Motion capture technologies reconstruct human movements and have wide-ranging applications. Mainstream research on motion capture can be divided into vision-based methods and inertial measurement unit (IMU)-based methods. The vision-based methods capture complex 3D geometrical deformations with high accuracy, but they rely on expensive optical equipment and suffer from the line-of-sight occlusion problem. IMU-based methods are lightweight but hard to capture fine-grained 3D deformations. In this work, we present a configurable self-sensing IMU sensor network to bridge the gap between the vision-based and IMU-based methods. To achieve this, we propose a novel kinematic chain model based on the four-bar linkage to describe the minimum deformation process of 3D deformations. We also introduce three geometric priors, obtained from the initial shape, material properties and motion features, to assist the kinematic chain model in reconstructing deformations and overcome the data sparsity problem. Additionally, to further enhance the accuracy of deformation capture, we propose a fabrication method to customize 3D sensor networks for different objects. We introduce origami-inspired thinking to achieve the customization process, which constructs 3D sensor networks through a 3D-2D-3D digital-physical transition. The experimental results demonstrate that our method achieves comparable performance with state-of-the-art methods.
<p>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.</p>
<p>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.</p>
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