{"id":3264,"date":"2025-08-04T13:07:07","date_gmt":"2025-08-04T13:07:07","guid":{"rendered":"http:\/\/www.labren.org\/mm\/?p=3264"},"modified":"2025-08-05T12:58:55","modified_gmt":"2025-08-05T12:58:55","slug":"%f0%9f%8c%9f-exciting-news-our-latest-research-work-entitled-rasec-rescaling-acquisition-strategy-with-energy-constraints-under-fusion-kernel-for-active-incision-recommendation-in-tracheotomy","status":"publish","type":"post","link":"http:\/\/www.labren.org\/mm\/news\/%f0%9f%8c%9f-exciting-news-our-latest-research-work-entitled-rasec-rescaling-acquisition-strategy-with-energy-constraints-under-fusion-kernel-for-active-incision-recommendation-in-tracheotomy\/","title":{"rendered":"\ud83c\udf1f 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)."},"content":{"rendered":"\n

\ud83d\udd0d 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.<\/p>\n\n\n\n

\ud83d\udca1 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.<\/p>\n\n\n\n

\ud83d\udd2c 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.<\/p>\n\n\n\n

\ud83d\ude80 The results showcase RASEC’s excellence in exploration efficiency, cost-effectiveness, and incision localization accuracy in real robot-assisted tracheotomy procedures.<\/p>\n\n\n\n

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

Paper is available at https:\/\/lnkd.in\/gEgmaDVj<\/p>\n\n\n\n

\"\"<\/figure>\n","protected":false},"excerpt":{"rendered":"

\ud83d\udd0d 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\u2026 Continue reading<\/a><\/p>\n","protected":false},"author":17,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"ngg_post_thumbnail":0,"footnotes":""},"categories":[4],"tags":[160,126,159,157,161,163,162,158],"class_list":["post-3264","post","type-post","status-publish","format-standard","hentry","category-news","tag-bayesianoptimization","tag-cuhk","tag-informativedecision","tag-medicalrobotics","tag-nuaa","tag-sgh","tag-susteck","tag-tactilesensing"],"_links":{"self":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/posts\/3264","targetHints":{"allow":["GET"]}}],"collection":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/users\/17"}],"replies":[{"embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/comments?post=3264"}],"version-history":[{"count":1,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/posts\/3264\/revisions"}],"predecessor-version":[{"id":3265,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/posts\/3264\/revisions\/3265"}],"wp:attachment":[{"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/media?parent=3264"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/categories?post=3264"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.labren.org\/mm\/wp-json\/wp\/v2\/tags?post=3264"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}