Open access
Date
2024Type
- Conference Paper
ETH Bibliography
yes
Altmetrics
Abstract
In this paper, we investigate the application of large language models (LLMs) to zero-shot and few-shot prediction and classification of multimodal wearable sensor data. Using data from the large-scale HomeKit2020 dataset, we explore health tasks including cardiac activity monitoring, metabolic health prediction, and sleep detection. We demonstrate that LLMs perform feature extraction, prediction, and classification with comparable or higher performance than classical
machine learning approaches even in the zero-shot scenario. Our findings show
promising results in using LLMs for wearable data analysis and interpretation. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000661839Publication status
publishedEvent
Subject
Wearable Sensing; Large language models; Machine LearningOrganisational unit
02219 - ETH AI Center / ETH AI Center
Notes
Tiny Paper ICLR 2024More
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ETH Bibliography
yes
Altmetrics