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. 2024 May 10:3:e52171.
doi: 10.2196/52171.

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

Joe Li  1 , Peter Washington  1
Affiliations

A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study

Joe Li et al. JMIR AI. .

Abstract

Background: There are a wide range of potential adverse health effects, ranging from headaches to cardiovascular disease, associated with long-term negative emotions and chronic stress. Because many indicators of stress are imperceptible to observers, the early detection of stress remains a pressing medical need, as it can enable early intervention. Physiological signals offer a noninvasive method for monitoring affective states and are recorded by a growing number of commercially available wearables.

Objective: We aim to study the differences between personalized and generalized machine learning models for 3-class emotion classification (neutral, stress, and amusement) using wearable biosignal data.

Methods: We developed a neural network for the 3-class emotion classification problem using data from the Wearable Stress and Affect Detection (WESAD) data set, a multimodal data set with physiological signals from 15 participants. We compared the results between a participant-exclusive generalized, a participant-inclusive generalized, and a personalized deep learning model.

Results: For the 3-class classification problem, our personalized model achieved an average accuracy of 95.06% and an F1-score of 91.71%; our participant-inclusive generalized model achieved an average accuracy of 66.95% and an F1-score of 42.50%; and our participant-exclusive generalized model achieved an average accuracy of 67.65% and an F1-score of 43.05%.

Conclusions: Our results emphasize the need for increased research in personalized emotion recognition models given that they outperform generalized models in certain contexts. We also demonstrate that personalized machine learning models for emotion classification are viable and can achieve high performance.

Keywords: affect detection; affective computing; deep learning; digital health; emotion recognition; machine learning; mental health; personalization; stress detection; wearable technology.

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Conflict of interest statement

Conflicts of Interest: None declared.

Figures

Figure 1
Figure 1
Overview of our model architecture for the 3-class emotion classification task. FNN: feedforward neural network; SiLU: sigmoid linear unit.
Figure 2
Figure 2
A comparison of different generalized and personalized approaches to the 3-class emotion classification task. The participant-exclusive generalized model mimics generalized approaches used in other papers. The participant-exclusive generalized model shown in the figure differs from what we use in this paper.
Figure 3
Figure 3
Deviations of mean and SD for participants 1 and 2 for neutral class modalities.
Figure 4
Figure 4
Deviations of mean and SD for subjects 1 and 2 for stress class modalities. EMG: electromyogram.
Figure 5
Figure 5
Deviations of mean and SD for subjects 1 and 2 for amusement class modalities.
See this image and copyright information in PMC

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