020)
Volume -, Issue -, Pages 29-30Publisher
IEEE
DOI: 10.1109/ISOCC50952.2020.9333072
Keywords
Emotion recognition; physiological signals; affective computing; multimodal analysis; convolutional neural network
Ask authors/readers for more resources
In this paper, we developed and integrated a real-time 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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available