Real-Time Multimodal Activity-Aware Error Detection in Robot-Assisted Surgery

• URL: http://arxiv.org/abs/2606.23593v1
• Date: Mon, 22 Jun 2026 17:01:45 GMT
• Title: Real-Time Multimodal Activity-Aware Error Detection in Robot-Assisted Surgery
• Abstract summary: We propose a unified framework for executional error detection using multimodal input, including video, kinematics, and textual prompts.<n>We achieve up to 5% and 16.6% F1 score improvements over state-of-the-art baselines on the JIGSAWS and SAR-RARP50 datasets.
• Score: 6.047276283488404
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Robot-assisted minimally invasive surgery improves surgical precision but introduces complexity, making technical error detection essential for ensuring patient safety. Current executional error detection methods using video data often overlook fine-grained contextual descriptions of activities and error types within the hierarchical structure of surgical procedures. They also under-utilize complementary multimodal information. We propose a unified framework for executional error detection that leverages multimodal input, including video, kinematics, and descriptive textual prompts. Through activity prompting, we integrate descriptive language in gesture-level activities, instrument-object interactions, and error definitions. We also introduce activity-aware visual embeddings derived from vision encoders pretrained on surgical activity labels to compare the effectiveness of contrastive language-image embeddings with traditional image-based embeddings for error detection. By seamlessly integrating kinematic data with video and textual modalities, our framework significantly improves error detection performance. Achieving up to 5\% and 16.6\% F1 score improvements over state-of-the-art baselines on the JIGSAWS and SAR-RARP50 datasets, respectively, we demonstrate the value of combining curated textual prompts with multimodal data for accurate error detection.

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