Analysis of Whole-Body Coordination Patterning in Successful and Faulty Spikes Using Self-Organising Map-Based Cluster Analysis: A Secondary Analysis

Abstract

This study investigated the whole-body coordination patterning in successful and faulty spikes using self-organising map-based cluster analysis. Ten young, elite volleyball players (aged 15.5 ± 0.7 years) performed 60 volleyball spikes in a real-game environment. Adopting the cluster analysis, based on a self-organising map, whole-body coordination patterning was explored between successful and faulty spikes of individual players. The self-organising maps (SOMs) portrayed whole body, lower and upper limb coordination dissimilarities during the jump phase and the ball impact phases between the successful and faulty spikes. The cluster analysis illustrated that the whole body, upper limb and lower limb coordination patterning of each individual’s successful spikes were similar to their faulty spikes. Range of motion patterning also demonstrated no differences in kinematics between spike outcomes. Further, the upper limb angular velocity patterning of the players’ successful/faulty spikes were similar. The SPM analysis portrayed significant differences between the normalized upper limb angular velocities from 35% to 45% and from 76% to 100% of the spike movement. Although the lower limb angular velocities are vital for achieving higher jumps in volleyball spikes, the results of this study portrayed that the upper limb angular velocities distinguish the differences between successful and faulty spikes among the attackers. This confirms the fact that volleyball coaches should shift their focus toward the upper limb velocity and coordination training for higher success rates in spiking for volleyball attackers.

Upper limb biomechanical differences in volleyball spikes among young female players

“…Prior to the data reconstruction and analysis, each spiking trial was trimmed from the start of the plant phase to the ball-hitting moment. 14 The data reconstruction and marker labeling were conducted using Vicon® Nexus software (Version 2.10.0, Oxford Metrics, Oxford, UK). The patten, spline, and rigid body methods were used to fill the missed markers (not more than 10 frames).…”

“…Interval of interest in the data collection was defined from the start of the plant phase to the ball-hitting moment. 14 Therefore, the limitation of this study would be due to the absence of ball speed. However, the prior objective of the study was to provide upper limb time-series differences in kinematics and muscle activity during the VS.…”

“…wrist). 14,15 The velocity of both proximal and distal segments is in proportion; nevertheless, the final velocity of the ball corresponds most closely to the velocity in the last distal segment (i.e. wrist).…”

“…12 To understand the upper limb kinematics pattern differences in each crucial movement phase during volleyball spiking across the players, the VS has been divided into three phases: wind-up, cocking, and acceleration. 13,14 The wind-up phase begins with the athlete's shoulder abduction and extension. The cocking phase then begins with the initiation of external shoulder rotation.…”

Profiling the Physical Performance of Young Boxers with Unsupervised Machine Learning: A Cross-Sectional Study

“…In line with the aims and design of these profiling studies, relevant and robust analysis techniques are required. For instance, the use of unsupervised machine learning algorithms [ 16 ], such as clustering analysis [ 17 , 18 , 19 ], take into account the natural grouping structure of the subjects. This improves statistical accuracy/efficiency and allows the identification of patterns and subgroups useful for decision-making and planning interventions.…”