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ZENODO
Dataset . 2024
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2024
License: CC BY
Data sources: Datacite
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Synthetic Health Sensor Data From Wearables With Stress Labels

Authors: Lange, Lucas; Wenzlitschke, Nils; Rahm, Erhard;

Synthetic Health Sensor Data From Wearables With Stress Labels

Abstract

Data stems from a adapted pipline of the models found in the paper: Generating Synthetic Health Sensor Data for Privacy-Preserving Wearable Stress DetectionPlease cite this paper as reference. Both are synthetic time-series datasets created from training GANs on the WESAD dataset.Each set features data for 10,000 subjects instead of just 15 found in the original.An important difference to the original is the reduction of labels to only: stress and non-stress.One is generated using a CGAN and the other a DGAN. Used for testing re-identification attacks in: Slice it up: Unmasking User Identities in Smartwatch Health Data

Keywords

Synthetic Data, Wearable Electronic Devices, Health, Stress Detection, WESAD, Smartwatch, Stress, Generative Adversarial Network

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average