Unsupervised Electrodermal Data Analysis Comparison between Biopac and Empatica E4 Data Collection Platforms

Kassy Raymond, Andrew Hamilton-Wright

2022

Abstract

Unsupervised learning algorithms are valuable for exploring a variety of data domains. In this paper we compare the efficacy of the k-means and DBSCAN algorithms in the context of discerning structure in electrodermal data obtained using two different collection modalities for simultaneously collected data: the “gold standard” Biopac data platform and the wearable Empatica E4. Insights into the structure of the data from each system are provided, as is an analysis of the performance of each clustering algorithm at identifying interesting structure within the data.

Paper Citation

in Harvard Style

Raymond K. and Hamilton-Wright A. (2022). Unsupervised Electrodermal Data Analysis Comparison between Biopac and Empatica E4 Data Collection Platforms. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 345-352. DOI: 10.5220/0011271800003269

in Bibtex Style

@conference{data22,
author={Kassy Raymond and Andrew Hamilton-Wright},
title={Unsupervised Electrodermal Data Analysis Comparison between Biopac and Empatica E4 Data Collection Platforms},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={345-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011271800003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Unsupervised Electrodermal Data Analysis Comparison between Biopac and Empatica E4 Data Collection Platforms
SN - 978-989-758-583-8
AU - Raymond K.
AU - Hamilton-Wright A.
PY - 2022
SP - 345
EP - 352
DO - 10.5220/0011271800003269