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Authors: Kassy Raymond and Andrew Hamilton-Wright

Affiliation: School of Computer Science, University of Guelph, Guelph, Ontario, Canada

Keyword(s): Clustering, Quality Metrics, Biosignal Analysis, Unsupervised Machine-learning, Data Analytics.

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.

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Paper citation in several formats:
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 - DATA; ISBN 978-989-758-583-8; ISSN 2184-285X, SciTePress, pages 345-352. DOI: 10.5220/0011271800003269

@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 - DATA},
year={2022},
pages={345-352},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011271800003269},
isbn={978-989-758-583-8},
issn={2184-285X},
}

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - DATA
TI - Unsupervised Electrodermal Data Analysis Comparison between Biopac and Empatica E4 Data Collection Platforms
SN - 978-989-758-583-8
IS - 2184-285X
AU - Raymond, K.
AU - Hamilton-Wright, A.
PY - 2022
SP - 345
EP - 352
DO - 10.5220/0011271800003269
PB - SciTePress

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