# Real-Time Context-aware Detection of Unsafe Events in Robot-Assisted Surgery

arXiv: https://arxiv.org/abs/2005.03611 · HTML: https://mlsanity.com/paper/2005.03611/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 May 2020.

## Built on by (most cited first, 11 of 11)

- [Gesture Recognition in Robotic Surgery: a Review](https://mlsanity.com/paper/2102.00027/) (2021)
- [Data-driven Design of Context-aware Monitors for Hazard Prediction in Artificial Pancreas Systems](https://mlsanity.com/paper/2104.02545/) (2021)
- [Robustness Testing of Data and Knowledge Driven Anomaly Detection in Cyber-Physical Systems](https://mlsanity.com/paper/2204.09183/) (2022)
- [Runtime Detection of Executional Errors in Robot-Assisted Surgery](https://mlsanity.com/paper/2203.00737/) (2022)
- [Think Step by Step: Chain-of-Gesture Prompting for Error Detection in Robotic Surgical Videos](https://mlsanity.com/paper/2406.19217/) (2024)
- [Towards Surgical Context Inference and Translation to Gestures](https://mlsanity.com/paper/2302.14237/) (2023)
- [Robotic Scene Segmentation with Memory Network for Runtime Surgical Context Inference](https://mlsanity.com/paper/2308.12789/) (2023)
- [Intelligent machines work in unstructured environments by differential neuromorphic computing](https://mlsanity.com/paper/2309.08835/) (2023)
- [MiDAS: A Multimodal Data Acquisition System and Dataset for Robot-Assisted Minimally Invasive Surgery](https://mlsanity.com/paper/2602.12407/) (2026)
- [Context-Aware Risk Estimation in Home Environments: A Probabilistic Framework for Service Robots](https://mlsanity.com/paper/2508.19788/) (2025)
- [Real-Time Multimodal Activity-Aware Error Detection in Robot-Assisted Surgery](https://mlsanity.com/paper/2606.23593/) (2026)

## Abstract (arXiv)

Cyber-physical systems for robotic surgery have enabled minimally invasive procedures with increased precision and shorter hospitalization. However, with increasing complexity and connectivity of software and major involvement of human operators in the supervision of surgical robots, there remain significant challenges in ensuring patient safety. This paper presents a safety monitoring system that, given the knowledge of the surgical task being performed by the surgeon, can detect safety-critical events in real-time. Our approach integrates a surgical gesture classifier that infers the operational context from the time-series kinematics data of the robot with a library of erroneous gesture classifiers that given a surgical gesture can detect unsafe events. Our experiments using data from two surgical platforms show that the proposed system can detect unsafe events caused by accidental or malicious faults within an average reaction time window of 1,693 milliseconds and F1 score of 0.88 and human errors within an average reaction time window of 57 milliseconds and F1 score of 0.76.