Tag: TactileSensing

We are excited to share our latest research entitled โ€œDisentangling Contact Location for Stretchable Tactile Sensors from Soft Waveguide Ultrasonic Scatter Signalsโ€ collaborated by Zhiheng Li and Yuan Lin, has been accepted by Advanced Intelligent Systems!

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Flexible tactile sensors have garnered significant attention due to their flexible, lightweight, and comfortable nature, catering to the growing demands in various applications such as human-machine interfaces, healthcare, and robotics. However, it remains a challenge to achieve precise contact location sensing that is decoupled from sensor strain and touching forces. Thus, this paper proposes a novel data-driven approach for force contact location sensing (FCLS), based on scatter signals (SS) of the ultrasonic waveguide, with the influence of sensor strain and forces. This method utilizes deep CNNs to fuse local and global features of the soft waveguide ultrasonic SS and an MLP to perform regression modeling on the fused features and FCL, thereby obtaining FCL information. The experimental results indicate that the accuracy of the proposed FCLS method has an MAE loss of 0.627 mm and an MRE loss of 3.19%.

The full paper will be available at: https://lnkd.in/gKr22Uvi

Co-Authors: Zhiheng Li (CUHK), Yuan Lin (SJTU), Peter B. Shull (SJTU) and Hongliang Ren (CUHK). The first two authors contributed equally to this work.

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๐ŸŒŸ Exciting News! Our latest research work, entitled “RASEC: Rescaling Acquisition Strategy With Energy Constraints Under Fusion Kernel for Active Incision Recommendation in Tracheotomy”, has been accepted by IEEE Transactions on Automation Science and Engineering (T-ASE).

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๐Ÿ” In this paper, we unveil an innovative autonomous palpation-based acquisition strategy – RASEC, designed for the tracheal region. RASEC predicts the next acquisition point interactively, maximizing expected information and minimizing palpation procedure costs. By leveraging a Gaussian Process (GP) to model tissue hardness distribution and anatomical information as a guiding input for medical robots, RASEC revolutionizes robot-assisted subtasks in tracheotomy.

๐Ÿ’ก We introduce a dynamic tactile sensor based on resonant frequency to measure tissue hardness at millimeter-scale precision, ensuring secure interactions. By exploring kernel fusion techniques blending Squared Exponential (SE) and Ornstein-Uhlenbeck (OU) kernels, and optimizing Bayesian search with larynx anatomical data, we enhance exploration efficiency and accuracy.

๐Ÿ”ฌ Our research considers new factors like tactile sensor movement and robotic base rotation in the acquisition strategy. Simulation and physical phantom experiments demonstrate a remarkable 53.1% reduction in sensor movement and 75.2% reduction in base rotation, with superior algorithmic performance metrics (average precision 0.932, average recall 0.973, average F1 score 0.952) and minimal distance errors (0.423 mm) at a high resolution of 1 mm.

๐Ÿš€ The results showcase RASEC’s excellence in exploration efficiency, cost-effectiveness, and incision localization accuracy in real robot-assisted tracheotomy procedures.

This collaborative work is achieved by WENCHAO YUE, Fan Bai, Jianbang Liu, and Prof Hongliang Ren from The Chinese University of Hong Kong, Prof Feng Ju from Nanjing University of Aeronautics and Astronautics, Prof Max Q.-H. Meng from Southern University of Science and Technology, and Dr. Chwee Ming Lim from Singapore General Hospital.

Paper is available at https://lnkd.in/gEgmaDVj