A Framework for Validation and Monitoring of Robotic Surgery Systems

The objective of this research is the development of a framework for assessing the reliability and safety of robotic surgery systems during development, field testing, and general deployment. The framework uses accurate simulations to assess pre-clinical reliability before deployment. After deployment, the framework uses data collection through online monitoring of the system as it is being used in the field, followed by analysis to obtain assessments of operational reliability and safety. The framework also supports post-market surveillance of these systems for reassessing reliability and safety after system maintenance.

In addition to contributing to robotics by providing a practical basis for ensuring the reliability and safety of robotic surgery systems, the research makes contributions to cyber-physical systems engineering and machine learning. For the former, new methods to characterize the safe usage-envelope of a system, to revalidate systems after maintenance, and to localize hardware and software defects are being developed. In machine learning, new methods to efficiently use samples to detect and label adverse events via active learning are being developed.

Award ID: 1035602