Summary of Explainable Deep Learning Framework For Human Activity Recognition, by Yiran Huang and Yexu Zhou and Haibin Zhao and Till Riedel and Michael Beigl
Explainable Deep Learning Framework for Human Activity Recognition
by Yiran Huang, Yexu Zhou, Haibin Zhao, Till Riedel, Michael Beigl
First submitted to arxiv on: 21 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed novel, model-agnostic framework enhances the interpretability and efficacy of Human Activity Recognition (HAR) models by strategically using competitive data augmentation. This innovative approach does not rely on any particular model architecture, broadening its applicability across various HAR models. The framework provides intuitive and accessible explanations of model decisions, significantly advancing the interpretability of HAR systems without compromising performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to understand how artificial intelligence (AI) makes decisions in human activity recognition (HAR) is needed. Currently, AI can explain some things, but it’s hard to understand because HAR data is complex and abstract. Even the best methods take 10-20 seconds to generate an explanation. This paper proposes a solution that makes HAR AI more understandable without affecting its performance. |
Keywords
* Artificial intelligence * Activity recognition * Data augmentation
