In recent years, with an increase in the use of smartwatches among wearable devices,\nvarious applications for the device have been developed. However, the realization of a user interface\nis limited by the size and volume of the smartwatch. This study aims to propose a method to classify\nthe userâ??s gestures without the need of an additional input device to improve the user interface.\nThe smartwatch is equipped with an accelerometer, which collects the data and learns and classifies\nthe gesture pattern using a machine learning algorithm. By incorporating the convolution neural\nnetwork (CNN) model, the proposed pattern recognition system has become more accurate than the\nexisting model. The performance analysis results show that the proposed pattern recognition system\ncan classify 10 gesture patterns at an accuracy rate of 97.3%.
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