Wheelchair skill tests in wheelchair Basketball: A systematic review.

OBJECTIVES: The aim of this study was to identify, describe and synthesize the skill tests used in wheelchair basketball. METHOD: A systematic review was carried out in the databases: PubMed/Medline, ScienceDirect, Scopus, Web of science and Google Scholar from inception to January 2021 with up to date in January 2022. the eligibility criteria used were Inclusion: (i) evaluation of wheelchair basketball athletes; (ii) using skill tests (defined as agility, speed, ball maneuverability, slalom, etc.) and (iii) papers needed to be written in English and published in peer-reviewed journals. Exclusion: (i) papers with poor description of the test methodology, (ii) participants not classified as wheelchair basketball athletes (less than one year of practice) and (iii) Participants were not people without disabilities. RESULTS: Our main findings were: a) the most explored skills were pass and speed, and the most frequent test was the pass tests and sprint tests, b) Strong associations were found between sports classes and performance in field tests, c) The most used tests for each skill were: pass = pass accuracy and maximum pass; speed = 20m sprint test with and without the ball; agility = slalom test; dribbling = obstacle dribbling tests and throw = free throw and spot shot. CONCLUSION: The most explored skills were passing and speed, and to evaluate these skills we highlight the two-handed chest pass test, 20m sprint test with ball and the WMP test. The use of specific tests can facilitate the creation of reference standards and possible comparison of athletes and, thus, enable better training conditions, aiming to meet the specific demands of each athlete and team.

Hand Resting Tremor Assessment of Healthy and Patients With Parkinson's Disease: An Exploratory Machine Learning Study.

The aim of this study is comparing the accuracies of machine learning algorithms to classify data concerning healthy subjects and patients with Parkinson's Disease (PD), toward different time window lengths and a number of features. Thirty-two healthy subjects and eighteen patients with PD took part on this study. The study obtained inertial recordings by using an accelerometer and a gyroscope assessing both hands of the subjects during hand resting state. We extracted time and temporal frequency domain features to feed seven machine learning algorithms: k-nearest-neighbors (kNN); logistic regression; support vector classifier (SVC); linear discriminant analysis; random forest; decision tree; and gaussian Naïve Bayes. The accuracy of the classifiers was compared using different numbers of extracted features (i.e., 272, 190, 136, 82, and 27) from different time window lengths (i.e., 1, 5, 10, and 15 s). The inertial recordings were characterized by oscillatory waveforms that, especially in patients with PD, peaked in a frequency range between 3 and 8 Hz. Outcomes showed that the most important features were the mean frequency, linear prediction coefficients, power ratio, power density skew, and kurtosis. We observed that accuracies calculated in the testing phase were higher than in the training phase. Comparing the testing accuracies, we found significant interactions among time window length and the type of classifier (p < 0.05). The study found significant effects on estimated accuracies, according to their type of algorithm, time window length, and their interaction. kNN presented the highest accuracy, while SVC showed the worst results. kNN feeding by features extracted from 1 and 5 s were the combination with more frequently highest accuracies. Classification using few features led to similar decision of the algorithms. Moreover, performance increased significantly according to the number of features used, reaching a plateau around 136. Finally, the results of this study suggested that kNN was the best algorithm to classify hand resting tremor in patients with PD.