Real-Time Multiplatform Emotion Classification Using CNN in a Fog Computing Environment

摘要

This paper introduces a robust multiplatform artificial intelligence (AI) system designed for real-time emotion classification in service robotics. The primary focus of this study is the development and implementation of an initial phase AI model within a diverse hardware ecosystem, enabling a Service Robot to interact dynamically with users in real-time, complemented by cloud connectivity and enhanced by fog computing techniques. Utilizing convolutional neural networks (CNNs) via TensorFlow in Python, emotion classification models were crafted to discern various emotional states accurately. The AI system’s multiplatform environment comprised three distinct processing units: a laptop with Windows OS and high-end specifications, Jetson Orin running on a Linux architecture, and Raspberry Pi-4 operating on Raspbian OS. The inclusion of fog computing allowed for local data processing near the source, significantly reducing latency and ensuring faster response times, crucial for real-time applications. Training and comparative analysis were conducted on both the laptop and Jetson Orin, leveraging the GPU capabilities of each device. The Raspberry Pi-4 was allocated for monitoring and sensor data collection. Results revealed an impressive average performance of 90% in accurately classifying diverse emotional states. Training duration ranged from 20 minutes on the laptop to 30 minutes on Jetson Orin, with faster program execution observed on Jetson Orin due to its lightweight operating system. Cloud connectivity played a pivotal role in wirelessly monitoring, controlling, and facilitating real-time data collection. Additionally, it enabled the implementation of alert systems and notifications throughout the classification process. This pioneering approach lays a robust foundation for the advancement of sophisticated human-machine interaction systems, setting the stage for further research in the realm of AI-driven service robotics.