Application of Machine Learning Methods for Human Gait Analysis

Keyphrases

• 3D Measurement 33%
• 3D Motion 33%
• Aided INS 33%
• Clinical Gait 33%
• Data Logger 33%
• Data Processing Software 33%
• Deep Learning 33%
• Deep Learning Methods 33%
• Foot Pressure Data 33%
• Force Curve 33%
• Forward Analysis 33%
• Gait 33%
• Gait Metrics 33%
• Gait Parameter Estimation 33%
• Gait Parameters 33%
• Gait Segmentation 66%
• Generalized Predictor 33%
• Ground Contact Time 66%
• Human Body Movement 33%
• Human Gait Analysis 100%
• Human Motion 33%
• Indirect Estimation 66%
• Learning Solutions 33%
• Machine Leaning 33%
• Machine Learning Applications 100%
• Machine Learning Approach 33%
• Machine Learning Techniques 100%
• Motion Parameters 33%
• Open Environment 33%
• Outdoor Running 33%
• Performance Analysis 33%
• Prediction Model 33%
• Pressure Insole 33%
• Running Environment 33%
• Segmentation-based 33%
• Spatiotemporal Gait Parameters 33%
• Stride Duration 33%
• Stride Length 33%
• Training Model 33%
• Two Dimensional 33%
• Vertical Analysis 33%
• Vertical Ground Reaction Force 100%
• Vertical Oscillation 33%
• Vertical Velocity 33%

INIS

• applications 100%
• capture 25%
• computer codes 25%
• curves 25%
• data 50%
• data processing 25%
• devices 25%
• environment 50%
• equipment 25%
• gps 25%
• humans 100%
• laboratories 25%
• learning 50%
• length 25%
• machine learning 100%
• metrics 25%
• motion 50%
• oscillations 25%
• outdoors 50%
• performance 50%
• prediction 25%
• solutions 25%
• speed 25%
• tracks 25%
• variations 25%
• velocity 25%

Engineering

• Contact Time 66%
• Deep Learning Method 66%
• Gait Analysis 100%
• Global Positioning System 33%
• Ground Contact 66%
• Human Motions 33%
• Inertial Navigation System 33%
• Learning Approach 33%
• Learning System 33%
• Machine Learning Method 100%
• Metrics 33%
• Parameter Estimation 33%
• Performance Analysis 33%
• Pressure Data 33%
• Processing Software 33%
• Reaction Force 100%
• Two Dimensional 33%