A Survey of Sensors in Healthcare Workflow Monitoring - Stoffel Antunes, Rodolfo; Adams Seewald, Lucas; Facco Rodrigues, Vinicius; Facco Rodrigues, Vinicius; Andre da Costa, Cristiano; da Silveira Junior, Luiz Gonzaga; da Rosa Righi, Rodrigo; Maier, Andreas; Eskofier, Björn; Ollenschläger, Malte; Naderi Boldaji, Farzad; Fahrig, Rebecca; et al. (ACM Computing Surveys, 2018)
Keywords: Real-time location systems; Workflow monitoring; Computer vision; Healthcare

HealthStack: Providing an IoT Middleware for Malleable QoS Service Stacking for Hospital 4.0 Operating Rooms - Facco Rodrigues, Vinicius; Righi, Rodrigo R.; Andre da Costa, Cristiano; Stoffel Antunes, Rodolfo; Bazo, Rodrigo; Reis, Eduardo S.; Seewald, Lucas A.; S., Luis G.; Eskofier, Björn (IEEE Internet of Things, 2022)
Healthcare 4.0 is a new concept that originates from the evolution of hospitals due to technological advances in medical activities. Nowadays, more and more doctors and healthcare administrators require real-time data analysis obtained from sensors and surgery monitoring. Using real-time information could make the difference between death and life in such settings. Therefore, quality of service (QoS) is essential in this context because, without it, the results of the applications become unreliable. Given the background, this article proposes HealthStack, a sensor middleware model for operating room facilities. HealthStack aims at improving delay and jitter from applications and, at the same time, reducing resource consumption. Our scientific contributions are twofold: a middleware for operating rooms with automatic QoS support for real-time data transmission; and a QoS strategy based on artificial neurons to select middleware components with critical performance. We developed a prototype that uses depth cameras and ultra-wideband (UWB) real-time location systems (RTLS) to monitor workflow during surgery. The evaluation demonstrates that the strategy improves the average jitter experienced by application by 92.3%. The results also reveal a reduction of network and CPU consumption by up to 61.8% and 8.3%.
CRIS-Team Scopus Importer:2022-04-01
Keywords: Biomedical monitoring; distributed systems; healthcare.; Internet of Things; Middleware; Monitoring; Quality of service; Real-time systems; Sensors; sensors; Surgery

Toward analyzing mutual interference on infrared-enabled depth cameras - Adams Seewald, Lucas; Facco Rodrigues, Vinicius; Ollenschläger, Malte; Stoffel Antunes, Rodolfo; Andre da Costa, Cristiano; da Rosa Righi, Rodrigo; da Silveira Junior, Luiz Gonzaga; Maier, Andreas; Eskofier, Björn; Fahrig, Rebecca (Computer Vision and Image Understanding, 2019)
Camera setups with multiple devices are a key aspect of ambient monitoring applications. These types of setups can result in data corruption when applied to recent RGB-D camera models because of mutual interference of the infrared light emitters employed by such devices. Consequently, the behavior of such interference must be appropriately evaluated to provide data that will allow monitoring systems to handle possible errors introduced into the data captured by depth sensors. However, multi-device setups have been explored in few studies in the current literature, especially in terms of the detailed measurements of the interference’s effect during long-term usage of RGB-D cameras. In this context, a methodology to evaluate the effect of mutual interference on the accuracy and precision of measured depth values is proposedin this article. The results of a series of experiments with different setups based on multiple depth cameras are explored. These setups include three devices that were widely used in studies in the computer vision literature related to depth imaging: the Microsoft Kinect v2 and two Intel RealSense models: R200 and D415. The experimental results indicate that the Kinect v2 yields considerably more stable depth readings than the RealSense R200 in single-camera scenarios, even considering the influence of the warm-up time that is characteristic of time-of-flight devices such as the Kinect v2. In multi-device setups, the Kinect v2 displays periodic peaks of mutual interference that increase in intensity depending on the distance between the cameras, with short-range setups yielding higher interference peaks. Further, the addition of more devices can potentially increase the duration of some interference peaks, albeit their intensity is not greatly affected. In long-range setups, the measured interference is small considering the experiments’ length, with the proportion of bad pixels among all captured frames ranging from 3.74% to 3.97% in a setup comprising three depth cameras. In turn, multi-device setups comprising the RealSense models are not affected by prejudicial interference peaks. In long-range setups, the instability of the R200 leads to its results being less accurate and precise than those of the Kinect v2 under mutual interference. However, in close range multi-device setups, the high interference peaks observed with the Kinect v2 render the RealSense models a more stable alternative.
Keywords: Computer vision; Interference; Multi-camera; Depth camera; Evaluation