Smart orthoses hold great potential for intelligent rehabilitation monitoring and training. However, most of these electronic assistive devices are typically too difficult for daily use and challenging to modify to accommodate variations in body shape and medical needs. For existing clinicians, the customization pipeline of these smart devices imposes significant learning costs. This paper introduces ThermoFit, an end-to-end design and fabrication pipeline for thermoforming smart orthoses that adheres to the clinically accepted procedure. ThermoFit enables the shapes and electronics positions of smart orthoses to conform to bodies and allows rapid iteration by integrating low-cost Low-Temperature Thermoplastics (LTTPs) with custom metamaterial structures and electronic components. Specifically, three types of metamaterial structures are used in LTTPs to reduce the wrinkles caused by the thermoforming process and to permit component position adjustment and joint movement. A design tool prototype aids in generating metamaterial patterns and optimizing component placement and circuit routing. Three applications show that ThermoFit can be shaped on bodies to different wearables. Finally, a hands-on study with a clinician verifies the user-friendliness of thermoforming smart orthosis, and technical evaluations demonstrate fabrication efficiency and electronic continuity.

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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.