Investigation into Recognizing Context Over Time using Physiological Signals

In this paper, we investigate recognizing context over time using physiological signals. Using the CASE dataset we evaluate both unimodal and multimodal approaches to physiological-based context recognition, over time. For recognition, we evaluate a random forest, as well as state-of-the-art neural...

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Bibliographic Details
Published inInternational Conference on Affective Computing and Intelligent Interaction and workshops pp. 1 - 8
Main Authors Hinduja, Saurabh, Kaur, Gurmeet, Canavan, Shaun
Format Conference Proceeding
LanguageEnglish
Published IEEE 28.09.2021
Subjects
affect
Computer aided software engineering
context
Correlation
Electrocardiography
Emotion
Emotion recognition
Neural networks
physiological signals
Stroke (medical condition)
Training
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ISSN2156-8111
DOI10.1109/ACII52823.2021.9597422

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Summary:In this paper, we investigate recognizing context over time using physiological signals. Using the CASE dataset we evaluate both unimodal and multimodal approaches to physiological-based context recognition, over time. For recognition, we evaluate a random forest, as well as state-of-the-art neural network. These classifiers are evaluated using accuracy, Kappa, and F1-Macro metrics. Our results suggest that the fusion of EMG signals is more accurate, at recognizing context over time, compared to the fusion of non-EMG physiological signals. Although the fusion of non-EMG has a comparatively higher accuracy, ECG data results in the highest unimodal accuracy. Considering this, we analyze how the signals are correlated, including when the are fused (i.e. multimodal). We also perform a cross-gender analysis (e.g. training on male data and testing on female data) suggesting some generalizability across gender.
ISSN:2156-8111
DOI:10.1109/ACII52823.2021.9597422