Search Results (84)

Background: Extensive data exists on external load during training and competition, but a significant gap remains in understanding internal physiological load, particularly in protocols conducted in ecological settings. Given the scarcity of studies on the on-field cardiorespiratory profiles of national-level athletes, especially in Argentine soccer, this study aimed to identify the on-field cardiorespiratory fitness profile of ten highly trained youth field soccer players (13.6 ± 1.3 years old) from both the first league of the Argentine Football Association and members of the national team in their age group category in the current year. Methods: Each athlete performed an on-field cardiorespiratory exercise test (20-m Shuttle Run Test, 20-m SRT) with the COSMED K5 wearable metabolic system (COSMED, Rome, Italy) in dynamic micro-mixing chamber mode. The 20-m Shuttle Run Test involves running back and forth between two lines set 20 m apart, following the pace set by an audio signal. The test starts at a running velocity of 8.5 km·h⁻¹ and increases by 0.5 km·h⁻¹ each min. Results: Mean velocity at maximal oxygen uptake (v$\dot{\mathrm{V}}$O_2max) was 12.3 ± 0.7 km·h⁻¹. The maximal oxygen uptake ($\dot{\mathrm{V}}$O_2max) on-field was 67.1 ± 5.3 mL·kg⁻¹·min⁻¹. The $\dot{\mathrm{V}}$O₂ at the first and second ventilatory thresholds (VT1 and VT2) were identified at 67.0 ± 3.0% $\dot{\mathrm{V}}$O_2max (44.9 ± 3.3 mL·kg⁻¹·min⁻¹) and 84.7 ± 3.7% $\dot{\mathrm{V}}$O_2max (56.8 ± 3.8 mL·kg⁻¹·min⁻¹), respectively. Conclusions: This is a scarce on-field gas exchange assessment, conducted in an ecological context using a portable analyzer with highly trained national-level youth soccer players from the Argentine youth national team, which underlines their cardiorespiratory fitness, showcases their high-performance potential, offers valuable insights into a selective group of players, and provides a reference for larger-scale research on elite youth soccer and the long-term development of aerobic power and capacity. Full article

Birds can cause substantial damage to crops, directly affecting farmers’ productivity and profitability. As a result, detecting bird presence in crop fields is crucial for effective crop management. Traditional agricultural practices have used various tools and techniques to deter pest birds, while digital agriculture has advanced these efforts through Internet of Things (IoT) and artificial intelligence (AI) technologies. With recent advancements in hardware and processing chips, connected devices can now utilize deep convolutional neural networks (CNNs) for on-field image classification. However, training these models can be energy-intensive, especially when large amounts of data, such as images, need to be transmitted for centralized model training. Federated learning (FL) offers a solution by enabling local training on edge devices, reducing data transmission costs and energy demands while also preserving data privacy and achieving shared model knowledge across connected devices. This paper proposes a low-energy federated learning framework for a compact smart camera network designed to perform simple image classification for bird detection in crop fields. The results demonstrate that this decentralized approach achieves performance comparable to a centrally trained model while consuming at least 8 times less energy. Further efficiency improvements, with a minimal tradeoff in performance reduction, are explored through early stopping. Full article

Non-Newtonian fluids are widespread in industry, e.g., biomedical, food, and oil and gas, and their rheology plays a fundamental role in choosing the processing parameters. Centrifugal pumps are widely employed to ensure the displacement of a huge amount of fluids due to their robustness and reliability. Since the pump performance is usually provided by manufacturers only for water, the selection of a proper pump to handle non-Newtonian fluids may prove very tricky. On-field experiences in pump operations with non-Newtonian slurries report severe head and efficiency drops, especially in part-load operations, whose causes are still not fully understood. Several models are found in the literature to predict the performance of centrifugal pumps with this type of fluids, but a lack of reliability and generality emerges. In this work, an extensive experimental campaign is carried out with an on-purpose test bench to investigate the effect of non-Newtonian shear-thinning fluids on the performance of a small commercial centrifugal pump. A dedicated experimental campaign is conducted to study the causes of performance drops. The results allow to establish a relationship between head and efficiency drops with solid content in the mixture. Sudden performance drops and unstable operating points are detected in part-load operations and the most severe drops are detected with the higher kaolin content in the mixture. Performance drop investigation allows to ascribe performance drop to gas-locking phenomena. Finally, a critical analysis is proposed to relate the resulting performance with both fluids’ rheology and the gas fraction trapped in the fluid. The results here presented can be useful for future numerical validation and predicting performance models. Full article

Background: Elite kayaking demands peak conditioning, and tracking these athletes reveals the science behind world-class performance. Physiological demands and body composition changes in elite male kayakers were tracked during the preparatory and transition periods of a kayaking competitive season. Methods: Laboratory (body composition assessment and a 4 min all-out test in a kayak ergometer) on-field tests (4 × 1500 m incremental intermittent protocol with 30 s rest intervals in a kayaking/rowing track) were applied on separate days to follow eight elite male kayakers (23.1 ± 5.6 y; 80 ± 8.8 kg; 177.0 ± 6.8 cm) at the beginning of the kayaking season (preparatory period, M1; first week of October), 22 weeks later, at the beginning of the transition period (M2; last week of February), and 5 weeks later, at the end of the transition period, i.e., beginning of the competitive period of the season (M3; first week of April). M3 corresponded to the participation in international competitions. Results: Distance at peak oxygen uptake ($\dot{\mathrm{V}}{\mathrm{O}}_{2\mathrm{peak}}$) on the kayak ergometer improved by 36.7 m from M1 to M3, the pace at V4 (aerobic capacity) was reduced (improved) by 25.2 s·km⁻¹ from M1 to M2, and 25.6 s·km⁻¹ by M3. Body weight decreased by 2.3 kg from M1 to M2, and fat mass percentage and kilograms decreased by 1.8% and 3.1%, respectively. Fat-free mass increased by 1.9% and 3.1%, respectively. Skinfold measurements showed a decrease in subscapular, suprailiac, abdominal, and geminal skinfold. Aerobic power ($\dot{\mathrm{V}}$O_2peak) in absolute values (in L·min⁻¹) improved by 0.7 L·min⁻¹ from M1 to M2, and by 1.1 L·min⁻¹ by M3, and from M2 to M3 was ~0.5 L·min⁻¹. Aerobic power in relative values improved by 15.0 from M1 to M2, and by 6.4 mL·kg⁻¹·min⁻¹ from M2 to M3. Conclusions: Elite male kayakers improved their physiological performance and body composition during the preparatory and transition phases of the competitive season. Notable gains in performance were mainly due to enhanced aerobic power, and positive body composition changes. These findings provide insights for optimizing training strategies and boosting competitive performance. Full article

Context: There is limited information on the quantification of external load and reconditioning programs during adductor longus injuries in soccer. Case Presentation: This case report describes a male professional soccer player (LaLiga) returning to performance following an adductor longus muscle injury during the 2022/2023 season. The player suffered the injury during a change of direction in a match. The injury was confirmed by ultrasound after 48 h, and the previously validated rehabilitation and reconditioning program was applied to the injured player. This case report has focused on the development of the on-field reconditioning program and the quantification of the load during this phase. The goal of this case report was to return the player to pre-injury loads using global positioning systems (GPS). Variables such as total distance, distances covered at different speeds and metabolic load variables were quantified during the injury process, with the aim of increasing them through training and reaching at least 75% of the game load. Therefore, objective performance criteria for making return-to-play decisions based on the use of GPS was determined. In addition, the return to play (RTP) was on the 20th day after the injury, and then four RTPs were recorded in the following 6 weeks after the injury occurred, without re-injury. Conclusions: The approach to the competition performance profile, through the quantification of the external load during the rehabilitation process of the injured player, allowed us a safe return to competition and continued competition with a 6-week follow-up. Full article

Generally, playing group management performance and financial management performance of sports clubs are assessed separately. We adopt a non-parametric methodology to assess overall performance, first conceptualising overall management as a production process comprising two serially linked subprocesses, namely, playing group management and financial management. Thereafter, we decompose overall performance to obtain estimates of performance at the subprocess level. Through this procedure, it is possible to determine whether a sports club’s on-field performance or off-field performance or both may contribute towards its inefficiency, if any, in overall management. Further, a model is developed to determine targets for inefficient clubs to become overall efficient. The method is applied to 18 clubs in the Australian rules football league. In the 2021 season, the results reveal that on-field performance, on average, is better than off-field performance, and variability in off-field performance is higher than that of on-field performance. The observed overall management inefficiency is mainly due to inefficiency in financial management. Results are robust to the weighting scheme adopted in the overall efficiency configuration. Full article

Soccer is evolving into a science rather than just a sport, driven by intense competition between professional teams. This transformation requires efforts beyond physical training, including strategic planning, data analysis, and advanced metrics. Coaches and teams increasingly use sophisticated methods and data-driven insights to enhance decision-making. Analyzing team performance is crucial to prepare players and coaches, enabling targeted training and strategic adjustments. Expected goals (xG) analysis plays a key role in assessing team and individual player performance, providing nuanced insights into on-field actions and opportunities. This approach allows coaches to optimize tactics and lineup choices beyond traditional scorelines. However, relying solely on xG might not provide a full picture of player performance, as a higher xG does not always translate into more goals due to the intricacies and variabilities of in-game situations. This paper seeks to refine performance assessments by incorporating predictions for both expected goals (xG) and actual goals (aG). Using this new model, we consider a wider variety of factors to provide a more comprehensive evaluation of players and teams. Another major focus of our study is to present a method for selecting and categorizing players based on their predicted xG and aG performance. Additionally, this paper discusses expected goals and actual goals for each individual game; consequently, we use expected goals per game (xGg) and actual goals per game (aGg) to reflect them. Moreover, we employ regression machine learning models, particularly ridge regression, which demonstrates strong performance in forecasting xGg and aGg, outperforming other models in our comparative assessment. Ridge regression’s ability to handle overlapping and correlated variables makes it an ideal choice for our analysis. This approach improves prediction accuracy and provides actionable insights for coaches and analysts to optimize team performance. By using constructed features from various methods in the dataset, we improve our model’s performance by as much as 12%. These features offer a more detailed understanding of player performance in specific leagues and roles, improving the model’s accuracy from 83% to nearly 95%, as indicated by the R-squared metric. Furthermore, our research introduces a player selection methodology based on their predicted xG and aG, as determined by our proposed model. According to our model’s classification, we categorize top players into two groups: efficient scorers and consistent performers. These precise forecasts can guide strategic decisions, player selection, and training approaches, ultimately enhancing team performance and success. Full article

This study examined the relationship between body composition and on-field, in-game physical performance in female collegiate soccer players. Body composition, including total mass, fat mass, and lean tissue mass for the lower extremities and total body, was measured in 10 starting players using dual energy x-ray absorptiometry (DXA). On-field, in-game physical performance was tracked via a global positioning system (GPS) over 14 regular-season games, measuring total distance and distance covered in six speed zones. Players covered 4544.7 ± 495.2 m in the first half of the game and significantly less distance in the second half (3356.5 ± 1211.7 m, p = 0.004). A repeated measures ANOVA revealed decreased distances in jogging, low-, and moderate-intensity running during the second half compared to the first half of the game (p < 0.001). Lower total-body fat mass, total-body fat percentage, and lower-extremities fat mass were correlated with greater distances at moderate- and high-intensity running during the second half and entire game (r values from −0.644 to −0.745, p < 0.01 to 0.04). These findings suggest that body composition can influence the distance covered at moderate- and high-intensity running speed during competitive games. Training strategies aimed at reducing fat mass and incorporating high-intensity training may benefit female soccer players and enhance team success. Full article

The imbalance between parking availability and demand has led to a rise in traffic challenges in many cities. The adoption of technologies like the Internet of Things and deep learning algorithms has been extensively explored to build automated smart parking systems in urban environments. Non-human-mediated, scalable smart parking systems that are built on decentralized blockchain systems will further enhance transparency and trust in this domain. The presented work, PARKTag, is an integration of a blockchain-based system and computer vision models to detect on-field free parking slots, efficiently navigate vehicles to those slots, and automate the computation of parking fees. This innovative approach aims to enhance the efficiency, scalability, and convenience of parking management by leveraging and integrating advanced technologies for real-time slot detection, navigation, and secure, transparent fee calculation with blockchain smart contracts. PARKTag was evaluated through implementation and emulation in selected areas of the MIT Art Design Technology University campus, with a customized built-in dataset of over 2000 images collected on-field in different conditions. The fine-tuned parking slot detection model leverages pre-trained algorithms and achieves significant performance metrics with a validation accuracy of 92.9% in free slot detection. With the Solidity smart contract deployed on the Ethereum test network, PARKTag achieved a significant throughput of 10 user requests per second in peak traffic hours. PARKTag is implemented as a mobile application and deployed in the mobile application store. Its beta version has undergone user validation for feedback and acceptance, marking a significant step toward the development of the final product. Full article

The ability to make on-field, split-second decisions is critical for National Football League (NFL) game officials. Multiple principles in visual function are critical for accuracy and precision of these play calls, including foveation time and unobstructed line of sight, static visual acuity, dynamic visual acuity, vestibulo-ocular reflex, and sufficient visual field. Prior research has shown that a standardized curriculum in these neuro-ophthalmic principles have demonstrated validity and self-rated improvements in understanding, confidence, and likelihood of future utilization by NFL game officials to maximize visual performance during officiating. Virtual reality technology may also be able to help optimize understandings of specific neuro-ophthalmic principles and simulate real-life gameplay. Personal communication between authors and NFL officials and leadership have indicated that there is high interest in 3D virtual on-field training for NFL officiating. In this manuscript, we review the current and past research in this space regarding a neuro-ophthalmic curriculum for NFL officials. We then provide an overview our current visualization engineering process in taking real-life NFL gameplay 2D data and creating 3D environments for virtual reality gameplay training for football officials to practice plays that highlight neuro-ophthalmic principles. We then review in-depth the physiology behind these principles and discuss strategies to implement these principles into virtual reality for football officiating. Full article

The ability to overcome an opposition in team sports is reliant upon an understanding of the tactical behaviour of the opposing team members. Recent research is limited to a performance analysts’ own playing team members, as the required opposing team athletes’ geolocation (GPS) data are unavailable. However, in professional Australian rules Football (AF), animations of athlete GPS data from all teams are commercially available. The purpose of this technical study was to obtain the on-field location of AF athletes from animations of the 2019 Australian Football League season to enable the examination of the tactical behaviour of any team. The pre-trained object detection model YOLOv4 was fine-tuned to detect players, and a custom convolutional neural network was trained to track numbers in the animations. The object detection and the athlete tracking achieved an accuracy of 0.94 and 0.98, respectively. Subsequent scaling and translation coefficients were determined through solving an optimisation problem to transform the pixel coordinate positions of a tracked player number to field-relative Cartesian coordinates. The derived equations achieved an average Euclidean distance from the athletes’ raw GPS data of 2.63 m. The proposed athlete detection and tracking approach is a novel methodology to obtain the on-field positions of AF athletes in the absence of direct measures, which may be used for the analysis of opposition collective team behaviour and in the development of interactive play sketching AF tools. Full article

Road traffic noise is a severe environmental hazard, to which a growing number of dwellers are exposed in urban areas. The possibility to accurately assess traffic noise levels in a given area is thus, nowadays, quite important and, on many occasions, compelled by law. Such a procedure can be performed by measurements or by applying predictive Road Traffic Noise Models (RTNMs). Although the first approach is generally preferred, on-field measurement cannot always be easily conducted. RTNMs, on the contrary, use input information (amount of passing vehicles, category, speed, among others), usually collected by sensors, to provide an estimation of noise levels in a specific area. Several RTNMs have been implemented by different national institutions, adapting them to the local traffic conditions. However, the employment of RTNMs proves challenging due to both the lack of input data and the inherent complexity of the models (often composed of a Noise Emission Model–NEM and a sound propagation model). Therefore, this work aims to propose a methodology that allows an easy application of RTNMs, despite the availability of measured data for calibration. Four different NEMs were coupled with a sound propagation model, allowing the computation of equivalent continuous sound pressure levels on a dataset (composed of traffic flows, speeds, and source–receiver distance) randomly generated. Then, a Multilinear Regressive technique was applied to obtain manageable formulas for the models’ application. The goodness of the procedure was evaluated on a set of long-term traffic and noise data collected in a French site through several sensors, such as sound level meters, car counters, and speed detectors. Results show that the estimations provided by formulas coming from the Multilinear Regressions are quite close to field measurements (MAE between 1.60 and 2.64 dB(A)), confirming that the resulting models could be employed to forecast noise levels by integrating them into a network of traffic sensors. Full article

Ozone is a crucial component of the Earth’s atmosphere, playing a critical role in protecting the planet from harmful ultraviolet radiation. However, its concentration can vary greatly across different regions with significant impacts on human health and environment equilibrium. The aim of this work was to calibrate a low-cost sensing platform, based on chemoresistive gas sensors, to monitor the environmental concentration of O₃. The ongoing on-field calibration is performed with a deep neural network using the concentration of O₃ collected by the local environmental protection agencies through certified tools as the gold standard. Full article

Wheat is one of the most cultivated cereals thanks to both its nutritional value and its versatility to technological transformation. Nevertheless, the growth and yield of wheat, as well as of the other food crops, can be strongly limited by many abiotic and biotic stress factors. To face this need, new methodological approaches are required to optimize wheat cultivation from both a qualitative and quantitative point of view. In this context, crop analysis based on imaging techniques has become an important tool in agriculture. Thermography is an appealing method that represents an outstanding approach in crop monitoring, as it is well suited to the emerging needs of the precision agriculture management strategies. In this work, we performed an on-field infrared monitoring of several durum and common wheat varieties to evaluate their adaptability to the internal Mediterranean area chosen for cultivation. Two new indices based on the thermal data useful to estimate the agronomical response of wheat subjected to natural stress conditions during different phenological stages of growth have been introduced. The comparison with some productive parameters collected at harvest highlighted the correlation of the indices with the wheat yield (ranging between p < 0.001 and p < 0.05), providing interesting information for their early prediction. Full article

The aim of our study was to investigate the effects of a 24-week-long training program on changes in static body posture, as well as the characteristics of anterior shoulder pain in youth swimmers, and the relationship between changes in whole-body posture and the frequency and intensity of anterior shoulder pain. Competitive young swimmers (n = 54, 13.9 ± 1.79 years) were divided into experimental group and control group and both groups performed their usual swimming training. In addition, the experimental group performed a 24-week-long whole-body posture correction program. Before and after the implemented training, whole-body posture was analyzed using the PostureScreen (version 13.7) mobile application, and subjective intensity of pain was determined using the swimmer’s functional pain scale. Significant changes were found between the two groups in numerous measured postural parameters. A significant reduction in the prevalence of shoulder pain and score of the pain scale was observed after the posture correction program in the experimental group. Our results may imply that more optimal biomechanical conditions may indirectly reduce the incidence of swimmer’s shoulder in terms of prevention. Analysis and monitoring of body posture of swimmers using an on-field mobile application continuously, and the application of preventive training programs, may help to avoid developing injuries. Full article