# Video and Accelerometer-Based Motion Analysis for Automated Surgical Skills Assessment

arXiv: https://arxiv.org/abs/1702.07772 · HTML: https://mlsanity.com/paper/1702.07772/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Feb 2017.

## Built on by (most cited first, 28 of 28)

- [Ego-Exo4D: Understanding Skilled Human Activity from First- and Third-Person Perspectives](https://mlsanity.com/paper/2311.18259/) (2023)
- [Group-aware Contrastive Regression for Action Quality Assessment](https://mlsanity.com/paper/2108.07797/) (2021)
- [Towards Unified Surgical Skill Assessment](https://mlsanity.com/paper/2106.01035/) (2021)
- [LOGO: A Long-Form Video Dataset for Group Action Quality Assessment](https://mlsanity.com/paper/2404.05029/) (2024)
- [Action Quality Assessment via Hierarchical Pose-guided Multi-stage Contrastive Regression](https://mlsanity.com/paper/2501.03674/) (2025)
- [Multimodal Action Quality Assessment](https://mlsanity.com/paper/2402.09444/) (2024)
- [Narrative Action Evaluation with Prompt-Guided Multimodal Interaction](https://mlsanity.com/paper/2404.14471/) (2024)
- [A Survey of Video-based Action Quality Assessment](https://mlsanity.com/paper/2204.09271/) (2022)
- [Surgical Skill Assessment via Video Semantic Aggregation](https://mlsanity.com/paper/2208.02611/) (2022)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Video-based assessment of intraoperative surgical skill](https://mlsanity.com/paper/2205.06416/) (2022)
- [Objective Surgical Skills Assessment and Tool Localization: Results from the MICCAI 2021 SimSurgSkill Challenge](https://mlsanity.com/paper/2212.04448/) (2022)
- [CPR-Coach: Recognizing Composite Error Actions based on Single-class Training](https://mlsanity.com/paper/2309.11718/) (2023)
- [A comprehensive survey on recent deep learning-based methods applied to surgical data](https://mlsanity.com/paper/2209.01435/) (2022)
- [Attention-Driven Multimodal Alignment for Long-term Action Quality Assessment](https://mlsanity.com/paper/2507.21945/) (2025)
- [SINGA-Easy: An Easy-to-Use Framework for MultiModal Analysis](https://mlsanity.com/paper/2108.02572/) (2021)
- [VisualTextRank: Unsupervised Graph-based Content Extraction for Automating Ad Text to Image Search](https://mlsanity.com/paper/2108.02725/) (2021)
- [Text is NOT Enough: Integrating Visual Impressions into Open-domain Dialogue Generation](https://mlsanity.com/paper/2109.05778/) (2021)
- [Discovering Personalized Semantics for Soft Attributes in Recommender Systems using Concept Activation Vectors](https://mlsanity.com/paper/2202.02830/) (2022)
- [Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks](https://mlsanity.com/paper/2203.11011/) (2022)
- [Explanation-Guided Fairness Testing through Genetic Algorithm](https://mlsanity.com/paper/2205.08335/) (2022)
- [Using Graph Neural Networks for Program Termination](https://mlsanity.com/paper/2207.14648/) (2022)
- [Tribe or Not? Critical Inspection of Group Differences Using TribalGram](https://mlsanity.com/paper/2303.09664/) (2023)
- [Video-based Surgical Skill Assessment using Tree-based Gaussian Process Classifier](https://mlsanity.com/paper/2312.10208/) (2023)
- [A vision-language model and platform for temporally mapping surgery from video](https://mlsanity.com/paper/2603.22583/) (2026)
- [How good was my shot? Quantifying Player Skill Level in Table Tennis](https://mlsanity.com/paper/2603.25736/) (2026)
- [Video-based Surgical Skill Assessment Using Dynamics-and-Uncertainty-Aware Tree-based Gaussian Process Classifier](https://mlsanity.com/paper/2609.24619/) (2026)
- [Semantic RGB--Depth Based Surgical Skill Assessment in Microscopic Stereo Videos](https://mlsanity.com/paper/2610.01205/) (2026)

## Abstract (arXiv)

Purpose: Basic surgical skills of suturing and knot tying are an essential part of medical training. Having an automated system for surgical skills assessment could help save experts time and improve training efficiency. There have been some recent attempts at automated surgical skills assessment using either video analysis or acceleration data. In this paper, we present a novel approach for automated assessment of OSATS based surgical skills and provide an analysis of different features on multi-modal data (video and accelerometer data). Methods: We conduct the largest study, to the best of our knowledge, for basic surgical skills assessment on a dataset that contained video and accelerometer data for suturing and knot-tying tasks. We introduce "entropy based" features - Approximate Entropy (ApEn) and Cross-Approximate Entropy (XApEn), which quantify the amount of predictability and regularity of fluctuations in time-series data. The proposed features are compared to existing methods of Sequential Motion Texture (SMT), Discrete Cosine Transform (DCT) and Discrete Fourier Transform (DFT), for surgical skills assessment. Results: We report average performance of different features across all applicable OSATS criteria for suturing and knot tying tasks. Our analysis shows that the proposed entropy based features out-perform previous state-of-the-art methods using video data. For accelerometer data, our method performs better for suturing only. We also show that fusion of video and acceleration features can improve overall performance with the proposed entropy features achieving highest accuracy. Conclusions: Automated surgical skills assessment can be achieved with high accuracy using the proposed entropy features. Such a system can significantly improve the efficiency of surgical training in medical schools and teaching hospitals.