Exploring the Impact of Hand Pose and Shadow on Hand-washing Action Recognition
Shengtai Ju, Amy R. Reibman

TL;DR
This study examines how hand pose and shadow variations affect handwashing action recognition performance, revealing that shadows and pose changes significantly degrade accuracy, and proposes mitigation via targeted additional training data.
Contribution
The paper introduces a synthetic data generation method to analyze environmental impacts on handwashing recognition and proposes a mitigation strategy using moderate pose variations.
Findings
Shadows cause earlier classifier breakdown points.
Pose variations drastically reduce accuracy.
Adding moderate pose data improves robustness.
Abstract
In the real world, camera-based application systems can face many challenges, including environmental factors and distribution shift. In this paper, we investigate how pose and shadow impact a classifier's performance, using the specific application of handwashing action recognition. To accomplish this, we generate synthetic data with desired variations to introduce controlled distribution shift. Using our synthetic dataset, we define a classifier's breakdown points to be where the system's performance starts to degrade sharply, and we show these are heavily impacted by pose and shadow conditions. In particular, heavier and larger shadows create earlier breakdown points. Also, it is intriguing to observe model accuracy drop to almost zero with bigger changes in pose. Moreover, we propose a simple mitigation strategy for pose-induced breakdown points by utilizing additional training data…
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Taxonomy
TopicsHand Gesture Recognition Systems · Human Pose and Action Recognition · Gait Recognition and Analysis
