Summary of Transfer Learning in Human Activity Recognition: a Survey, by Sourish Gunesh Dhekane et al.
Transfer Learning in Human Activity Recognition: A Survey
by Sourish Gunesh Dhekane, Thomas Ploetz
First submitted to arxiv on: 18 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP)
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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 In this paper, researchers investigate transfer learning methods for human activity recognition (HAR) in smart homes and wearables. They examine how deep learning-based end-to-end training can be applied to sensor-based HAR despite limited annotated data availability. The authors focus on categorizing and presenting existing works that address challenges in these application domains, providing an updated view of the state-of-the-art. Based on their analysis of 205 papers, they highlight gaps in the literature and propose a research agenda. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to use deep learning for recognizing human activities using sensors like those found in smart homes or wearables. The researchers want to figure out how to apply techniques that work well with lots of data to situations where there’s not as much data available. They’re looking at what other people have done and trying to identify patterns and gaps in the research, so they can suggest new ideas for improvement. |
Keywords
* Artificial intelligence * Activity recognition * Deep learning * Transfer learning
