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

Human activity recognition using wearable devices has been actively investigated in a wide range of applications. Most of them, however, either focus on simple activities wherein whole body movement is involved or require a variety of sensors to identify daily activities. In this study, we propose a human activity recognition system that collects data from an off-the-shelf smartwatch and uses an artificial neural network for classification. The proposed system is further enhanced using location information. We consider 11 activities, including both simple and daily activities. Experimental results show that various activities can be classified with an accuracy of 95%.

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