![]() ![]() It could be inferred that the method proposed decisively provide the classification of skateboarding tricks efficiently and would certainly provide a more objective based judgment in awarding the score of the tricks. It was observed from the preliminary investigation that the SVM model attained the highest classification accuracy with a value of 99.5% followed by LR, k-NN, RF, and NB with 98.6%, 95.8%, 82.4% and 78.7%, respectively. Several classification models were evaluated, namely, Support Vector Machine (SVM), k-Nearest Neighbour (kNN), Logistic Regression (LR), Random Forest (RF) and Naïve Bayes (NB) on their ability in classifying the tricks based on the engineered features. The features extracted from each trick were engineered using Inception-V3 image embedder. Each trick is collected upon five successful landings and the camera is placed 1.26 m from the subject on a flat cemented ground. The subject used for accomplishing the tricks is a male amateur skateboarder at the age of 23 years old with ±5.0 years’ experience using ORY skateboard. The common tricks in skateboarding such as Kickflip, Ollie, Nollie, Pop Shove-it and Frontside 180 are investigated in this study via the synthetization of image processing and machine learning classifiers. Hence, it is crucial to underline the benchmark for analyzing the rate of successful execution of skateboarding trick for high level tournaments. More often than not, the evaluation of skateboarding tricks executions is assessed intuitively according to the judges’ observation and hence are susceptible to biasness if not inaccurate judgement. Professional and semi-professional golfers as well coaches could consider Putt.It.In device in monitoring strokes related parameters to enhance their performance due to its effectiveness in providing information on putting performance. ![]() Conclusion: The Putt.It.In monitoring device is found to be reliable in measuring the backswing distance, front swing distance, clubhead speed, ideal front swing distance and swing angle. Moreover, the Kolmogorov/Smirnov test re-test indicates that there is no significant difference between the first two 2 m strokes p > 0.05, and subsequent two 1 m strokes p > 0.05 highlighting its ability to recognise the pattern of the strokes applied in the four successive strokes. Results: The ICC reveals 0.98 and 0.96 for both test 1 and 2, as well as a Cronbach's Alpha of 0.99 and 0.96, respectively suggesting excellent consistencies in the overall observations. The intra-class correlation (ICC) coefficient is employed to test the reliability of the device whilst the Kolmogorov/Smirnov test was utilised to further reaffirm the reliability of the application in measuring the aforementioned parameters over test re-test between first two strokes of 2 m distance and the last two strokes of 1 m distance. Methods: A semi-professional golfer (30 years of age ± 5.0 years' experience) executed four strokes repeatedly from a distance of 2 m and 1 m using a Ram Zebra Mallet putter on a PGM golf mat. Objectives: This study aims to investigate the reliability of the device in measuring the backswing distance, front swing distance, clubhead speed, ideal front swing distance and swing angle. A cost-effective golf putting monitoring device namely the Putt.It.In was developed for analysing a golfers' putting performance. The provision of such information is favourable only if it is reliable. Background: The accurate transfer of information on the athletes' performance in any sport is essential in enhancing the performance and overall coaching process.
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