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Abstract This study applied Nielsen's heuristics to assess the user interface of the Sport Analysis Application. The purpose of this application aid students in evaluating their performance based on test results and adhering to training guidelines derived from the application's recommendations. The primary framework used in this investigation is Nielsen's heuristics, coupled with experiments to gauge the extent to which the application's user interface aligns with user preferences (user-friendly). The study targeted a population of students, with a sample size of 100 individuals. Analysis and discussion were conducted through the distribution of questionnaires to participants. The research findings revealed that the Visibility of System Status score reached 151. Overall, the study concludes that the developed Sport Analysis Application successfully achieves its primary objectives with a user-friendly interface.

We outline the development of a model for predicting the outcome of a yacht match race between two competing designs. The model is a fixed-time-increment simulation that accounts for the dynamic performance of each yacht. The wind speed and direction are modelled using hidden Markov chain models. Each yacht is assumed to follow a fixed sailing strategy determined by a set of simple decision rules. The simulation models both yachts simultaneously and accounts for interactions between them-for example, when they cross. The model is illustrated by applying it to International America's Cup Class designs.

An objective analysis of the human movement can help both clinical assessment and sports performance. Kinovea is a free 2D motion analysis software that can be used to measure kinematic parameters. This low-cost technology has been used in sports sciences, as well as in the clinical and research fields. One interesting tool is that it can measure an object (or person) passing in front of the camera, taking into account the perspective between the camera and the recorded object. Although it has been validated as a tool to assess time-related variables, few studies assessed its validity compared to a Gold Standard; furthermore, its reliability in different perspectives has not been previously assessed. The main objective of this study is to determine the validity of the Kinovea software compared to AutoCAD, and its intra and inter-rater reliability in obtaining coordinates data; a second objective is to compare their results at 4 different perspectives (90°, 75°, 60° and 45°) and to assess the inter and intra rater reliability at each perspective. For this purpose, a wire structure figure in the shape of a human lower limb was designed and measured in AutoCAD; it was then recorded during a pendular motion with a video-camera placed at distance of 5 m and analyzed with Kinovea in the 4 perspectives (90°, 75°, 60° and 45°). Each frame was examined by three observers who made two attempts. A multiple approach was applied involving the analysis of the systematic error, with a two-way ANOVA 2x4; the relative reliability with Intraclass Correlation Coefficient (ICC) and the Coefficient of Variance (CV) (95% confidence interval); and the absolute reliability with the Standard Error (SE). The results indicate that the Kinovea software is a valid and reliable tool that is able to measure accurately at distances up to 5 m from the object and at an angle range of 90°-45°. Nevertheless, for optimum results an angle of 90° is suggested.

Purpose Several review articles have been published within the Artificial Intelligence (AI) literature that have explored a range of applications within the tourism and hospitality sectors. However, how efficiently the applied AI methods and algorithms have performed with respect to the type of applications and the multimodal sets of data domains have not yet been reviewed. Therefore, this paper aims to review and analyse the established AI methods in hospitality/tourism, ranging from data modelling for demand forecasting, tourism destination and behaviour pattern to enhanced customer service and experience. Design/methodology/approach The approach was to systematically review the relationship between AI methods and hospitality/tourism through a comprehensive literature review of papers published between 2010 and 2021. In total, 146 articles were identified and then critically analysed through content analysis into themes, including “AI methods” and “AI applications”. Findings The review discovered new knowledge in identifying AI methods concerning the settings and available multimodal data sets in hospitality and tourism. Moreover, AI applications fostering the tourism/hospitality industries were identified. It also proposes novel personalised AI modelling development for smart tourism platforms to precisely predict tourism choice behaviour patterns. Practical implications This review paper offers researchers and practitioners a broad understanding of the proper selection of AI methods that can potentially improve decision-making and decision-support in the tourism/hospitality industries. Originality/value This paper contributes to the tourism/hospitality literature with an interdisciplinary approach that reflects on theoretical/practical developments for data collection, data analysis and data modelling using AI-driven technology.

Background: Research on the application of technology in sports in Romania is completely lacking, and the existing studies at the international level have mainly been carried out in recent years. We considered it appropriate to highlight the best practice models of technology application in sports that can be multiplied, adapted, improved, and widely used. The paper aims to identify the use of technology and devices in sports, with an emphasis on their role in training and competitions with the aim of improving sports performance, to provide sports specialists, organizations, and authorities with a wide range of information regarding the connection between sport and technology. The results obtained regarding the application of technology in sports refer mainly to the following: techniques and technologies used in training and competition (portable localization technology and global positioning systems (GPS); Virtual Reality (VR) technology; video analysis; digital technologies integrated into sports training); aspects of sports training targeted through the use of technology (use of technology for athlete health, recovery, and injury management; use of technology for monitoring sports performance and various body indicators); training optimization and ecological dynamics and the sustainable development of sports. Conclusions: Unitary research, at a European or even global level, in a uniform theoretical and practical framework, could lead to much more efficient training with large increases in sports performance. The coaches and specialists working with the athlete determine the specificity of some elements of the training, depending on the characteristics of each athlete. Large clubs could become a factor in generating and disseminating knowledge related to training and competition monitoring, sports performance enhancement, and health, recovery, and injury management. Research directions for the use of technology in sport and the formation of connections with other fields can be extended. For example, combined technologies assisted by specialized software can be used. Creativity must be the starting point for the use and combination of existing technologies in sports and for the creation of new ones. Their creation and use involve the teamwork of athletes, coaches, and specialists from different fields, such as sports, physiology, psychology, biomechanics, informatics, etc.

Background The study of human movement within sports biomechanics and rehabilitation settings has made considerable progress over recent decades. However, developing a motion analysis system that collects accurate kinematic data in a timely, unobtrusive and externally valid manner remains an open challenge. Main body This narrative review considers the evolution of methods for extracting kinematic information from images, observing how technology has progressed from laborious manual approaches to optoelectronic marker-based systems. The motion analysis systems which are currently most widely used in sports biomechanics and rehabilitation do not allow kinematic data to be collected automatically without the attachment of markers, controlled conditions and/or extensive processing times. These limitations can obstruct the routine use of motion capture in normal training or rehabilitation environments, and there is a clear desire for the development of automatic markerless systems. Such technology is emerging, often driven by the needs of the entertainment industry, and utilising many of the latest trends in computer vision and machine learning. However, the accuracy and practicality of these systems has yet to be fully scrutinised, meaning such markerless systems are not currently in widespread use within biomechanics. Conclusions This review aims to introduce the key state-of-the-art in markerless motion capture research from computer vision that is likely to have a future impact in biomechanics, while considering the challenges with accuracy and robustness that are yet to be addressed.

Research investigating the nature and scope of developmental participation patterns leading to international senior-level success is mainly explorative up to date. One of the criticisms of earlier research was its typical multiple testing for many individual participation variables using bivariate, linear analyses. Here, we applied state-of-the-art supervised machine learning to investigate potential non-linear and multivariate effects of coach-led practice in the athlete's respective main sport and in other sports on the achievement of international medals. Participants were matched pairs (sport, sex, age) of adult international medallists and non-medallists (n = 166). Comparison of several non-ensemble and tree-based ensemble binary classification algorithms identified \"eXtreme gradient boosting\" as the best-performing algorithm for our classification problem. The model showed fair discrimination power between the international medallists and non-medallists. The results indicate that coach-led other-sports practice until age 14 years was the most important feature. Furthermore, both main-sport and other-sports practice were non-linearly related to international success. The amount of main-sport practice displayed a parabolic pattern while the amount of other-sports practice displayed a saturation pattern. The findings question excess involvement in specialised coach-led main-sport practice at an early age and call for childhood/adolescent engagement in coach-led practice in various sports. In data analyses, combining traditional statistics with advanced supervised machine learning may improve both testing of the robustness of findings and new discovery of patterns among multivariate relationships of variables, and thereby of new hypotheses.

Recent advances in sensor technologies, in particular video-based human detection, object tracking and pose estimation, have opened new possibilities for the automatic or semi-automatic per-frame annotation of sport videos. In the case of racket sports such as tennis and padel, state-of-the-art deep learning methods allow the robust detection and tracking of the players from a single video, which can be combined with ball tracking and shot recognition techniques to obtain a precise description of the play state at every frame. These data, which might include the court-space position of the players, their speeds, accelerations, shots and ball trajectories, can be exported in tabular format for further analysis. Unfortunately, the limitations of traditional table-based methods for analyzing such sport data are twofold. On the one hand, these methods cannot represent complex spatio-temporal queries in a compact, readable way, usable by sport analysts. On the other hand, traditional data visualization tools often fail to convey all the information available in the video (such as the precise body motion before, during and after the execution of a shot) and resulting plots only show a small portion of the available data. In this paper we address these two limitations by focusing on the analysis of video-based tracking data of padel matches. In particular, we propose a domain-specific query language to facilitate coaches and sport analysts to write queries in a very compact form. Additionally, we enrich the data visualization plots by linking each data item to a specific segment of the video so that analysts have full access to all the details related to the query. We demonstrate the flexibility of our system by collecting and converting into readable queries multiple tips and hypotheses on padel strategies extracted from the literature.

The recent trend in electrocardiogram (ECG) device development is towards wireless body sensors applied for patient monitoring. The ultimate goal is to develop a multi-functional body sensor that will provide synchronized vital bio-signs of the monitored user. In this paper, we present an ECG sensor for long-term monitoring, which measures the surface potential difference between proximal electrodes near the heart, called differential ECG lead or differential lead, in short. The sensor has been certified as a class IIa medical device and is available on the market under the trademark Savvy ECG. An improvement from the user’s perspective—immediate access to the measured data—is also implemented into the design. With appropriate placement of the device on the chest, a very clear distinction of all electrocardiographic waves can be achieved, allowing for ECG recording of high quality, sufficient for medical analysis. Experimental results that elucidate the measurements from a differential lead regarding sensors’ position, the impact of artifacts, and potential diagnostic value, are shown. We demonstrate the sensors’ potential by presenting results from its various areas of application: medicine, sports, veterinary, and some new fields of investigation, like hearth rate variability biofeedback assessment and biometric authentication.

Action quality assessment (AQA) tasks in computer vision evaluate action quality in videos, and they can be applied to sports for performance evaluation. A typical example of AQA is predicting the final score from a video that captures an entire figure skating program. However, no previous studies have predicted individual jump scores, which are of great interest to competitors because of the high weight of competition. Despite the presence of unnecessary information in figure skating videos, human specialists can focus and reduce information when they evaluate jumps. In this study, we clarified the eye movements of figure skating judges and skaters while evaluating jumps and proposed a prediction model for jump performance that utilized specialists’ gaze location to reduce information. Kinematic features obtained from the tracking system were input into the model in addition to videos to improve accuracy. The results showed that skaters focused more on the face, whereas judges focused on the lower extremities. These gaze locations were applied to the model, which demonstrated the highest accuracy when utilizing both specialists’ gaze locations. The model outperformed human predictions and the baseline model (RMSE:0.775), suggesting a combination of human specialist knowledge and machine capabilities could yield higher accuracy.