victor369basu/MyosuiteDDQN

In this repository, we try to solve musculoskeletal tasks with `Double DQN reinforcement learning` by using a `transformer` model has been used as the base model architecture.

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Experimental

This project helps researchers simulate and understand how the complex human hand moves and interacts with objects, even under various physical conditions. It takes detailed musculoskeletal models of the hand as input and produces optimized control strategies that mimic natural, dexterous movements. Physical therapists, prosthetics designers, and biomechanics researchers can use this to explore motor control and rehabilitation.

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Use this if you need to simulate and analyze the coordination of human hand muscles and bones for tasks like grasping or manipulation, especially when studying conditions like muscle weakness, fatigue, or post-surgical changes.

Not ideal if you are looking for real-time robotic control or direct patient therapy tools, as this focuses on simulation and theoretical motor control research.

biomechanics-research motor-control-simulation rehabilitation-engineering prosthetics-design sarcopenia-modeling
Stale 6m No Package No Dependents
Maintenance 0 / 25
Adoption 6 / 25
Maturity 16 / 25
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17

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Language

Python

License

Apache-2.0

Last pushed

Nov 07, 2023

Commits (30d)

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