A Scalable and Wearable Self-Sensing IMU Sensor Network for Personalized
Human Motion and Deformation Capture
In recent years, the wearable motion capture technology has been
developed rapidly in various applications. However, conventional methods
usually emphasize capturing the whole body skeleton or limb movements,
without considering the personalized human body data and fine-grained
deformation information. Thus, it is important to develop a proper
wearable motion and deformation capture system based on the personalized
human body data to provide people with more customized and immersive
experiences. In this paper, a rapid and scalable construction method of
the wearable inertial measurement unit (IMU) sensor network is proposed
to generate personalized wearable solutions for people with different
body types. Additionally, a robust self-sensing algorithm based on the
IMU sensor network is proposed to reconstruct not only the whole body or
limb movements but also the fine-grained muscle deformations. To
validate the performance, we evaluate the accuracy and robustness of our
method. In the accuracy evaluation, the average measurement error is
3.90mm, less than 1.80% of the test model size (180mm × 150mm × 72mm).
In the robustness evaluation, the average measurement error is 6.15mm.
Finally, an application on personalized arm motion and deformation
capture demonstrates the feasibility and applicability of the proposed
self-sensing IMU sensor network.