Summary of Exploring the Impact Of Synthetic Data on Human Gesture Recognition Tasks Using Gans, by George Kontogiannis et al.
Exploring the Impact of Synthetic Data on Human Gesture Recognition Tasks Using GANs
by George Kontogiannis, Pantelis Tzamalis, Sotiris Nikoletseas
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 explores the application of Deep Generative Models (DGMs) and Generative Adversarial Networks (GANs) to Human Activity Recognition (HAR) in healthcare settings. Specifically, it focuses on Human Gesture Recognition (HGR), a subset of HAR that uses time series data to recognize allergic gestures. The authors aim to address the challenges of data scarcity and quality issues in HAR by generating synthetic data using GANs. They investigate how these models can enhance classification metrics scores and improve healthcare applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to understand what people are doing, like recognizing hand movements or gestures. This is called Human Activity Recognition (HAR). In hospitals, doctors need to recognize certain hand gestures that indicate allergic reactions. But it’s hard because there isn’t much data available and the data we do have might not be very good quality. Researchers are trying to solve this problem by creating fake data using special computer models. This paper looks at how these models can help us better understand people’s gestures and make hospitals a safer place. |
Keywords
» Artificial intelligence » Activity recognition » Classification » Gesture recognition » Synthetic data » Time series
