Summary of Metawears: a Shortcut in Wearable Systems Lifecycle with Only a Few Shots, by Alireza Amirshahi et al.
MetaWearS: A Shortcut in Wearable Systems Lifecycle with Only a Few Shots
by Alireza Amirshahi, Maedeh H.Toosi, Siamak Mohammadi, Stefano Albini, Pasquale Davide Schiavone, Giovanni Ansaloni, Amir Aminifar, David Atienza
First submitted to arxiv on: 4 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Hardware Architecture (cs.AR)
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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 proposed meta-learning method, MetaWearS, addresses challenges in wearable system lifecycle by reducing initial data collection requirements. It incorporates a prototypical updating mechanism, modifying class prototypes rather than retraining entire models. The approach is tested in two case studies: detecting epileptic seizures and atrial fibrillation. Fine-tuning with just a few samples achieves 70% and 82% AUC respectively. Compared to conventional approaches, MetaWearS performs better with up to 45% AUC improvement. Model updates consume significantly less energy, reducing consumption by 456x and 418x for respective tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MetaWearS is a new way to make wearable devices more useful. Right now, it’s hard to train models for these devices because we need a lot of labeled data from many people. Also, when we want to update the model, we need even more data. This takes up battery life and makes it hard to keep track of health issues over time. MetaWearS solves this problem by using a special kind of learning called meta-learning. It only needs a little bit of new data to make updates, which saves energy and makes it faster. The researchers tested MetaWearS on two different problems: detecting seizures in people with epilepsy and detecting abnormal heart rhythms. They found that it worked really well and was better than other methods. |
Keywords
» Artificial intelligence » Auc » Fine tuning » Meta learning
