Emotion Recognition from Physiological Signals Collected with a Wrist Device and Emotional Recall

Enni Mattern, Roxanne R Jackson, Roya Doshmanziari, Marieke Dewitte, Damiano Varagnolo*, Steffi Knorn

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Implementing affective engineering in real-life applications requires the ability to effectively recognize emotions using physiological measurements. Despite being a widely researched topic, there seems to be a lack of systems that translate results from data collected in a laboratory setting to higher technology readiness levels. In this paper, we delve into the feasibility of emotion recognition beyond controlled laboratory environments. For this reason, we create a minimally-invasive experimental setup by combining emotional recall via autobiographical emotion memory tasks with a user-friendly Empatica wristband measuring blood volume pressure, electrodermal activity, skin temperature, and acceleration. We employ standard practices of feature-based supervised learning and specifically use support vector machines to explore subject dependency through various segmentation methods. We collected data from 45 participants. After preprocessing, using a data set of 134 segments from 40 participants, the accuracy of the classifier after 10-fold cross-validation was barely better than random guessing (36% for four emotions). However, when extracting multiple segments from each emotion task per participant using 10-fold cross-validation (i.e., including subject-dependent data in the training set), the classification rate increased to up to 75% for four emotions but was still as low as 32% for leave-one-subject-out cross-validation (i.e., subject-independent training). We conclude that highly subject-dependent issues might pose emotion recognition.
Original languageEnglish
Article number1308
Number of pages17
JournalBioengineering
Volume10
Issue number11
DOIs
Publication statusPublished - 11 Nov 2023

Keywords

  • emotion recognition
  • machine learning
  • medical wristband
  • physiological signals
  • support vector machines

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