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On Task-specific Autonomy in Robotic Interventions: A Multimodal Learning-based Approach for Multi-level Skill Assessment during Cyborg Catheterization

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posted on 06.11.2021, 14:04 by Olatunji OmisoreOlatunji Omisore, Wenke Duan, Wenjing Du, Shipeng Han, Toluwanimi Akinyemi, Lei Wang
Lack of learning-based methods for characterizing the multimodal data generated during cyborg catheterization hinders the drive towards autonomous robotic control. Also, multiplexing salient features from multiple data-sources can enhance effective assessment and classification of domain skills for apt intelligent surgeon-robot (cyborg) catheterization during intravascular interventions. In this study, task-specific autonomous intervention is envisioned upon an isomorphic master-slave robotic catheter system that exhibit hand defter techniques used in Cath Labs. To drive cyborg catheterization, stacking-based deep neural network is developed for three-level skill assessment.

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Email Address of Submitting Author

omisore@siat.ac.cn

ORCID of Submitting Author

0000-0002-9740-5471

Submitting Author's Institution

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences

Submitting Author's Country

China