Title Electromyographic patterns during golf swing: activation sequence profiling and prediction of shot effectiveness /
Authors Verikas, Antanas ; Vaiciukynas, Evaldas ; Gelzinis, Adas ; Parker, James ; Olsson, M. Charlotte
DOI 10.3390/s16040592
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Is Part of Sensors.. Basel : MDPI. 2016, vol. 16, iss. 4, art. 592, p. 1-9.. ISSN 1424-8220
Keywords [eng] EMG ; muscle activity onset ; peak detection ; random forest ; decision fusion
Abstract [eng] This study analyzes muscle activity, recorded in an eight-channel electromyographic (EMG) signal stream, during the golf swing using a 7-iron club and exploits information extracted from EMG dynamics to predict the success of the resulting shot. Muscles of the arm and shoulder on both the left and right sides, namely flexor carpi radialis, extensor digitorum communis, rhomboideus and trapezius, are considered for 15 golf players ( 5 shots each). The method using Gaussian filtering is outlined for EMG onset time estimation in each channel and activation sequence profiling. Shots of each player revealed a persistent pattern of muscle activation. Profiles were plotted and insights with respect to player effectiveness were provided. Inspection of EMG dynamics revealed a pair of highest peaks in each channel as the hallmark of golf swing, and a custom application of peak detection for automatic extraction of swing segment was introduced. Various EMG features, encompassing 22 feature sets, were constructed. Feature sets were used individually and also in decision-level fusion for the prediction of shot effectiveness. The prediction of the target attribute, such as club head speed or ball carry distance, was investigated using random forest as the learner in detection and regression tasks. Detection evaluates the personal effectiveness of a shot with respect to the player-specific average, whereas regression estimates the value of target attribute, using EMG features as predictors. Fusion after decision optimization provided the best results: the equal error rate in detection was 24.3% for the speed and 31.7% for the distance; the mean absolute percentage error in regression was 3.2% for the speed and 6.4% for the distance. Proposed EMG feature sets were found to be useful, especially when used in combination. [...].
Published Basel : MDPI
Type Journal article
Language English
Publication date 2016
CC license CC license description