ABSTRACT
Background: Heart failure remains a considerable burden to healthcare in Asia. Early intervention, mainly using echocardiography, to assess cardiac function is crucial. However, due to limited resources and time, the procedure has become more challenging during the COVID-19 pandemic. On the other hand, studies have shown that artificial intelligence (AI) is highly potential in complementing the work of clinicians to diagnose heart failure accurately and rapidly. Methods: We systematically searched Europe PMC, ProQuest, Science Direct, PubMed, and IEEE following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and our inclusion and exclusion criteria. The 14 selected works of literature were then assessed for their quality and risk of bias using the QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies). Results: A total of 2105 studies were retrieved, and 14 were included in the analysis. Five studies posed risks of bias. Nearly all studies included datasets in the form of 3D (three dimensional) or 2D (two dimensional) images, along with apical four-chamber (A4C) and apical two-chamber (A2C) being the most common echocardiography views used. The machine learning algorithm for each study differs, with the convolutional neural network as the most common method used. The accuracy varies from 57% to 99.3%. Conclusions: To conclude, current evidence suggests that the application of AI leads to a better and faster diagnosis of left heart failure through echocardiography. However, the presence of clinicians is still irreplaceable during diagnostic processes and overall clinical care;thus, AI only serves as complementary assistance for clinicians.
ABSTRACT
The use of masks due to the Covid-19 pandemic reduces the accuracy of facial recognition systems applied to camera-based security systems. The use of the mask by the people covers most of the facial featureswhich is located from middle to bottom area. In addition, the area which are still visible are the upper face which are eyes and forehead. This paper proposes a masked face recognition using a combination of RetinaFace as a face detector and FaceNet as a face recognizer. The MFR2 dataset with 53 identities was used to train and test this method. The test data in this study are only images of masked faces. Cosine Distance was implemented to measure the face similarity. Based on the experiment results, the proposed method obtained 98.2% of detection accuracy. The proposed method provided 78% accurate performance with 3.63 s for processing a single frame in terms of face recognition. The performance indicates that our system can potentially be applied in security systems with many different identities. © 2021 IEEE.
ABSTRACT
A surveillance system is still the most exciting and practical security system to prevent crime effectively. Surveillance systems run on edge devices such as the low-cost Raspberry mobile camera with the Internet of Things (IoT). The primary purpose of this system is to recognize the identity of the face caught by the camera. However, it raises the challenge of unstructured image/video where the video contains low quality, blur, and variations of human poses. Moreover, the challenge is increasing because people used to wear a mask during the Covid -19 pandemic. Therefore, we proposed developing an all-in-one surveillance system with face detection, recognition, and face tracking capabilities. The surveillance system integrated three modules: Multi-Task Cascaded Convolutional Network (MTCNN) face detector, VGGFace2 face recognition, and Discriminative Single-Shot Segmentation (D3S) tracker. We train new face mask data for face recognition and tracking. This system utilizes the Raspberry Pi camera and processes the frame on the cloud as a mobile sensor approach. The proposed method was successfully implemented and got competitive detection, recognition, and tracking results under an unconstrained surveillance camera. © 2021. All Rights Reserved.
ABSTRACT
The Novel Coronavirus, termed as COVID-19 outbreak, is faced by almost all countries in the world. It spread through communal interaction between people, especially in densely populated areas. An effort to prevent Covid-19 transmission is social distancing regulation. However, this policy is not obeyed by the public, so the government needs to supervise the movement and people's interaction. The government needs a crowd surveillance system that can detect people's presence, identify the crowd, and give social distancing warnings. Therefore, we propose a drone that has the ability of localization, navigation, people detection, crowd identifier, and social distancing warning. We utilize YOLO-v3 to detect people and define adaptive social distancing detector. In this paper, we implemented a road segmentation on the IRIS PX4 drone in the Robot Operating System and Gazebo simulation. The proposed system also successfully demonstrated people and crowd detection with varying degrees of the crowd. The system obtained crowd detection accuracy is around 90% and expected to be readily implemented on real hardware drones and tested in real environments. © 2020 IEEE.