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Transformer-Driven Affective State Recognition from Wearable Physiological Data in Everyday Contexts.

Fang Li¹, Dan Zhang¹

¹Department of Psychological and Cognitive Sciences, Tsinghua University, Beijing 100084, China.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

This summary is machine-generated.

This study introduces a Transformer-based algorithm for recognizing affective states using wearable physiological data. The model shows promise for real-world emotion detection in daily life.

Keywords:

affective state recognition multi-modal data transformer model

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Area of Science:

Affective computing
Human-computer interaction
Psychiatry

Background:

Wearable physiological measurement technology is advancing rapidly.
Recognizing affective states in daily contexts has potential applications in human-computer interaction and psychiatry.
Long-term, multi-modal physiological data in everyday settings presents a challenge.

Purpose of the Study:

To introduce a Transformer-based algorithm for affective state recognition.
To exploit temporal characteristics of signals and interrelationships between different modalities.
To assess the feasibility of affective state recognition using wearable multi-modal physiological signals in everyday contexts.

Main Methods:

Utilized the DAPPER dataset with 5-day wrist-worn recordings (heart rate, skin conductance, tri-axial acceleration) from 88 subjects.
Developed a Transformer-based algorithm for affective state recognition.
Performed binary and five-class classification of affective states.

Main Results:

Achieved 71.5% average binary classification accuracy for positive/negative affective state.
Obtained 60.29% and 61.55% accuracy for five-class classification based on valence and arousal.
Demonstrated feasibility of real-time affective state recognition from wearable sensors.

Conclusions:

Affective state recognition using wearable multi-modal physiological signals is feasible in everyday contexts.
Transformer-based models can effectively utilize temporal signal characteristics and inter-modal relationships.
This approach holds potential for enhancing human-computer interaction and psychiatric applications.