CyanKitten: AI-Driven Markerless Motion Capture for Improved Elderly Well-Being
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909551568093184 |
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| author | Guo, Mengyao Nie, Yu Han, Jinda Li, Zongxing Gao, Ze |
| author_facet | Guo, Mengyao Nie, Yu Han, Jinda Li, Zongxing Gao, Ze |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_19398 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CyanKitten: AI-Driven Markerless Motion Capture for Improved Elderly Well-Being Guo, Mengyao Nie, Yu Han, Jinda Li, Zongxing Gao, Ze Human-Computer Interaction F.2.2; I.2.7 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. |
| title | CyanKitten: AI-Driven Markerless Motion Capture for Improved Elderly Well-Being |
| topic | Human-Computer Interaction F.2.2; I.2.7 |
| url | https://arxiv.org/abs/2503.19398 |