Fast Human Motion reconstruction from sparse inertial measurement units considering the human shape.

ABSTRACT: Inertial Measurement Unit-based methods have great potential in capturing motion in large-scale and complex environments with many people. Sparse Inertial Measurement Unit-based methods have more research value due to their simplicity and flexibility. However, improving the computational efficiency and reducing latency in such methods are challenging. In this paper, we propose Fast Inertial Poser, which is a full body motion estimation deep neural network based on 6 inertial measurement units considering body parameters. We design a network architecture based on recurrent neural networks according to the kinematics tree. This method introduces human body shape information by the causality of observations and eliminates the dependence on future frames. During the estimation of joint positio

SUBMITTER: Xiao X 

PROVIDER: S-EPMC10948800 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature