"Are you okay, honey?": Recognizing Emotions among Couples Managing Diabetes in Daily Life using Multimodal Real-World Smartwatch Data

TL;DR

This study develops machine learning models to recognize emotional states of couples managing diabetes using multimodal smartwatch data collected in real-world settings, aiming to improve emotional well-being monitoring.

Contribution

It introduces a novel approach using multimodal smartwatch data from real-life interactions to recognize emotions in couples managing chronic illness, surpassing prior lab-based studies.

Findings

Best models achieved 63.8% accuracy for arousal and 78.1% for valence.

Collected 85 hours of real-world multimodal data from 26 partners.

Demonstrated feasibility of emotion recognition in daily life using wearable sensors.

Abstract

Couples generally manage chronic diseases together and the management takes an emotional toll on both patients and their romantic partners. Consequently, recognizing the emotions of each partner in daily life could provide an insight into their emotional well-being in chronic disease management. Currently, the process of assessing each partner's emotions is manual, time-intensive, and costly. Despite the existence of works on emotion recognition among couples, none of these works have used data collected from couples' interactions in daily life. In this work, we collected 85 hours (1,021 5-minute samples) of real-world multimodal smartwatch sensor data (speech, heart rate, accelerometer, and gyroscope) and self-reported emotion data (n=612) from 26 partners (13 couples) managing diabetes mellitus type 2 in daily life. We extracted physiological, movement, acoustic, and linguistic features, and trained machine learning models (support vector machine and random forest) to recognize each partner's self-reported emotions (valence and arousal). Our results from the best models (balanced accuracies of 63.8% and 78.1% for arousal and valence respectively) are better than chance and our prior work that also used data from German-speaking, Swiss-based couples, albeit, in the lab. This work contributes toward building automated emotion recognition systems that would eventually enable partners to monitor their emotions in daily life and enable the delivery of interventions to improve their emotional well-being.