full-body human musculoskeletal model with crutches  (OpenSim Ltd)

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The proposed DRL method for the dynamic optimization of the forward dynamics of a human musculoskeletal model during stairs or ramp ascent.

Article Snippet: To summarize, the main contributions of this paper are: To show that DRL, based on PPO in combination with imitation learning, can successfully teach a physics-based human musculoskeletal model in OpenSim to ascend stairs and ramps, with the future goal of using such architecture for the control of lower-limb prostheses.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The physics-based human musculoskeletal model developed in this study. Figures from left to right: side view facing the right leg, front view, side view facing the left leg, and back view.

Article Snippet: To summarize, the main contributions of this paper are: To show that DRL, based on PPO in combination with imitation learning, can successfully teach a physics-based human musculoskeletal model in OpenSim to ascend stairs and ramps, with the future goal of using such architecture for the control of lower-limb prostheses.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The state variables of the human musculoskeletal model.

Article Snippet: To summarize, the main contributions of this paper are: To show that DRL, based on PPO in combination with imitation learning, can successfully teach a physics-based human musculoskeletal model in OpenSim to ascend stairs and ramps, with the future goal of using such architecture for the control of lower-limb prostheses.

Techniques: Plasmid Preparation

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The reward obtained during the learning process of the human musculoskeletal model to ascend the stairs.

Article Snippet: To summarize, the main contributions of this paper are: To show that DRL, based on PPO in combination with imitation learning, can successfully teach a physics-based human musculoskeletal model in OpenSim to ascend stairs and ramps, with the future goal of using such architecture for the control of lower-limb prostheses.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The reward obtained during the learning process of the human musculoskeletal model to ascend the ramp.

Article Snippet: To summarize, the main contributions of this paper are: To show that DRL, based on PPO in combination with imitation learning, can successfully teach a physics-based human musculoskeletal model in OpenSim to ascend stairs and ramps, with the future goal of using such architecture for the control of lower-limb prostheses.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The proposed DRL method for the dynamic optimization of the forward dynamics of a human musculoskeletal model during stairs or ramp ascent.

Article Snippet: The 3D lower-extremity physics-based human musculoskeletal model used in this study was developed in OpenSim 3.3 (model version number 3000) as an .osim file.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The physics-based human musculoskeletal model developed in this study. Figures from left to right: side view facing the right leg, front view, side view facing the left leg, and back view.

Article Snippet: The 3D lower-extremity physics-based human musculoskeletal model used in this study was developed in OpenSim 3.3 (model version number 3000) as an .osim file.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The state variables of the human musculoskeletal model.

Article Snippet: The 3D lower-extremity physics-based human musculoskeletal model used in this study was developed in OpenSim 3.3 (model version number 3000) as an .osim file.

Techniques: Plasmid Preparation

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The reward obtained during the learning process of the human musculoskeletal model to ascend the stairs.

Article Snippet: The 3D lower-extremity physics-based human musculoskeletal model used in this study was developed in OpenSim 3.3 (model version number 3000) as an .osim file.

Techniques:

Journal: Sensors (Basel, Switzerland)

Article Title: Learning to Ascend Stairs and Ramps: Deep Reinforcement Learning for a Physics-Based Human Musculoskeletal Model

doi: 10.3390/s22218479

Figure Lengend Snippet: The reward obtained during the learning process of the human musculoskeletal model to ascend the ramp.

Article Snippet: The 3D lower-extremity physics-based human musculoskeletal model used in this study was developed in OpenSim 3.3 (model version number 3000) as an .osim file.

Techniques: