Summary of Beyond Confusion: a Fine-grained Dialectical Examination Of Human Activity Recognition Benchmark Datasets, by Daniel Geissler et al.
Beyond Confusion: A Fine-grained Dialectical Examination of Human Activity Recognition Benchmark Datasets
by Daniel Geissler, Dominique Nshimyimana, Vitor Fortes Rey, Sungho Suh, Bo Zhou, Paul Lukowicz
First submitted to arxiv on: 12 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 The paper explores machine learning (ML) algorithms for human activity recognition (HAR), highlighting limitations in current approaches. Despite recent models like transformers showing limited success on HAR datasets, their counterparts have achieved near 100% accuracy on similar tasks. This raises questions about the capabilities of current methods. The authors conduct a fine-grained inspection of six popular HAR benchmark datasets, identifying issues with ambiguous annotations, recording irregularities, and misaligned transition periods. They contribute to the field by quantifying and characterizing annotated data ambiguities, providing a trinary categorization mask for dataset patching, and suggesting improvements for future data collections. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how well machine learning algorithms can recognize human activities, like walking or running. Right now, these algorithms are not doing as well as they could because of some problems with the way the data is collected. The authors looked at six different datasets and found that some parts of the data are really hard to understand. They think this might be because of mistakes in how the data was labeled or recorded. To help fix this, they came up with a new way to look at the data and identified three types of problems: ambiguous labels, recording errors, and mismatched transitions. They hope their findings will help improve how we collect data for human activity recognition. |
Keywords
» Artificial intelligence » Activity recognition » Machine learning » Mask
