Sensor network oriented human motion capture via wearable intelligent system

TLDR

Inertial measurement units mounted on the foot can improve positioning accuracy in human motion capture systems. The study develops a lightweight, low‑cost wireless inertial motion capture system that simultaneously reconstructs body attitude and displacement, introducing a novel 3‑D reconstruction method distinct from traditional systems. The system employs a network of 15 low‑cost inertial sensors on key limbs, performs initial alignment, uses a zero‑speed update algorithm for foot displacement, fuses data via gradient‑descent motion reconstruction, and combines posture and foot trajectory through unconstrained root traversal to achieve synchronous reconstruction. Experimental results show that estimation errors are well controlled and the captured motion patterns closely match real movements.

Abstract

Using inertial measurement units mounted on foot is a feasible approach to improve the positioning accuracy for the human motion capture system. This paper presents a lightweight and low cost wireless inertial motion capture system for the simultaneous reconstruction of human body attitude and displacement. First of all, the device is based on human sensor networks and distributes 15 sensor nodes on the key human limbs. Then, after an initial sensor alignment with the reduced error, a zero-speed update algorithm is used to calculate foot displacement. In addition, to constantly update the human posture information, a kind of motion reconstruction method based on the gradient descent method was used to fuse the sensor data. Finally, a new method of three-dimensional human body reconstruction is proposed, which is different from the traditional motion capture system. Through unconstrained traversal of the root, the human posture and foot trajectory are combined to realize the synchronous reconstruction of posture and displacement. It is concluded from the experiment results that the estimation errors are well controlled, and motion patterns are consistent with the actual situation.