Identification of Deep Breath While Moving Forward Based on Multiple Body Regions and Graph Signal Analysis

Abstract

This paper presents an unobtrusive solution that can automatically identify deep breath when a person is walking past the global depth camera. Existing non-contact breath assessments achieve satisfactory results under restricted conditions when human body stays relatively still. When someone moves forward, the breath signals detected by depth camera are hidden within signals of trunk displacement and deformation, and the signal length is short due to the short stay time, posing great challenges for us to establish models. To overcome these challenges, multiple region of interests (ROIs) based signal extraction and selection method is proposed to automatically obtain the signal informative to breath from depth video. Subsequently, graph signal analysis (GSA) is adopted as a spatial-temporal filter to wipe the components unrelated to breath. Finally, a classifier for identifying deep breath is established based on the selected breath-informative signal. In validation experiments, the proposed approach outperforms the comparative methods with the accuracy, precision, recall and F1 of 75.5%, 76.2%, 75.0% and 75.2%, respectively. This system can be extended to public places to provide timely and ubiquitous help for those who may have or are going through physical or mental trouble.

DFSNet: A 3D Point Cloud Segmentation Network toward Trees Detection in an Orchard Scene

“…The point cloud has increasingly been used in visual perception tasks of orchard management robotics [8], with its insensitivity to variances in lighting, shadows, and other factors. Point cloudlevel semantic segmentation helps visual perception systems realize object recognition and detection, which greatly improves the operation and production efficiency of robots [9].…”