# Detecting Clues for Skill Levels and Machine Operation Difficulty from Egocentric Vision

## TL;DR

This paper analyzes egocentric vision data to identify behavioral cues that distinguish skill levels and operational difficulty in machine tasks, revealing key visual and motion features linked to expertise and challenge.

## Contribution

It introduces a method to analyze head, hand, and hotspot relations from egocentric videos to assess skill and difficulty in machine operation tasks.

## Key findings

- Gazing behavior decreases with skill improvement
- Hand-approaching duration correlates with operational difficulty
- Attention movement frequency predicts task complexity

## Abstract

With respect to machine operation tasks, the experiences from different skill level operators, especially novices, can provide worthy understanding about the manner in which they perceive the operational environment and formulate knowledge to deal with various operation situations. In this study, we describe the operator's behaviors by utilizing the relations among their head, hand, and operation location (hotspot) during the operation. A total of 40 experiences associated with a sewing machine operation task performed by amateur operators was recorded via a head-mounted RGB-D camera. We examined important features of operational behaviors in different skill level operators and confirmed their correlation to the difficulties of the operation steps. The result shows that the pure-gazing behavior is significantly reduced when the operator's skill improved. Moreover, the hand-approaching duration and the frequency of attention movement before operation are strongly correlated to the operational difficulty in such machine operating environments.