Abstract
Having a systemic system in recognizing activity in sports is very essential along with enhancing the performance analysis in sport. As the system is required to provide a quality, reliable and unbiased notational data for determining the strength and weakness of field hockey players. Therefore, this study is analysing the accelerometer and gyroscope signal on of the four inertial sensors attached to the upper body chest, waist, right and left wrist and formulate the best model in using the wearable sensor for human activity recognition in the field hockey which are passing, drive, drag flick, dribbling, receiving and tackling. Set of features such as mean, standard deviation, maximum and minimum peak are extracted from each inertial sensor signal as an input vector for classification purpose. Results from the study shows that the recognition using combination of all four sensors achieved the highest performance of 96.7% accuracy; and waist and left wrist is recommended if single sensor based human activity recognition is preferred.
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