A Human Activity Recognition Scheme Using Mobile Smartphones Based on Varying Orientations and Positions

With the popularity and portability of smartphones, it is feasible to continuously measure and monitor people’s daily activities with a variety of sensors built into smartphones, such as health diagnosis and motion detection. Due to the flexibility of smartphones in practice, the acquisition of data from sensors with position and orientation effects leads to a decrease in the accuracy of activity recognition. In this article, we propose a robust human activity recognition system based on extended Kalman filtering (EKF) with adaptive local and global principal component analysis (ALGPCA), temporal channel interaction and attention neural network (TCIANet). The EKF with ALGPCA (EKF-ALGPCA) sensor data preprocessing method is proposed for eliminating the effects of orientation and position changes. An efficient human activity recognition neural network model TCIANet is proposed, in which the temporal features of collected data can also be extracted during feature extraction using the time channel interaction (TCI) module in the convolutional module, and the temporal attention module is used after the convolutional feature extraction to deal with the long-time dependence of temporal data. The experimental data collected with two kinds of smartphones, Xiaomi and Huawei, in different orientations and positions, are used to prove the effectiveness of the modification method in continuous behavior recognition under the condition of changing the orientation and position of smartphones.