Summary of Enhancing Screen Time Identification in Children with a Multi-view Vision Language Model and Screen Time Tracker, by Xinlong Hou et al.
Enhancing Screen Time Identification in Children with a Multi-View Vision Language Model and Screen Time Tracker
by Xinlong Hou, Sen Shen, Xueshen Li, Xinran Gao, Ziyi Huang, Steven J. Holiday, Matthew R. Cribbet, Susan W. White, Edward Sazonov, Yu Gan
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a novel sensor informatics framework for accurately monitoring young children’s screen exposure, which is crucial for understanding phenomena linked to screen use such as childhood obesity, physical activity, and social interaction. The proposed framework utilizes egocentric images from a wearable sensor, the Screen Time Tracker (STT), and a Vision Language Model (VLM). A multi-view VLM was designed to interpret screen exposure dynamically by processing multiple views from image sequences. Experimental results on a dataset of children’s free-living activities demonstrate significant improvements over existing methods using plain vision language models and object detection models, highlighting the potential of this approach for optimizing behavioral research on screen exposure in naturalistic settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a better way to measure how much time kids spend looking at screens. Right now, most studies rely on parents or researchers counting how long kids look at their phones or tablets, which isn’t very accurate. To fix this, the authors developed a new system that uses pictures taken by a special device worn by the child. These pictures are then analyzed using a special kind of computer program called a Vision Language Model. The model is trained to recognize different views and scenes from the pictures, and it can figure out how much time the child spends looking at screens. The authors tested their system with real data from kids doing everyday activities and found that it was way more accurate than other methods. This could be really helpful for researchers who want to understand how screen use affects kids’ lives. |
Keywords
» Artificial intelligence » Language model » Object detection
