AnnoSense: A Framework for Physiological Emotion Data Collection in Everyday Settings for AI

TL;DR

AnnoSense is a comprehensive framework designed to facilitate the collection of physiological emotion data in everyday settings, addressing challenges in data quality and annotation for improving AI emotion recognition.

Contribution

The paper introduces AnnoSense, a novel framework developed from stakeholder insights to improve emotion data collection and annotation in real-world environments for AI applications.

Findings

Framework evaluated positively by emotion AI experts

Stakeholder insights informed framework design

Potential to enhance real-world emotion data collection

Abstract

Emotional and mental well-being are vital components of quality of life, and with the rise of smart devices like smartphones, wearables, and artificial intelligence (AI), new opportunities for monitoring emotions in everyday settings have emerged. However, for AI algorithms to be effective, they require high-quality data and accurate annotations. As the focus shifts towards collecting emotion data in real-world environments to capture more authentic emotional experiences, the process of gathering emotion annotations has become increasingly complex. This work explores the challenges of everyday emotion data collection from the perspectives of key stakeholders. We collected 75 survey responses, performed 32 interviews with the public, and 3 focus group discussions (FGDs) with 12 mental health professionals. The insights gained from a total of 119 stakeholders informed the development of our framework, AnnoSense, designed to support everyday emotion data collection for AI. This framework was then evaluated by 25 emotion AI experts for its clarity, usefulness, and adaptability. Lastly, we discuss the potential next steps and implications of AnnoSense for future research in emotion AI, highlighting its potential to enhance the collection and analysis of emotion data in real-world contexts.