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.