Summary of Cognitive-motor Integration in Assessing Bimanual Motor Skills, by Erim Yanik and Xavier Intes and Suvranu De
Cognitive-Motor Integration in Assessing Bimanual Motor Skills
by Erim Yanik, Xavier Intes, Suvranu De
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel deep learning-based approach to assess bimanual motor skills, integrating cognitive decision-making and motor execution. By leveraging deep neural networks (DNNs) and combining video capture of motor actions with functional near-infrared spectroscopy (fNIRS), the methodology accurately classifies subjects by expertise level and predicts behavioral performance scores in laparoscopic surgery tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The new approach helps improve accuracy in assessing bimanual motor skills, which is essential for various professions. The method combines video capture of motor actions with neural activations measured using fNIRS to precisely classify subjects by expertise level and predict behavioral performance scores. |
Keywords
* Artificial intelligence * Deep learning
