Scientific Computing in Biomechanics and Motor Control

Marcos Duarte

This repository is a collection of lectures and code on scientific computing and data analysis in Biomechanics and Motor Control. The lectures are written using the IPython Notebook, part of the Python ecosystem for scientific computing. You can view these lectures in different ways: simply read online by clicking on the links below; or download a single notebook or all the stuff or yet fork this entire repository using the GitHub resources and run the notebooks in your computer or in the cloud.
This material is far from finished.

• Biomechanics

• Python for scientific computing
• How to install Python
• Python tutorial
• Version control with Git and GitHub

• Scalar and vector
• Matrix
• Confidence and prediction intervals

• Prediction ellipse and prediction ellipsoid

• Curve fitting
• Propagation of uncertainty
• Frequency analysis
• Data filtering in signal processing

• Residual analysis for the optimal cutoff frequency

• Change detection

• Detection of peaks
• Detection of onset
• Detection of changes using the Cumulative Sum (CUSUM)

• Time normalization of data
• Ensemble average
• Open files in C3D format

• Kinematics

• Frame of reference
• Kinematics of a particle
• Spatial and temporal characteristcs
• Minimum jerk hypothesis
• Angular kinematics (2D)
• Kinematic chain
• Rigid-body transformations (2D)
• Rigid-body transformations (3D)
  • Determining rigid body transformation using the SVD algorithm

• Kinetics

• Fundamental concepts
• Body segment parameters
• Free body diagram
• Gait analysis (2D)

• The inverted pendulum model of the human standing posture
• Measurements in stabilography

This work is licensed under the Creative Commons Attribution 4.0 International License.