OPPH：一种用于个人医疗保健的基于视觉的身体运动测量算子
OPPH: A Vision-Based Operator for Measuring Body Movements for Personal Healthcare
AI总结：
针对现有视觉运动估计方法在医疗保健场景中存在的细粒度运动检测精度不足、抗噪性差等问题，提出结合人体运动与噪声特性的多级OPPH算子，可有效去噪、提升静止状态检测精度，适用于危重及慢性医疗场景分析。
摘要：
基于视觉的运动估计方法在以医疗保健为目的、准确且无干扰地估计人体运动方面展现出应用前景。然而，这些方法并非专门为医疗保健场景设计，在实际应用中面临诸多挑战。人体姿态估计方法通常缺乏检测细粒度、细微身体运动所需的精度，而基于光流的方法在光照条件差和未见过的真实世界数据上表现不佳。这些问题会导致人体运动估计出现误差，尤其是在身体静止的关键医疗场景中，例如昏迷状态下。为应对这些挑战、提升医疗保健场景下人体运动估计的精度，我们提出了OPPH算子，用于增强现有基于视觉的运动估计方法。该算子结合人体运动和噪声特性，是一种多级滤波器。在两个真实世界人体运动数据集和一个合成人体运动数据集上的测试结果表明，该算子可有效去除真实世界噪声，显著提升静止状态的检测效果，同时保持主动身体运动的估计精度，并能保留长期身体运动趋势。该方法可用于分析危重医疗事件和慢性疾病。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
