Journal: PLoS Computational Biology

Article Title: Computational modelling of muscle fibre operating ranges in the hindlimb of a small ground bird ( Eudromia elegans ), with implications for modelling locomotion in extinct species

doi: 10.1371/journal.pcbi.1008843

Figure Lengend Snippet: This is shown for walking (A) and running (B) trials. Grey regions denote the stance phase, white regions denote the swing phase. Note the strong contributions to external joint moment made by reserves acting about the abduction–adduction and long-axis rotation DOFs of the knee and ankle joints. Also illustrated are the kinematics of the left hindlimb (reversed for visualization) at various parts of the stride cycle.

Article Snippet: A high-fidelity 3-D musculoskeletal model of the pelvis and hindlimbs was developed for use in OpenSim 3.3 [ , ] ( and see ).

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: