[1]ZHU Jun,ZHENG Wendong,GE Quanbo,et al.Dynamic imaging of touch tracking based on multi-frame reconstruction in electrical impedance tomography[J].CAAI Transactions on Intelligent Systems,2024,19(6):1458-1467.[doi:10.11992/tis.202308027]
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Dynamic imaging of touch tracking based on multi-frame reconstruction in electrical impedance tomography

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 19 Number of periods: 2024 6 Page number: 1458-1467 Column: 学术论文—智能系统 Public date: 2024-12-05
Title:
Dynamic imaging of touch tracking based on multi-frame reconstruction in electrical impedance tomography
Author(s):
ZHU Jun1;  ZHENG Wendong2;  GE Quanbo1;  LIU Huaping2
1. School of Automation, Nanjing University of Information Science and Technology, Nanjing 210044, China;
2. Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
Keywords:
electrical impedance tomography; tactile sensing; touch tracking; multi-frame; image reconstruction; dynamic imaging; trajectory writing; human-machine interaction
CLC:
TP391
DOI:
10.11992/tis.202308027
Abstract:
Touch sensing is of considerable importance to human–machine interaction. A touch interaction method integrating electrical impedance tomography (EIT) is proposed to enhance the real-time responsiveness and accuracy of touch sensing in human–machine interaction. This method employs a dynamic image reconstruction approach based on multiple data frames to construct a multiframe regularization model and fully mine the temporal correlation information between adjacent frames. An EIT-based resistive tactile sensing system is also designed to validate the effectiveness of this method. The system, which features a multilayer composite structure, responds to changes in touch pressure in real time. Experiments are conducted to discretely and continuously measure touch pressure and track touch points. Experimental results indicate that, compared with traditional single-frame static imaging methods, the proposed multiframe reconstruction algorithm notably reduces the positional error, deformation, and artifacts in the reconstructed images during motion, achieving a higher degree of conformity with the actual touch positions. This approach offers a promising solution for touch sensing in the field of human–machine interaction.
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