# Cognitive-Motor Integration in Assessing Bimanual Motor Skills

arXiv: https://arxiv.org/abs/2404.10889 · HTML: https://mlsanity.com/paper/2404.10889/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 62 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 18 of 18)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Network In Network](https://mlsanity.com/paper/1312.4400/) (2013)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://mlsanity.com/paper/1803.02579/) (2018)
- [Tool Detection and Operative Skill Assessment in Surgical Videos Using Region-Based Convolutional Neural Networks](https://mlsanity.com/paper/1802.08774/) (2018)
- [Video-based surgical skill assessment using 3D convolutional neural networks](https://mlsanity.com/paper/1903.02306/) (2019)
- [Detection and Localization of Robotic Tools in Robot-Assisted Surgery Videos Using Deep Neural Networks for Region Proposal and Detection](https://mlsanity.com/paper/2008.00936/) (2020)
- [Evaluating surgical skills from kinematic data using convolutional neural networks](https://mlsanity.com/paper/1806.02750/) (2018)
- [How Much Can We Really Trust You? Towards Simple, Interpretable Trust Quantification Metrics for Deep Neural Networks](https://mlsanity.com/paper/2009.05835/) (2020)
- [Accurate and interpretable evaluation of surgical skills from kinematic data using fully convolutional neural networks](https://mlsanity.com/paper/1908.07319/) (2019)
- [Deep Learning in fNIRS: A review](https://mlsanity.com/paper/2201.13371/) (2022)
- [Deep Neural Networks for the Assessment of Surgical Skills: A Systematic Review](https://mlsanity.com/paper/2103.05113/) (2021)
- [Video-based Formative and Summative Assessment of Surgical Tasks using Deep Learning](https://mlsanity.com/paper/2203.09589/) (2022)
- [Where Does Trust Break Down? A Quantitative Trust Analysis of Deep Neural Networks via Trust Matrix and Conditional Trust Densities](https://mlsanity.com/paper/2009.14701/) (2020)
- [Joint Surgical Gesture and Task Classification with Multi-Task and Multimodal Learning](https://mlsanity.com/paper/1805.00721/) (2018)
- [Who's Better? Who's Best? Pairwise Deep Ranking for Skill Determination](https://mlsanity.com/paper/1703.09913/) (2017)
- [Knowledge-based Analysis for Mortality Prediction from CT Images](https://mlsanity.com/paper/1902.07687/) (2019)
- [One-shot skill assessment in high-stakes domains with limited data via meta learning](https://mlsanity.com/paper/2301.00812/) (2022)

## Abstract (arXiv)

Accurate assessment of bimanual motor skills is essential across various professions, yet, traditional methods often rely on subjective assessments or focus solely on motor actions, overlooking the integral role of cognitive processes. This study introduces a novel approach by leveraging deep neural networks (DNNs) to analyze and integrate both cognitive decision-making and motor execution. We tested this methodology by assessing laparoscopic surgery skills within the Fundamentals of Laparoscopic Surgery program, which is a prerequisite for general surgery certification. Utilizing video capture of motor actions and non-invasive functional near-infrared spectroscopy (fNIRS) for measuring neural activations, our approach precisely classifies subjects by expertise level and predicts FLS behavioral performance scores, significantly surpassing traditional single-modality assessments.