Capturing 3D foot models is important for applications such as manufacturing customized shoes and creating clubfoot orthotics. In this paper, we propose a novel prototype, Sensock, to offer a fully wearable solution for the task of 3D foot reconstruction. The prototype consists of four soft stretchable sensors, made from silk fibroin yarn. We identify four characteristic foot girths based on the existing knowledge of foot anatomy, and measure their lengths with the resistance value of the stretchable sensors. A learning-based model is trained offline and maps the foot girths to the corresponding 3D foot shapes. We compare our method with existing solutions using red–green–blue (RGB) or RGBD (RGB-depth) cameras, and show the advantages of our method in terms of both efficiency and accuracy. In the user experiment, we find that the relative error of Sensock is lower than 0.55%. It performs consistently across different trials and is considered comfortable and suitable for long-term wearing.
The ACM CHI Conference on Human Factors in Computing Systems