Non-Linear Chaotic Features-Based Human Activity Recognition

Human activity recognition (HAR) has vital applications in human-computer interaction, somatosensory games, and motion monitoring, etc. On the basis of the human motion accelerate sensor data, through a nonlinear analysis of the human motion time series, a novel method for HAR that is based on non-linear chaotic features is proposed in this paper. First, the C-C method and G-P algorithm are used to, respectively, compute the optimal delay time and embedding dimension. Additionally, a Reconstructed Phase Space (RPS) is formed while using time-delay embedding for the human accelerometer motion sensor data. Subsequently, a two-dimensional chaotic feature matrix is constructed, where the chaotic feature is composed of the correlation dimension and largest Lyapunov exponent (LLE) of attractor trajectory in the RPS. Next, the classification algorithms are used in order to classify and recognize the two different activity classes, i.e., basic and transitional activities. The experimental results show that the chaotic feature has a higher accuracy than traditional time and frequency domain features.

Automated summary

This paper proposes a novel method for human activity recognition based on non-linear chaotic features using human motion accelerometer sensor data. Optimal delay time and embedding dimension are calculated using the C-C method and G-P algorithm, and a two-dimensional chaotic feature matrix is constructed for activity classification. Experimental results show that the chaotic feature outperforms traditional time and frequency domain features in accuracy.