Abstract
The rapid development of motion capture technologies has greatly increased the use of human motion data in many applications. This has increased the demand to have an effective means to systematically analyze those massive data in order to understand human motion variation patterns. This paper studies one typical type of motion data, which are recorded as multi-stream trajectories of human joints. Such a high dimensional multi-stream data structure makes it difficult to directly perform visual comparisons or simply apply conventional methods such as PCA to capture the variation of human motion patterns. In this paper, a high order array (tensor) is suggested for data representation, based on which the Uncorrelated Multilinear Principal Component Analysis (UMPCA) is applied to analyze the variation of human motion patterns. A simulation study is presented to show the superiority of UMPCA over PCA in preserving the cross-correlation among multi-stream trajectories. The effectiveness of UMPCA is also demonstrated using a case study for analyzing vehicle ingress test data.
•The UMPCA method preserves the multistream structure of the motion data.•That UMPCA can capture the cross-correlation among different signals and important variation patterns more effectively than PCA.•The motion pattern that has the highest variation is associated with ingress discomfort.•The information about the aforementioned motion pattern can be used to guide and improve vehicle design.•The proposed method has other applications in athletic performance evaluation and training, medical diagnosis.