Deep-seeded Clustering for Emotion Recognition from Wearable Physiological Sensors

TL;DR

This paper introduces a deep-seeded clustering method that automatically extracts and classifies physiological signals related to emotion, reducing the need for labeled data and showing strong performance across multiple datasets.

Contribution

The study presents a novel deep-seeded clustering algorithm combining autoencoders and c-means clustering for emotion recognition from wearable sensors with minimal supervision.

Findings

Achieved 80.7% accuracy on WESAD dataset

Achieved 64.2% accuracy on Stress-Predict dataset

Achieved 61.0% accuracy on CEAP360-VR dataset

Abstract

According to the circumplex model of affect, an emotional response could characterized by a level of pleasure (valence) and intensity (arousal). As it reflects on the autonomic nervous system (ANS) activity, modern wearable wristbands can record non-invasively and during our everyday lives peripheral end-points of this response. While emotion recognition from physiological signals is usually achieved using supervised machine learning algorithms that require ground truth labels for training, collecting it is cumbersome and particularly unfeasible in naturalistic settings, and extracting meaningful insights from these signals requires domain knowledge and might be prone to bias. Here, we propose and test a deep-seeded clustering algorithm that automatically extracts and classifies features from those physiological signals with minimal supervision - combining an autoencoder (AE) for unsupervised feature representation and c-means clustering for fine-grained classification. We also show that the model obtains good performance results across three different datasets frequently used in affective computing studies (accuracies of 80.7% on WESAD, 64.2% on Stress-Predict and 61.0% on CEAP360-VR).