On the prediction of tibiofemoral contact forces for healthy individuals and osteoarthritis patients during gait: a comparative study of regression methods.

Knee osteoarthritis (OA) is a public health problem affecting millions of people worldwide. The intensity of the tibiofemoral contact forces is related to cartilage degeneration, and so is the importance of quantifying joint loads during daily activities. Although simulation with musculoskeletal models has been used to calculate joint loads, it demands high-cost equipment and a very time-consuming process. This study aimed to evaluate consolidated machine learning algorithms to predict tibiofemoral forces during gait analysis of healthy individuals and knee OA patients. Also, we evaluated three different datasets to train each model, considering different combinations of primary kinematic and kinetic data, and post-processing data. We evaluated 14 patients with severe unilateral knee OA and 14 healthy individuals during 3-5 gait trials. Data were split into 70% and 30% of the samples as training and test data. Test data was independently evaluated considering a mixture of pathological and healthy individuals, and only OA and Control patients. The main results showed that accurate predictions of the tibiofemoral contact forces were achieved using machine learning methods and that the predictions were sensitive to changes in the input data as training. The present study provided insights into the most promising regressions methods to predict knee contact forces representing an important starting point for the broader application of biomechanical analysis in clinical environments.

Interpersonal coordination of opposing player dyads during attacks performed in official football matches.

The purpose of this study was to characterise the interpersonal coordination between opponent players during offensive sequences in official matches and to verify if offensive sequences ended in shots to goal present different coordination patterns when compared than those that ended in defensive tackles. A total of 580 offensive sequences occurred during matches resulting in shots to goal (n = 172) or defensive tackles (n = 408) were analysed. The bidimensional coordinates and technical actions of male professional football players (n = 1160) were obtained using a video-based tracking system. Dyads were defined using a network analysis and composed of the nearest opponent. Interpersonal coordination of the dyads was analysed using the vector coding and the frequency for each coordination pattern was computed. In-phase was predominant for all displacement directions and offensive sequences outcomes, and antiphase was the least frequent. For lateral displacements, offensive sequences ending in shot to goal presented lower frequency for in-phase and higher frequency for offensive player phase than ended in defensive tackle. This information about the relationship of opponent players dyads during decisive moments of the matches provides fundamentals for future research and assists coaches to understand the different behaviours in successful and unsuccessful attacks.

Classification and determinants of passing difficulty in soccer: a multivariate approach.

INTRODUCTION: Usually, the players' or teams' efficiency to perform passes is measured in terms of accuracy. The degree of difficulty of this action has been overlooked in the literature. OBJECTIVES: The present study aimed to classify the degree of passing difficulty in soccer matches and to identify and to discuss the variables that most explain the passing difficulty using spatiotemporal data. RESULTS: The data used corresponds to 2,856 passes and 32 independent variables. The Fisher Discriminant Analysis presented 72.0% of the original grouped cases classified correctly. The passes analyzed were classified as low (56.5%), medium (22.6%), and high difficulty (20.9%), and we identified 16 variables that best explain the degree of passing difficulty related to the passing receiver, ball trajectory, pitch position and passing player. CONCLUSIONS: The merit and ability of the player to perform passes with high difficulty should be valued and can be used to rank the best players and teams.In addition, the highlighted variables should be looked carefully by coaches when analyzing profiles, strengths and weaknesses of players and teams, and talent identification context. PRACTICAL IMPLICATIONS: The values found for each variable can be used as a reference for planning training, such as small side games, and in future research.

Comparing CAM Algorithms for the Identification of Salient Image Features in Iconography Artwork Analysis.

Iconography studies the visual content of artworks by considering the themes portrayed in them and their representation. Computer Vision has been used to identify iconographic subjects in paintings and Convolutional Neural Networks enabled the effective classification of characters in Christian art paintings. However, it still has to be demonstrated if the classification results obtained by CNNs rely on the same iconographic properties that human experts exploit when studying iconography and if the architecture of a classifier trained on whole artwork images can be exploited to support the much harder task of object detection. A suitable approach for exposing the process of classification by neural models relies on Class Activation Maps, which emphasize the areas of an image contributing the most to the classification. This work compares state-of-the-art algorithms (CAM, Grad-CAM, Grad-CAM++, and Smooth Grad-CAM++) in terms of their capacity of identifying the iconographic attributes that determine the classification of characters in Christian art paintings. Quantitative and qualitative analyses show that Grad-CAM, Grad-CAM++, and Smooth Grad-CAM++ have similar performances while CAM has lower efficacy. Smooth Grad-CAM++ isolates multiple disconnected image regions that identify small iconographic symbols well. Grad-CAM produces wider and more contiguous areas that cover large iconographic symbols better. The salient image areas computed by the CAM algorithms have been used to estimate object-level bounding boxes and a quantitative analysis shows that the boxes estimated with Grad-CAM reach 55% average IoU, 61% GT-known localization and 31% mAP. The obtained results are a step towards the computer-aided study of the variations of iconographic elements positioning and mutual relations in artworks and open the way to the automatic creation of bounding boxes for training detectors of iconographic symbols in Christian art images.

Relationship between maximal aerobic power with aerobic fitness as a function of signal-to-noise ratio.

Efforts to better understand cardiorespiratory health are relevant for the future development of optimized physical activity programs. We aimed to explore the impact of the signal quality on the expected associations between the ability of the aerobic system in supplying energy as fast as possible during moderate exercise transitions with its maximum capacity to supply energy during maximal exertion. It was hypothesized that a slower aerobic system response during moderate exercise transitions is associated with a lower maximal aerobic power; however, this relationship relies on the quality of the oxygen uptake data set. Forty-three apparently healthy participants performed a moderate constant work rate (CWR) followed by a pseudorandom binary sequence (PRBS) exercise protocol on a cycle ergometer. Participants also performed a maximum incremental cardiopulmonary exercise testing (CPET). The maximal aerobic power was evaluated by the peak oxygen uptake during the CPET, and the aerobic fitness was estimated from different approaches for oxygen uptake dynamics analysis during the CWR and PRBS protocols at different levels of signal-to-noise ratio. The product moment correlation coefficient was used to evaluate the correlation level between variables. Aerobic fitness was correlated with maximum aerobic power, but this correlation increased as a function of the signal-to-noise ratio. Aerobic fitness is related to maximal aerobic power; however, this association appeared to be highly dependent on the data quality and analysis for aerobic fitness evaluation. Our results show that simpler moderate exercise protocols might be as good as maximal exertion exercise protocols to obtain indexes related to cardiorespiratory health.NEW & NOTEWORTHY Optimized methods for cardiorespiratory health evaluation are of great interest for public health. Moderate exercise protocols might be as good as maximum exertion exercise protocols to evaluate cardiorespiratory health. Pseudorandom or constant workload moderate exercise can be used to evaluate cardiorespiratory health.