We present a gait motion generating method using only two microfluidic sensors. We select appropriate sensor positions with consideration of the deformation patterns of the lower-limb skins and mutual interference with soft actuators. A semi-supervised deep learning model is proposed to reduce the size of calibration data. We evaluate the performance of the proposed model with various walking speeds. We collaborate with Prof. Yong-Lae Park’s group at Seoul National University.


Related publications

1. D Kim, M Kim, J Kwon, Y-L Park, S Jo, Semi-Supervised Gait Generation with Two Microfluidic Soft Sensors, IEEE RA-L, 4(3): 2501-2507,  2019 [LINK] [PDF]