Deep learning-enabled block scrambling algorithm for securing telemedicine data of table tennis players

A comparative study of machine learning and deep learning algorithms for padel tennis shot classification

Data processing in sports is a phenomenon increasingly present at all levels, from professionals in search of tools to improve their performance to beginners motivated by the quantification of their physical activity. In this work, a comparison between some of the main machine learning and deep learning algorithms is carried out in order to classify padel tennis strokes. Up to 13 representative padel tennis strokes are classified. Before a classification of padel tennis strokes is performed, a sufficiently representative data set is needed that collects numerous examples of the performance of these strokes. Since there was no similar data set in the literature, we proceeded to the creation of such a data set, for which we developed a data collection system based on an electronic device with an inertial measurement unit. Using the developed data set, the machine learning and deep learning algorithms were hyperparameterized to compare their performance under the best possible configurations. The algorithms were fed with both the temporal series of the acceleration and speed of the six degrees of freedom and also with feature engineering input, consisting in calculating the mean, maximum, and minimum values for each axis. The algorithms evaluated are: fully connected or dense neural networks, 1D convolutional neural networks, decision tree, K nearest neighbors, support vector machines, and eigenvalue classification. According to the results achieved, the best algorithm is the 1D convolutional neural network with temporal series input that achieves an accuracy higher than 93%. However, other simpler algorithms such as dense networks and support vector machines achieve similar results.

Design of Medical Image Detail Enhancement Algorithm for Ankle Joint Talar Osteochondral Injury

Medical imaging modalities, such as magnetic resonance imaging (MRI) and computerized tomography (CT), have allowed medical researchers and clinicians to examine the structural and functional features of the human body, thereby assisting the clinical diagnosis. However, due to the highly controlled imaging environment, the imaging process often creates noise, which seriously affects the analysis of the medical images. In this study, a medical imaging enhancement algorithm is presented for ankle joint talar osteochondral injury. The gradient operator is used to transform the image into the gradient domain, and fuzzy entropy is employed to replace the gradient to determine the diffusion coefficient of the gradient field. The differential operator is used to discretize the image, and a partial differential enhancement model is constructed to achieve image detail enhancement. Three objective evaluation indexes, namely, signal-to-noise ratio (SNR), information entropy (IE), and edge protection index (EPI), were employed to evaluate the image enhancement capability of the proposed algorithm. Experimental results show that the algorithm can better suppress noise while enhancing image details. Compared with the original image, the histogram of the transformed image is more uniform and flat and the gray level is clearer.