With the development of Internet of Things (IoT) technology and various sensing technologies, some newer ways of perceiving people and the environment have emerged. Commercial wearable sensing devices integrate a variety of sensors that can play a significant role in motion capture and behavioral analysis. This paper proposes a solution for recognizing human motion in ping-pong using a commercial smart watch. We developed a data acquisition system based on the IoT architecture to obtain data relating to areas such as acceleration, angular velocity, and magnetic induction of the watch. Based on the features of the extracted data, experiments were performed using major machine learning classification algorithms including k-nearest neighbor, support vector machine, Naive Bayes, logistic regression, decision tree, and random forest. The results show that the random forest has the best performance, reaching a recognition rate of 97.80%. In addition, we designed a simple convolutional neural network to compare its performance in this problem. The network consists of two convolutional layers, two pooling layers, and two fully connected layers, and it uses data with no extracted features. The results show that it achieves an accuracy of 87.55%. This research can provide training assistance for amateur ping-pong players.
Smart watches have become one of the most representative devices in wearable devices because of their unique advantages such as integration, portability, reliability, stability, universality and low environmental dependence. At present, it is mainly used for the monitoring of health indicators such as human heart rate. Whole-body inertial sensing devices cannot meet the actual needs of the general public for virtual sports because of high prices and inconvenient wear. In this paper, a single piece smart watch is used to study the recognition of the most common actions in table tennis which is a kind of fast-moving sport and has many fans through an improved convolution neural network model. The final experimental results show that the recognition accuracy reaches 95.46%, which can basically meet the needs of amateurs' motionSports.
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