An AI-edge platform with multimodal wearable physiological signals monitoring sensors for affective computing applications

Keyphrases

• Convolution Neural Network 100%
• Electroencephalogram 100%
• Monitoring System 100%
• Physiological Monitoring 100%
• Photoplethysmography 100%
• Affective Computing Applications 100%
• Edge Platform 100%
• Physiological Signals 66%
• Emotion Recognition 66%
• Classification System 33%
• Feature Vector 33%
• Electroencephalography 33%
• Access Point 33%
• Anger 33%
• Sadness 33%
• Happiness 33%
• Bluetooth piconet 33%
• Short-time Fourier Transform 33%
• Wireless Technology 33%
• Time Monitoring 33%
• Photoplethysmography Sensor 33%
• Emotion Classification 33%
• Low-power Wireless Networks 33%
• Single Platform 33%
• Machine Learning Approach 33%
• Subject-dependent 33%
• FPGA Platform 33%
• Signal Sensor 33%
• Electrocardiogram Sensors 33%
• Real-time Classification 33%
• Subject-independent Classification 33%
• RISC-V Processor 33%
• Combined Machine 33%

Computer Science

• Convolutional Neural Network 100%
• Physiological Signal 100%
• Edge AI 100%
• Affective Computing 100%
• Average Accuracy 66%
• Recognition System 33%
• Feature Vector 33%
• Access Point 33%
• Extracted Feature 33%
• Piconets 33%
• Wireless Technologies 33%
• Time Monitoring 33%
• Preprocessing 33%
• Time Fourier Transform 33%
• Input Processing 33%
• Machine Learning-Based Approach 33%
• Field Programmable Gate Arrays 33%