Sleep-deprived Fatigue Pattern Analysis using Large-Scale Selfies from Social Med

TL;DR

This paper proposes a novel, efficient method to predict individual fatigue levels from selfies by analyzing facial cues, leveraging large-scale social media data to understand fatigue distribution across demographics.

Contribution

It introduces a new computational framework for large-scale fatigue assessment using facial analysis of social media selfies, bypassing traditional equipment-intensive methods.

Findings

Fatigue varies across weekdays, age, gender, and ethnicity.

Facial cues can reliably predict fatigue levels from selfies.

Large-scale social media data enables comprehensive fatigue analysis.

Abstract

The complexities of fatigue have drawn much attention from researchers across various disciplines. Short-term fatigue may cause safety issue while driving; thus, dynamic systems were designed to track driver fatigue. Long-term fatigue could lead to chronic syndromes, and eventually affect individuals physical and psychological health. Traditional methodologies of evaluating fatigue not only require sophisticated equipment but also consume enormous time. In this paper, we attempt to develop a novel and efficient method to predict individual's fatigue rate by scrutinizing human facial cues. Our goal is to predict fatigue rate based on a selfie. To associate the fatigue rate with user behaviors, we have collected nearly 1-million timeline posts from 10,480 users on Instagram. We first detect all the faces and identify their demographics using automatic algorithms. Next, we investigate the fatigue distribution by weekday over different age, gender, and ethnic groups. This work represents a promising way to assess sleep-deprived fatigue, and our study provides a viable and efficient computational framework for user fatigue modeling in large-scale via social media.