Inferring Low-level Mental States of Mobile Users from Plethysmogram Features by Regression Models based on Kernel Method

Toshiki Iso

Abstract

To infer user’s response when using mobile services without direct interrogation, we propose an algorithm that analyzes earlobe plethysmograms to determine low-level mental states such as ‘relax’, ‘concentration’, ‘awake’. We use subject’s responses acquired in a subjective evaluation as indicative of low-level mental states when subjects use some mobile contents. In order to draw an inference of low-level mental states based on plethysmogram features, our proposed algorithm uses a kernel-based regression model such as Gaussian Process Regression (GPR) or Support Vector Regression (SVR). Our evaluations show that features effective for inferring user’s low-level mental states can be extracted from plethysmograms by using regression and Automatic Relevance Determination (ARD); the regression performance of GPR and SVR are described.

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Paper Citation


in Harvard Style

Iso T. (2020). Inferring Low-level Mental States of Mobile Users from Plethysmogram Features by Regression Models based on Kernel Method.In Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS, ISBN 978-989-758-398-8, pages 250-257. DOI: 10.5220/0008977002500257


in Bibtex Style

@conference{biosignals20,
author={Toshiki Iso},
title={Inferring Low-level Mental States of Mobile Users from Plethysmogram Features by Regression Models based on Kernel Method},
booktitle={Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,},
year={2020},
pages={250-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008977002500257},
isbn={978-989-758-398-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 4: BIOSIGNALS,
TI - Inferring Low-level Mental States of Mobile Users from Plethysmogram Features by Regression Models based on Kernel Method
SN - 978-989-758-398-8
AU - Iso T.
PY - 2020
SP - 250
EP - 257
DO - 10.5220/0008977002500257