CyanKitten: AI-Driven Markerless Motion Capture for Improved Elderly Well-Being
Year: 2025
Type: preprint
Abstract: This paper introduces CyanKitten, an interactive virtual companion system tailored for elderly users, integrating advanced posture recognition, behavior recognition, and multimodal interaction capabilities. The system utilizes a three-tier architecture to process and interpret user movements and gestures, leveraging a dual-camera setup and a convolutional neural network trained explicitly on elderly movement patterns. The behavior recognition module identifies and responds to three key interactive gestures: greeting waves, petting motions, and heart-making gestures. A multimodal integration layer also combines visual and audio inputs to facilitate natural and intuitive interactions. This paper outlines the technical implementation of each component, addressing challenges such as elderly-specific movement characteristics, real-time processing demands, and environmental adaptability. The result is an engaging and accessible virtual interaction experience designed to enhance the quality of life for elderly users.
Source: arXiv (Cornell University)
Authors: Guo, Mengyao, Nie, Yu, Han, Jinda, Li, Zongxing, Gao, Ze
Language: English
Subfield: Human-Computer Interaction
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6910850845
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