RESUMEN

BACKGROUND: After the World Health Organization declared the COVID-19 pandemic, the role of Vitamin D has become even more critical for people worldwide. The most accurate way to define vitamin D level is 25-hydroxy vitamin D(25-OH-D) blood test. However, this blood test is not always feasible. Most data sets used in health science research usually contain highly correlated features, which is referred to as multicollinearity problem. This problem can lead to misleading results and overfitting problems in the ML training process. Therefore, the proposed study aims to determine a clinically acceptable ML model for the detection of the vitamin D status of the North Cyprus adult participants accurately, without the need to determine 25-OH-D level, taking into account the multicollinearity problem. METHOD: The study was conducted with 481 observations who applied voluntarily to Internal Medicine Department at NEU Hospital. The classification performance of four conventional supervised ML models, namely, Ordinal logistic regression(OLR), Elastic-net ordinal regression(ENOR), Support Vector Machine(SVM), and Random Forest (RF) was compared. The comparative analysis is performed regarding the model's sensitivity to the participant's metabolic syndrome(MtS)'positive status, hyper-parameter tuning, sensitivities to the size of training data, and the classification performance of the models. RESULTS: Due to the presence of multicollinearity, the findings showed that the performance of the SVM(RBF) is obviously negatively affected when the test is examined. Moreover, it can be obviously detected that RF is more robust than other models when the variations in the size of training data are examined. This experiment's result showed that the selected RF and ENOR showed better performances than the other two models when the size of training samples was reduced. Since the multicollinearity is more severe in the small samples, it can be concluded that RF and ENOR are not affected by the presence of the multicollinearity problem. The comparative analysis revealed that the RF classifier performed better and was more robust than the other proposed models in terms of accuracy (0.94), specificity (0.96), sensitivity or recall (0.94), precision (0.95), F1-score (0.95), and Cohen's kappa (0.90). CONCLUSION: It is evident that the RF achieved better than the SVM(RBF), ENOR, and OLR. These comparison findings will be applied to develop a Vitamin D level intelligent detection system for being used in routine clinical, biochemical tests, and lifestyle characteristics of individuals to decrease the cost and time of vitamin D level detection.

Asunto(s)

COVID-19 , Pandemias , Adulto , Humanos , COVID-19/diagnóstico , Aprendizaje Automático , Modelos Logísticos , Máquina de Vectores de Soporte , Vitamina D

RESUMEN

Psychological and behavioral evidence suggests that home sports activity reduces negative moods and anxiety during lockdown days of COVID-19. Low-cost, nonintrusive, and privacy-preserving smart virtual-coach Table Tennis training assistance could help to stay active and healthy at home. In this paper, a study was performed to develop a Forehand stroke' performance evaluation system as the second principal component of the virtual-coach Table Tennis shadow-play training system. This study was conducted to show the effectiveness of the proposed LSTM model, compared with 2DCNN and RBF-SVR time-series analysis and machine learning methods, in evaluating the Table Tennis Forehand shadow-play sensory data provided by the authors. The data was generated, comprising 16 players' Forehand strokes racket's movement and orientation measurements; besides, the strokes' evaluation scores were assigned by the three coaches. The authors investigated the ML models' behaviors changed by the hyperparameters values. The experimental results of the weighted average of RMSE revealed that the modified LSTM models achieved 33.79% and 4.24% estimation error lower than 2DCNN and RBF-SVR, respectively. However, the R ¯ 2 results show that all nonlinear regression models are fit enough on the observed data. The modified LSTM is the most powerful regression method among all the three Forehand types in the current study.

Asunto(s)

Aprendizaje Profundo , Tenis/psicología , Anciano , Algoritmos , Brazo/fisiología , Fenómenos Biomecánicos , Simulación por Computador , Femenino , Humanos , Aprendizaje , Masculino , Persona de Mediana Edad , Destreza Motora , Dinámicas no Lineales , Análisis de Regresión

RESUMEN

Shadow-play, is an assistance solution for Table Tennis training, develops novice players' strokes and performing skills, and helps the players' brain to train in terms of the correct positioning and how the proper stroke technique feels. Most currently proposed training assistance systems are rarely used in actual applications, as they are expensive and their setup is complex. Thus, there is a need for a practical and low-cost intelligent system training assistance solution, as well as the possibility of using this solution comfortably to assist players. This paper specifies Forehand shadow play strokes movement and orientation sensory dataset for Table Tennis using a miniaturized low-powered, inexpensive and non- intrusive Inertial Measurement Unit (IMU) BNO055. We mounted the IMU on the center of a standard Table Tennis racket's surface. Eight novices, eight professional players, and three high ranked Table Tennis coaches participated in this research voluntarily. The Racket enabled us to collect players' strokes' time-series data responsively and sensitively. Collected sensory time-series data contains 1570 samples for the Basic, Topspin, and Push Forehand strokes of the players. Besides, all performed strokes were manually labeled and scored by the coaches simultaneously. The sensory dataset contains data from one 9-axis IMU (3- axis Accelerometer, 3- axis gyroscope, and 3- axis magnetometer) and Euler angles (roll, pitch, and yaw angles), mounted on the Racket. Based on the nature of the Forehand movements, the center of the surface was empirically determined to be the appropriate sensor placement in this experiment. We accomplished the collection of all samples under conditions that have been set by the coaches. The authors expect that the collected dataset can be used in a digital shadow-play coaching system to automatically send feedback to novice players when they practice shadow-play Table Tennis strokes individually.