The application of data analysis methods for surface electromyography in shot putting and sprinting

Muscles are the key drivers in any human movement. Since the muscles generate the forces and consequently the impulses to move the athlete from one position to another, it can be useful to study the muscle activity during sports movements to help with optimisation of technique, injury prevention and performance enhancements. Due to recent advances in electromyography (EMG) technologies, muscle activity in sports movement such as shot putting and overground sprinting can now be acquired using wireless surface mount sensors. Previously the use of tethered devices restricted the movements which could be analysed. The aim of this research was to investigate data analysis methods for use with EMG. There is a need to develop an in depth understanding of what EMG data can convey by understanding muscle activations and patterns in various sports movements and techniques. The research has been implemented by conducting a literature review, a survey and experimental studies to examine EMG signals on shot putting, sprinting and to understand cross-talk. There has been significant work done in understanding the biomechanics of sprinting, with emphasis on kinematics. The literature review on muscle activities in sprinting highlighted the need for wireless devices to allow testing of athletes in ecologically valid environments, rather than on a treadmill which offers little comparison with the environment of the sprinter, and proposed that there existed a bias on the muscles studied which may have been due to technology constraints of tethered systems. The survey of biomechanists gave an insight into the sensor devices utilised, the types of experimental studies being undertaken and the specifications desired in these devices. The study of muscle activations during the glide technique in shot put delivered meaningful activation patterns which coincided with key movements in the technique and augmented previously known kinematic data and anecdotal evidence. The study on muscle activations during maximal sprinting returned similar results, the 50% threshold provided information on the higher volume of muscle activity and these bursts of activity also coincided with key kinematic events. The use of independent component analysis (ICA) was examined to reduce cross-talk during sporting movements and recreating EMG signals due incorrectly positioned electrodes. Few studies have examined ICA with myoelectric signals. This research applies ICA to EMG signals during isometric contractions; small increases in correlation were found in some cases between the output signals and the ideal signals. The data analysis methods used in this research along with the supporting studies may prove to be a vital aid in supporting practitioners, coaches and athletes in the analysis of shot putting and sprinting using muscle activations and patterns. The thresholding methods used in this work may be useful in future studies to distinguish between low and high volumes of EMG activity in sports movements. It is recommended that future studies examine the muscle activity of specific exercises and compare the activity to that of sports movements to determine which exercises are most suitable in training and for pre-activation. The ICA algorithm should be examined further, to analyse isotonic movements.