Summary of Representation Learning Of Daily Movement Data Using Text Encoders, by Alexander Capstick et al.
Representation Learning of Daily Movement Data Using Text Encoders
by Alexander Capstick, Tianyu Cui, Yu Chen, Payam Barnaghi
First submitted to arxiv on: 7 May 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 A new representation learning method is proposed for remote healthcare monitoring applications, specifically for people living with Dementia. The approach converts activity recordings into text strings that can be encoded using a fine-tuned language model. This enables clustering and vector searching across participants and days, allowing for the identification of activity deviations to inform personalized care delivery. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to learn better representations of time-series data from people with Dementia at home. They use a special way to turn activities into text that can be understood by language models. This helps group similar activities together and find unusual ones, which is useful for providing personalized healthcare. |
Keywords
» Artificial intelligence » Clustering » Language model » Representation learning » Time series
