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This article describes a new approach for quantitative evaluation of respiration in the pediatric intensive care unit (PICU). Video sequences of thorax movements are recorded by two depth cameras to cover the 3-D surface of the torso and its lateral sides. The breathing activity implies a frame-by-frame surface deformation, which can be described by the volume variation of reconstructed surfaces between the consecutive video frames. A quantitative evaluation of the breathing pattern is then performed through a subtraction technique, thereby detecting the volume variation between the subsequent frames. A high-fidelity simulation was performed in a realistic environment designed for critically ill children. The simulation was then followed by a real-world evaluation, involving two newborn babies (one female and one male) who required ventilator support. The breathing signal patterns resulting from our approach were compared to those measured by mechanical ventilation in terms of their waveforms, evaluating the most significant dynamic parameters: tidal volume, respiratory rate, and minute ventilation. Our experimental study showed a significant agreement between the proposed 3-D imaging system and the gold standard method in estimating respiratory waveforms and parameters. This article presents the following innovations. First, we suggest a 3-D imaging system specifically designed for PICUs based on a contactless design. Second, we propose an efficient positioning mechanism for the cameras, offering a very high spatial coverage of the thoracoabdominal zone and considering the PICU constraints. Finally, we propose an objective vision-based method to quantitatively measure respiration for spontaneous breathing patients in PICUs. [ABSTRACT FROM AUTHOR]

Suicide by hanging is a sentinel event and a major cause of death in prisons, with an increasing frequency over recent years. The rapid detection of suicidal behavior can reduce the mortality rate and increase the odds of survival for the suicide victim. Significant efforts have been made to develop technologies for preventing hanging attempts, but most of them use cumbersome devices, or they are mainly depending on human attention and intervention. In this paper, we propose a vision-based method to automatically detect suicide by hanging. Our intelligent video surveillance system operates using depth streams provided by an RGB-D camera, regardless of illumination conditions. The proposed algorithm is based on the exploitation of the body joints’positions to model suicidal behavior. Both dynamic and static pose characteristics are calculated in order to efficiently capture the body joints’movement and model suicidal behavior. Results from the experiments on realistic video sequences, show that our system achieves a high accuracy in detecting suicide attempts, while meeting real-time requirements. [ABSTRACT FROM AUTHOR]