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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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  • Olatunji Omisore ,
  • Wenke Duan ,
  • Wenjing Du ,
  • Shipeng Han ,
  • Toluwanimi Akinyemi ,
  • Lei Wang
Olatunji Omisore
Shenzhen Institutes of Advanced Technology

Corresponding Author:[email protected]

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Wenke Duan
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Wenjing Du
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Shipeng Han
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Toluwanimi Akinyemi
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Abstract

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.