A database-driven neural computing framework for classification of vertical jump patterns of healthy female netballers using 3D kinematics–EMG features

Abstract

Classification of athletes’ performance in vertical jump (VJ) tests is a recommended practice at various stages of athlete fitness development and performance enhancement. The practice is, however, currently conducted subjectively and mainly based on measures of the vertical jump height (VJH) attained in standardized VJ tests. The current study presents an intelligent integrated classification framework (IICF) for classification of athlete performance in single-leg (SL) and double-leg (DL) standing VJ tests based on lower extremity (LE) biomechanical data. Biomechanical data consisted of three-dimensional (3D) kinematics and electromyography (EMG) features generated from ankle–knee joints and eight LE muscles of 13 healthy female national netball players (subjects) obtained during six trials of SL-left leg (SLLL), SL-right leg (SLRL), and DL VJ tests. Each participating subject had prior VJ classification by the trainer as either excellent or very good in each of the three VJ tests. IICF introduced in this work utilizes an integration of the scalable and interoperable relational database management system (MySQLDB) and artificial neural networks (ANNs). Integrated pattern sets containing the extracted features (EF) obtained from first three VJ trials data were randomly partitioned into design and test data sets and ANN implemented using fully connected multilayer perceptron feedforward neural networks (MLP-FFNN) with three different training algorithms. Subjects’ prior VJ classifications were used as MLP-FFNN target outputs. A second classifier is trained using support vector machine (SVM) using three different kernel mapping functions and performances between MPL-FFNN and SVM classifiers compared on both training and independent test pattern sets. The test pattern sets comprise of EF generated from the latter three VJ trials data stored in MySQLDB. The average classification accuracy (F-measure) achieved by the optimally trained MLP-FFNN classifier on independent test pattern sets across all the three VJ activities was 93.33% (86.67–96.77%), whereas SVM classifier’s was 82.5% (73.33–87.5%). Through a custom-made web-based interface having backend integration of MySQLDB and the optimally trained MLP-FFNN, classification of individual subjects’ pattern sets from either VJ activities is also enabled. Individual subjects’ classification results can then be compared with prior classifications by the trainer, and differences were highlighted. IICF introduced in this work has demonstrated its feasibility as an objective complementary assessment tool for trainers during conducting of SL and DL vertical jump tests for netball players and athletes in general. Descriptive statistics for the entire experimental data sets’ features used in this study are also presented.