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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/17025

Title: Human Emotional State Estimation Evaluation using Heart Rate Variability and Activity Data
Authors: Setiono, Felix Yustian
Elibol, Armagan
Chong, Nak Young
Issue Date: 2020-11
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2020 Fourth IEEE International Conference on Robotic Computing (IRC)
Start page: 178
End page: 182
DOI: 10.1109/IRC.2020.00035
Abstract: Human-Robot Interaction (HRI) is one of the most rapidly emerging fields in robotic applications over the years. One direction of the improvements in the HRI field is by adding the capability of emotional understanding as a fundamental part of human-human interaction necessities. Human emotion understanding has been studied through the well-known Heart Rate Variability (HRV) analysis recently. In this paper, two different methods of classification are proposed to find the relations between activity, heart rate, and emotional states. Two individual k-Nearest Neighborhood (kNN)-based classifications used in the first method and implemented for each dataset of pre- processed accelerometer data and HRV data where both aim to estimate the user's emotion and activity data at the same time. The features of the frequency domain-based HRV data and the user's activity data are combined into a new dataset and two different classifiers of Multilayer Perceptron (MLP) and Support Vector Machines (SVM) were used in the experimental evaluations. Performance comparisons are presented to show the efficiency. Results from both methods are analyzed and reported in this paper.
Rights: This is the author's version of the work. Copyright (C) 2020 IEEE. 2020 Fourth IEEE International Conference on Robotic Computing (IRC), 2020, pp.178-182. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/17025
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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