Motion capture data segmentation using Riemannian manifold learning

Abstract

Due to the inherent nonlinear nature of data, traditional linear methods have some limitations in finding the intrinsic dimensions of motion capture (Mo‐cap) data. Mo‐cap data are more in line with the characteristics of the manifold. Assuming that the data are initially a low‐dimensional manifold and uniformly sampled in high‐dimensional Euclidean space, manifold learning recovers low‐dimensional manifold structures from high‐dimensional sampled data. This paper proposes an automatic segmentation method based on geodesics by introducing a Riemannian manifold. We convert Mo‐cap data from Euler angles into quaternions, calculate the intrinsic mean of the motion sequence, hemispherize quaternions, and use logarithmic and exponential mapping to calculate geodesic distances instead of quaternions. The experimental results show that the algorithms can achieve automatic segmentation and have a better segmentation effect.

Humanoid navigation: A firefly based approach

“…He et al 38 focused on model identification and design of control strategy for a humanoid robot based on Newton-Euler formulation. Bin et al 39 proposed use of Riemannian manifold learning approach for analyzing motion capture data segmentation. Kumar et al [40][41][42][43] designed several motion control algorithms for efficient navigation in complicated terrains.…”