Correction: Mackala et al. Evaluation of the Pre-Planned and Non-Planed Agility Performance: Comparison between Individual and Team Sports. Int. J. Environ. Res. Public Health 2020,17, 975.

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).Peer reviewe

A Survey on Computer Vision based Human Analysis in the COVID-19 Era

The emergence of COVID-19 has had a global and profound impact, not only on society as a whole, but also on the lives of individuals. Various prevention measures were introduced around the world to limit the transmission of the disease, including face masks, mandates for social distancing and regular disinfection in public spaces, and the use of screening applications. These developments also triggered the need for novel and improved computer vision techniques capable of (i) providing support to the prevention measures through an automated analysis of visual data, on the one hand, and (ii) facilitating normal operation of existing vision-based services, such as biometric authentication schemes, on the other. Especially important here, are computer vision techniques that focus on the analysis of people and faces in visual data and have been affected the most by the partial occlusions introduced by the mandates for facial masks. Such computer vision based human analysis techniques include face and face-mask detection approaches, face recognition techniques, crowd counting solutions, age and expression estimation procedures, models for detecting face-hand interactions and many others, and have seen considerable attention over recent years. The goal of this survey is to provide an introduction to the problems induced by COVID-19 into such research and to present a comprehensive review of the work done in the computer vision based human analysis field. Particular attention is paid to the impact of facial masks on the performance of various methods and recent solutions to mitigate this problem. Additionally, a detailed review of existing datasets useful for the development and evaluation of methods for COVID-19 related applications is also provided. Finally, to help advance the field further, a discussion on the main open challenges and future research direction is given.Comment: Submitted to Image and Vision Computing, 44 pages, 7 figure

INTEROPERABILITY AND MAMAGEMENT OF E-HEALTH DATA IN THE EUROPEAN UNION

V Evropski uniji obstaja več kot 800 različnih IT podjetij, ki se ukvarjajo z elektronskimi zdravstvenimi zapisi, kjer je med rešitvami treba omogočiti interoperabilnost, ki bo pripomogla k bolj optimalni zdravstveni oskrbi in k večji učinkovitosti ter uspešnosti. Interoperabilnost e-zdravja omogoča izmenjavo podatkov in informacij med različnimi informacijskimi sistemi, kot so: sistemi za elektronski zdravstveni zapis EZZ, sistemi za elektronski medicinski zapis, sistemi za osebni zdravstveni zapis, sistemi za klinične študije, zdravstvene naprave, zavarovalniški sistemi in sistemi na državnem, evropskem ter svetovnem nivoju. Bistveno je, da so pravi podatki in informacije na voljo na vseh točkah zdravstvene nege in na vseh mestih kot podpora pri odločanju na razumljiv, varen, zanesljiv in zaseben način. Uporaba informacijskih tehnologij v zdravstvu zaostaja glede na druga gospodarska področja, zato je treba temu področju nameniti več pozornosti. Za optimalno izmenjavo zdravstvenih podatkov v družbi na nacionalni in mednarodni ravni je treba zagotoviti tehnično, semantično, organizacijsko in pravno interoperabilnost. Tem vrstam interoperabilnosti mora biti prilagojena večina informacijskih rešitev v zdravstvu. Najprej se naloga osredotoča na tehnične in semantične mednarodne in lokalne standarde e-zdravja, ki so predpogoj za doseganje širše interoperabilnosti in narekujejo gradnjo zdravstvenih informacijskih sistemov. Nadalje je te standarde treba vključiti v arhitekturne zasnove rešitev, pri čemer sta v pomoč OpenEHR in IHE (angl. IHE – Integrating the Healthcare Enterprise). Že obstoječe rešitve je treba prilagajati novim standardom in priporočilom ter upoštevati sodobne računalniške smernice. Pri izmenjavi podatkov je najpomembnejša odločitev glede uporabe SOAP oziroma REST tehnologije, ki je odvisna od potreb in omejene pasovne širine. V srednjem delu je opisano stanje na področju interoperabilnosti v EU in Sloveniji, kjer so smernice sicer dobro zasnovane, vendar se izvedbe vsaj v nekaterih državah odvijajo prepočasi. V nalogi je podrobneje analiziran novo nastajajoči standard FHIR (angl. FHIR - Fast Healthcare Interoperability Resources), ki bi lahko nadomestil vse trenutno obstoječe standarde na področju izmenjave podatkov v zdravstvu. Na rešitvi fMedic, ki je plod razvoja našega podjetja, so opisane smernice, kako rešitev prilagoditi novemu standardu FHIR. Na koncu so podane smernice in in procedure postopkov, ki jih je treba izvajati pri gradnji interoperabilnih rešitev e-zdravja. Glavni cilj naloge je pomagati poiskati hiter način prilagoditve obstoječih rešitev e-zdravja za potebe interoperabilnosti.In the European Union there are more than 800 different IT companies dealing with electronic medical records, where interoperability has to be established among some of them. Interoperability will contribute to a more optimal heath care, greater efficiency and effectiveness of health sector. eHealth interoperability enables the exchange of data and information among diverse information systems such as electronic health record, electronic medical record, personal health record, clinical studies, healthcare devices, insurance systems and health systems at national, European and global level. It is essential to have the right data and information available at all points of care in all areas for decision support in an understandable, safe, secure and private way. Because information technology usage in healthcare sector is lagging behind other industry sectors, this sector needs more attention in implementing IT technology. For optimal health data exchange in society on national and international level it is necessary to ensure technical, semantic, organizational and legal interoperability. Majority of IT solutions in healthcare must meet these types of interoperability. First, thesis is focusing on eHealth technical and semantic international and local standards, which is a prerequisite for achieving broader interoperability and which dictate development of health information systems. Furthermore, these standards need to be included in architectural design of healthcare solutions. Great help to achieve this are OpenEHR and IHE (Integrating the Healthcare Enterprise). Existing solutions need to be adopted to the new healthcare standards and recommendations and consider modern computer trends. The most important decision regarding data exchange among variety of IT solutions is use of SOA or REST technology, which depends on the needs and limited bandwidth. Thesis middle part describes eHealth interoperability circumstances in EU and Slovenia, where guidelines are well designed, but the performance, at least in some countries, is unfolding too slow. Emerging standard FHIR (Fast Healthcare Interoperability Resources) is further analysed, which could even replace all currently existing healthcare standards for data exchange. Our company solution fMedic is analysed to present guidelines, how to adjust it, to the new FHIR standard. At the end there is set of guidelines and procedures, which have to be implemented when upgrading or developing interoperable eHealth solutions. The main thesis objective is to help find quick way to adjust existing eHealth solutions for the need of interoperability

Making the most of single sensor information

Most commercially successful face recognition systems combine information from multiple sensors (2D and 3D, visible light and infrared, etc.) to achieve reliable recognition in various environments. When only a single sensor is available, the robustness as well as efficacy of the recognition process suffer. In this paper, we focus on face recognition using images captured by a single 3D sensor and propose a method based on the use of region covariance matrixes and Gaussian mixture models (GMMs). All steps of the proposed framework are automated, and no metadata, such as pre-annotated eye, nose, or mouth positions is required, while only a very simple clustering-based face detection is performed. The framework computes a set of region covariance descriptors from local regions of different face image representations and then uses the unscented transform to derive low-dimensional feature vectors, which are finally modeled by GMMs. In the last step, a support vector machine classification scheme is used to make a decision about the identity of the input 3D facial image. The proposed framework has several desirable characteristics, such as an inherent mechanism for data fusion/integration (through the region covariance matrixes), the ability to explore facial images at different levels of locality, and the ability to integrate a domain-specific prior knowledge into the modeling procedure. Several normalization techniques are incorporated into the proposed framework to further improve performance. Extensive experiments are performed on three prominent databases (FRGC v2, CASIA, and UMB-DB) yielding competitive results

Towards Robust 3D Face Verification Using Gaussian Mixture Models

This paper focuses on the use of Gaussian Mixture models (GMM) for 3D face verification. A special interest is taken in practical aspects of 3D face verification systems, where all steps of the verification procedure need to be automated and no meta-data, such as pre-annotated eye/nose/mouth positions, is available to the system. In such settings the performance of the verification system correlates heavily with the performance of the employed alignment (i.e., geometric normalization) procedure. We show that popular holistic as well as local recognition techniques, such as principal component analysis (PCA), or Scale-invariant feature transform (SIFT)-based methods considerably deteriorate in their performance when an “imperfect” geometric normalization procedure is used to align the 3D face scans and that in these situations GMMs should be preferred. Moreover, several possibilities to improve the performance and robustness of the classical GMM framework are presented and evaluated: i) explicit inclusion of spatial information, during the GMM construction procedure, ii) implicit inclusion of spatial information during the GMM construction procedure and iii) on-line evaluation and possible rejection of local feature vectors based on their likelihood. We successfully demonstrate the feasibility of the proposed modifications on the Face Recognition Grand Challenge data set

Face-domain-specific automatic speech recognition models

This entry contains all the files required to implement face-domain-specific automatic speech recognition (ASR) applications using the Kaldi ASR toolkit (https://github.com/kaldi-asr/kaldi), including the acoustic model, language model, and other relevant files. It also includes all the scripts and configuration files needed to use these models for implementing face-domain-specific automatic speech recognition. The acoustic model was trained using the relevant Kaldi ASR tools (https://github.com/kaldi-asr/kaldi) and the Artur speech corpus (http://hdl.handle.net/11356/1776; http://hdl.handle.net/11356/1772). The language model was trained using the domain-specific text data involving face descriptions obtained by translating the Face2Text English dataset (https://github.com/mtanti/face2text-dataset) into the Slovenian language. These models, combined with other necessary files like the HCLG.fst and decoding scripts, enable the implementation of face-domain-specific ASR applications. Two speech corpora ("test" and "obrazi") and two Kaldi ASR models ("graph_splosni" and "graph_obrazi") can be selected for conducting speech recognition tests by setting the variable "graph" and "test_sets" in the "local/test_recognition.sh" script. Acoustic speech features can be extracted and speech recognition tests can be conducted using the "local/test_recognition.sh" script. Speech recognition test results can be obtained using the "results.sh" script. The KALDI_ROOT environment variable also needs to be set in the script "path.sh" to set the path to the Kaldi ASR toolkit installation folder

Evaluation of the pre-planned and non-planned agility performance

This study assessed differences in agility performance between athletes of team and individual sports by assessing change-of-direction speed (CODS) as pre-planned agility and reactive agility (RA) as non-planned in different spatial configurations. The study involved 36 individual (sprint, hurdles, jumping, tennis, and judo) and 34 team (soccer, basketball, and handball) athletes. CODS and RA were measured with a light-based reactive training system in a frontal (FR), universal (UN), semicircular (SC), and lateral (LA) design. Lower limb power and sprint performance were also measured in a 10 m single leg jump test and 15 m sprint. Individual athletes showed significantly better performance in three of the eight agility tests: LA-RA, UN-RA, and SC-CODS (p < 0.008, p < 0.036, and p < 0.027, respectively) and were found to present stronger correlations (p < 0.01) between jump test performance and the CODS condition. Team athletes showed stronger associations between sprint performance and the CODS condition. In the RA condition both jump and sprint performance showed stronger correlations in the group of individual athletes. Agility performance as measured by CODS and RA should improve with enhanced of motor proficiency. Finally, the tests applied in this experiment seem to be multidimensional, but require spatio-temporal adjustment for their implementation, so that they meet the requirements of the particular sport