Key research themes
1. How can Raspberry Pi-based smart mirrors integrate multimodal interaction and IoT functions for enhanced home automation and user convenience?
This research area focuses on leveraging Raspberry Pi microcomputers combined with various sensors and modules to create smart mirrors capable of face recognition, voice interaction, multimedia display, and IoT home automation functionalities. It is significant because it enables affordable, compact, and adaptable smart home interfaces that enhance daily user engagement and automate routine tasks seamlessly.
2. What are the current machine learning and computer vision approaches utilized in smart mirrors for user identification, security, and health monitoring?
This theme investigates the application of machine learning techniques such as convolutional neural networks (CNNs), YOLO object detection, and multimodal computer vision to enable smart mirrors with person identification, intrusion detection, and health status assessment. These capabilities enhance personalization, security, and potential healthcare functionalities within ambient assisted living environments.
3. How are smart mirrors being adapted for specialized public and institutional information systems using voice interaction and environmental monitoring?
This research stream explores smart mirrors configured as interactive information desks or ambient interfaces in public institutions like universities. It emphasizes voice-assisted query handling, real-time environment sensing (e.g., entrance monitoring), and multimodal interactivity aimed at improving accessibility, reducing dependency on staff, and supporting users including disabled populations.