Human Limb Motion Segmentation by PCA-ARMA Methods
Lingfeng Liu, Feng Mei, Qian Hu · 2021 IEEE 21st International Conference on Communication Technology (ICCT) · 2021
Effective segmentation of motion capture (MoCap) data is becoming a crucial issue for further human motion posture and behavior analysis, which requires both robustness and computation efficiency in the algorithm design. In this paper, we propose an unsupervised segmentation algorithm based on limb bone partition angle body structural representation, principle component analysis (PCA) for sequences dimension reduction, and autoregressive moving average (ARMA) model fitting. The collected MoCap data is converted into the angle sequence formed by the human limb bone partition segment and the central spine segment. The PCA algorithm is used to reduce the dimension of limbs sequence into a two-dimensional sequence of upper limb and lower limb. The limb angle sequences are matched by the ARMA model, and the segmentation points of the limb angle sequences are distinguished by analyzing the good of fitness of the ARMA model. A medial filtering algorithm is proposed to ensemble the segmentation results from individual limb motion sequences. A set of MoCap measurements are also conducted to evaluate the algorithm including typical body motions collected from subjects of different heights, and are labeled by manual segmentation.