Improved One-Dimensional Convolutional Neural Networks for Human Motion Recognition

Wearable devices provide an extremely convenient way to collect a large amount of human motion data. In this paper, the human motion recognition method based on wearable devices is studied. We smooth the data to remove the noise caused by additional motion first. After that, the characteristic values that can distinguish the types of activities can be extracted. Then, we propose a human motion recognition method based on the improved one-dimensional convolutional neural networks(1D-CNNs). Compared with other traditional classification and recognition methods, the recognition rates of 11 human motions have been greatly improved. The average accuracy of each activity identification can reach 92.8%, while the average precision and recall can reach 98.7% and 92.8%.