OPPH: A Vision-Based Operator for Measuring Body Movements for Personal Healthcare
This addresses the need for reliable body movement monitoring in healthcare, particularly for critical events like unconsciousness, but it is incremental as it enhances existing methods rather than introducing a new paradigm.
The paper tackled the problem of inaccurate vision-based motion estimation for healthcare by proposing the OPPH operator, which effectively removes noise and enhances detection of motionless states while maintaining accuracy for active movements, as demonstrated on real-world and synthetic datasets.
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.