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
Discord webhook example for Second Life/Opensimulator
We developed a method animating a statistical 3D human model for biomechanical analysis to increase accessibility for non-experts, like patients, athletes, or designers.
Scripts and models to optimize musculotendon parameters in musculoskeletal models.
Toolbox for using multiple cameras from intrinsic calculations to reconstructing kinematics
Materials to reproduce the results of our paper about 3D muscle modelling.
Code for "Synthesis of Biologically Realistic Human Motion Using Joint Torque Actuation", SIGGRAPH 2019
Anaximander the Grid Cartographer is a map tile generator for Halcyon-based servers using C#, utilizing parallel processing and advanced tree data structures to generate only what is needed in as short a time as possible.
An OpenAI-Gym Package for Training and Testing Reinforcement Learning algorithms with OpenSim Models
Data (bone geometries and gait analysis data) and scripts to reproduce the results and figures of the scientific publication specified in the README file.
Apply Reinforcement Learning (RL) to enable prosthetics to calibrate with differences between humans and differences between walking environments
Neuromusculoskeletal Modeling (NMSM) Pipeline codebase
Master Thesis project at Linköping University by Erik Sars and Sophia Cedermalm. The digital twin developed by ISB research group at Linköping University is extended with biomechanical modelling of the human body. It is done by a dance sequence. Later acquired muscle forces will be analysed.
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