Data Feature Extraction Method of Wearable Sensor Based on Convolutional Neural Network

With the rapid development of society and science technology, human health issues have attracted much attention due to wearable devices' ability to provide high-quality sports, health, and activity monitoring services. This paper proposes a method for feature extraction of wearable sensor data based on a convolutional neural network (CNN). First, it uses the Kalman filter to fuse the data to obtain a preliminary state estimation, and then it uses CNN to recognize human behavior, thereby obtaining the corresponding behavior set. Moreover, this paper conducts experiments on 5 datasets. The experimental results show that the method in this paper extracts data features at multiple scales while fully maintaining data independence, can effectively extract corresponding feature data, and has strong generalization ability, which can adapt to different learning tasks.