Summary of Process-aware Human Activity Recognition, by Jiawei Zheng et al.
Process-aware Human Activity Recognition
by Jiawei Zheng, Petros Papapanagiotou, Jacques D. Fleuriot, Jane Hillston
First submitted to arxiv on: 13 Nov 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 research paper proposes a novel approach to human activity recognition (HAR) by incorporating process information from context, which is often overlooked in traditional neural network or machine learning-based methods. The proposed method aligns probabilistic events generated by machine learning models with process models derived from contextual information, adaptively weighing these sources of information to optimize HAR accuracy. Compared to baseline models, the approach demonstrates better accuracy and Macro F1-score in experiments. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make human activity recognition (HAR) better by adding context. Right now, HAR usually uses special types of artificial intelligence called neural networks or machine learning. But these methods often don’t use all the information that’s available. The researchers came up with a new way to do HAR that takes into account what’s happening around the time and place where the activities are happening. They tested it and found out that it works better than other ways of doing HAR. |
Keywords
» Artificial intelligence » Activity recognition » F1 score » Machine learning » Neural network
