Summary of Toward Foundation Model For Multivariate Wearable Sensing Of Physiological Signals, by Yunfei Luo et al.
Toward Foundation Model for Multivariate Wearable Sensing of Physiological Signals
by Yunfei Luo, Yuliang Chen, Asif Salekin, Tauhidur Rahman
First submitted to arxiv on: 12 Dec 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 Medium Difficulty summary: The paper proposes a time-series foundation model called NormWear that can be used for wearable sensing data. It learns generalizable representations by being pretrained on a large set of physiological signals, including PPG, ECG, EEG, GSR, and IMU, from various public resources. NormWear is evaluated on 11 public wearable sensing datasets, spanning 18 applications in mental health, body state inference, biomarker estimations, and disease risk evaluations. The model achieves better performance improvement over competitive baselines in general time series foundation modeling. Additionally, the paper introduces a novel representation-alignment-match-based method that enables zero-shot inference for previously unseen wearable signal-based health applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This paper is about creating a new way to process data from wearables like smartwatches and fitness trackers. It makes it possible to use this data in new ways, such as predicting our mental and physical state or detecting diseases early on. The researchers created a model that can learn from many different types of wearable data, and then test it with lots of real-world examples. They found that their model works better than others at doing these tasks. |
Keywords
* Artificial intelligence * Alignment * Inference * Time series * Zero shot
