OPPH: A Vision-Based Operator for Measuring Body Movements for Personal Healthcare

TL;DR

This paper introduces OPPH, a multi-stage filtering operator designed to improve vision-based human body motion estimation accuracy in healthcare, especially during motionless states and under real-world noise conditions.

Contribution

The paper presents the OPPH operator, a novel multi-stage filter that enhances existing vision-based motion estimation methods for healthcare applications.

Findings

Effectively removes real-world noise from motion data

Improves detection of motionless states during medical events

Maintains accuracy of active body movement estimation

Abstract

Vision-based motion estimation methods show promise in accurately and unobtrusively estimating human body motion for healthcare purposes. However, these methods are not specifically designed for healthcare purposes and face challenges in real-world applications. Human pose estimation methods often lack the accuracy needed for detecting fine-grained, subtle body movements, while optical flow-based methods struggle with poor lighting conditions and unseen real-world data. These issues result in human body motion estimation errors, particularly during critical medical situations where the body is motionless, such as during unconsciousness. To address these challenges and improve the accuracy of human body motion estimation for healthcare purposes, we propose the OPPH operator designed to enhance current vision-based motion estimation methods. This operator, which considers human body movement and noise properties, functions as a multi-stage filter. Results tested on two real-world and one synthetic human motion dataset demonstrate that the operator effectively removes real-world noise, significantly enhances the detection of motionless states, maintains the accuracy of estimating active body movements, and maintains long-term body movement trends. This method could be beneficial for analyzing both critical medical events and chronic medical conditions.