Reinforcement learning environments with musculoskeletal models
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Updated
Jan 24, 2022 - Python
Reinforcement learning environments with musculoskeletal models
Markerless kinematics with any cameras — From 2D Pose estimation to 3D OpenSim motion
Tools for the Cleveland State Human Motion and Control Lab
An optimization framework that links CasADi, Ipopt, ACADOS and biorbd for Optimal Control Problem
An Open-Source Python Package to Facilitate Research in Biomechanics
Extract and visualize gait data
preparing meshes for FEA using Blender
An efficient task-space capacity calculation package for robotics and biomechanics
python Convention Gait Model
Pose2Sim visualizer tool — Import cameras and OpenSim data in Blender
MyoChallenge 23 -- Towards Human-Level Dexterity and Agility
Computational Biophysics for Medicine in 3D Slicer
Gym environments for NeuroMechFly in various physics simulators
Natural Coordinates with python for biomechanics
Python interface of C3Dserver software for reading and editing C3D motion capture files
GapFill: gap filling functions for motion capture marker data
Usage of direct linear transformation (camera calibration and point reconstruction) for accurate motion analysis in biomechanical applications.
This is an automatic implementation of the Rapid Upper Limb Assessment by using data from IMU sensors
Validation of Leap Motion Controller for finger kinematics
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