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:

Journal: Sensors (Basel, Switzerland)

Article Title: A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

doi: 10.3390/s16122018

Figure Lengend Snippet: OpenSim upper limb musculoskeletal model. This model was developed by Saul et al. . It consists of 7 body segments and 32 muscles across the shoulder, elbow, forearm, and wrist. The muscle kinematics parameters and experimental joint moment can be obtained through this musculoskeletal model.

Article Snippet: Muscle kinematics transforms the joint angle into muscle kinematics parameters such as musculotendon unit (MTU) lengths and moment arms using an upper limb musculoskeletal model [ ] based on OpenSim [ ].

Techniques: Muscles