https://github.com/r614/bmeg350

a ml algorithm to analyze in-vivo ndt motion
https://github.com/r614/bmeg350

biomechanics biomechanics-analysis machine-learning

Last synced: about 6 hours ago
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a ml algorithm to analyze in-vivo ndt motion

• Host: GitHub
• URL: https://github.com/r614/bmeg350
• Owner: r614
• Created: 2021-04-11T19:54:46.000Z (over 3 years ago)
• Default Branch: master
• Last Pushed: 2021-06-03T05:32:00.000Z (over 3 years ago)
• Last Synced: 2024-11-05T05:42:40.465Z (about 2 months ago)
• Topics: biomechanics, biomechanics-analysis, machine-learning
• Language: Jupyter Notebook
• Homepage:
• Size: 2.16 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# What is this?

A small exploration I did for BMEG 350 (Human Physiology), trying to find a way to compare neutral density target motion in in-vivo ferrets and human cadavers.  

# How should I run this?

Install the requirements using `pip install -r requirements.txt` 

Walk through the notebook, a large part of it is undocumented but it essentially tries to use different curve fitting approaches to model the 3 dimensional 

(technically 2 dimensional, because we only had access to figures from the paper) motion of the NDT