We propose use of deep learning for full-body motion sensing, which significantly increases efficiency in calibration and estimation. The sensing suit is made of stretchable fabric and contains 20 soft strain sensors distributed on both the upper and the lower extremities. Three athletic motions were tested with a human subject, and the proposed learning-based calibration and mapping method showed a higher accuracy than traditional methods that are mainly based on mathematical estimation, such as linear regression. For this work, we collaborate with Prof. Yong-Lae Park’s group at Seoul National University.
Related publications
1.D Kim, J Kwon, S Han, Y-L Park, S Jo, Deep Full-Body Motion Network (DFM-Net) for a Soft Wearable Motion Sensing Suit, IEEE/ASME T Mechatronics, 24(1): 56-66, 2019 [LINK] [PDF] [CODE].
