Generating synthetic gait patterns based on benchmark datasets for controlling prosthetic legs

Keyphrases

• Pattern-based 100%
• Gait Pattern 100%
• Prosthetic Limb 100%
• Benchmark Dataset 100%
• Deep Neural Network 57%
• Amputation 42%
• Gait Data 42%
• Ambulation 42%
• Training Data 42%
• Generative Adversarial Networks 42%
• Conditional Generative Adversarial Network (cGAN) 42%
• Neural Network Controller 28%
• Vertical Load 28%
• Level Controller 28%
• Learnability 14%
• Controller 14%
• Control Method 14%
• Control Parameters 14%
• Locomotion 14%
• Knee 14%
• Learning-based 14%
• User Input 14%
• Ankle 14%
• Effective Training 14%
• Level Walking 14%
• Lower Limb Joints 14%
• Joint Angle 14%
• Thigh 14%
• Impedance Control 14%
• Stiffness Coefficient 14%
• Structural Similarity Index 14%
• Coefficient of Determination 14%
• Damping Coefficient 14%
• Impedance Parameters 14%
• Neural Network Control 14%
• Efficient Training 14%
• Sitting Motion 14%

Engineering

• Gait Pattern 100%
• Prosthetics 100%
• Deep Neural Network 57%
• Angle Joint 14%
• Control Parameter 14%
• Ground Level 14%
• Obtaining Data 14%
• Damping Coefficient 14%
• Impedance Parameter 14%
• Structural Similarity 14%
• Effective Training 14%
• Network Model 14%
• Similarity Index 14%

Earth and Planetary Sciences

• Similarity Index 100%