Applying LDA-based pattern recognition to predict isometric shoulder and elbow torque generation in individuals with chronic stroke with moderate to severe motor impairment

Keyphrases

• Moderate to Severe 100%
• Pattern Recognition 100%
• Severe Motor Disability 100%
• Chronic Stroke 100%
• Shoulder Torque 100%
• Elbow Torque 100%
• Torque Generation 100%
• Linear Discriminant Analysis 100%
• Shoulder Abduction 80%
• Eight Class 80%
• Abnormal Synergy 60%
• Internal Rotation 60%
• External Rotation 60%
• Adduction 60%
• Classification Accuracy 40%
• Negative Effects 40%
• Control Signal 40%
• Load Cell 40%
• Flexion Synergy 40%
• Computationally Efficient 20%
• Control Strategy 20%
• EMG Signal 20%
• Degrees of Freedom 20%
• Elbow Joint 20%
• Elbow Flexion 20%
• Joint Torque 20%
• Internal-external 20%
• Wrist 20%
• Elbow 20%
• Teasing 20%
• Six Degrees of Freedom 20%
• Movement Impairment 20%
• Elbow Extension 20%
• Abduction 20%
• Cellular Data 20%
• Flexion 20%
• Persons with Amputation 20%
• Torque Coupling 20%
• Multi-joint 20%
• Assistive Devices 20%
• Shoulder Joint 20%
• Misclassification 20%
• Feature Combination 20%
• Control Options 20%
• Joint Flexion 20%
• Myoelectric Prosthesis 20%
• Torque Signal 20%
• EMG Data 20%
• Wearable Exoskeleton 20%
• Multijoint Task 20%
• Major Stroke 20%
• Eight-channel 20%
• Exoskeletal 20%
• Movement Intention 20%
• Offline Classification 20%
• Horizontal Abduction 20%
• Horizontal Adduction 20%

Engineering

• Joints (Structural Components) 100%
• Pattern Recognition 100%
• Degree of Freedom 50%
• Load Cell 50%
• Classification Accuracy 50%
• Negative Effect 50%
• Control Signal 50%
• Control Strategy 25%
• Misclassification 25%
• Control Option 25%
• Prosthesis 25%