On the Importance of Behavioral Nuances: Amplifying Non-Obvious Motor Noise Under True Empirical Considerations May Lead to Briefer Assays and Faster Classification Processes

TL;DR

This paper introduces a novel affective computing platform that leverages micropeaks in brief face videos and advanced AI methods to improve personalized analysis of affective microexpressions, especially distinguishing autistic from neurotypical individuals.

Contribution

It presents a new approach combining micropeak analysis and nonlinear dynamical systems to enhance affective data sampling and classification in short video assays.

Findings

Effective differentiation of microexpressions in brief videos.

Enhanced detection of affective nuances in autistic individuals.

Potential for shorter, more scalable affective assessments.

Abstract

There is a tradeoff between attaining statistical power with large, difficult to gather data sets, and producing highly scalable assays that register brief data samples. Often, as grand-averaging techniques a priori assume normally-distributed parameters and linear, stationary processes in biorhythmic, time series data, important information is lost, averaged out as gross data. We developed an affective computing platform that enables taking brief data samples while maintaining personalized statistical power. This is achieved by combining a new data type derived from the micropeaks present in time series data registered from brief (5-second-long) face videos with recent advances in AI-driven face-grid estimation methods. By adopting geometric and nonlinear dynamical systems approaches to analyze the kinematics, especially the speed data, the new methods capture all facial micropeaks. These include as well the nuances of different affective micro expressions. We offer new ways to differentiate dynamical and geometric patterns present in autistic individuals from those found more commonly in neurotypical development.