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

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.<br>

Ähnliche Arbeiten