Getting Really Wild: Challenges and Opportunities of Real-World Multimodal Affect Detection

Abstract

Affect detection in the "real" wild - where people go about their daily routines in their homes and workplaces - is arguably a different problem than affect detection in the lab or in the "quasi" wild (e.g., YouTube videos). How will our affect detection systems hold up when put to the test in the real wild? Some in the Affective Computing community had an opportunity to address this question as part of the MOSAIC (Multimodal Objective Sensing to Assess Individuals with Context [1]) program which ran from 2017 to 2020. Results were sobering, but informative. I'll discuss those efforts with an emphasis on performance achieved, insights gleaned, challenges faced, and lessons learned.