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Application of Machine Learning Methods for Human Gait Analysis
Dharmendra Sharma
Research output
:
Thesis
›
Master's thesis
Overview
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Dive into the research topics of 'Application of Machine Learning Methods for Human Gait Analysis'. Together they form a unique fingerprint.
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Keyphrases
Machine Learning Techniques
100%
Vertical Ground Reaction Force
100%
Human Gait Analysis
100%
Machine Learning Applications
100%
Indirect Estimation
66%
Ground Contact Time
66%
Gait Segmentation
66%
Two Dimensional
33%
Machine Learning Approach
33%
Performance Analysis
33%
Gait
33%
Open Environment
33%
Human Body Movement
33%
Prediction Model
33%
Data Logger
33%
Vertical Velocity
33%
Vertical Oscillation
33%
3D Measurement
33%
Training Model
33%
Learning Solutions
33%
Force Curve
33%
Human Motion
33%
Deep Learning
33%
Motion Parameters
33%
Segmentation-based
33%
Running Environment
33%
Data Processing Software
33%
Deep Learning Methods
33%
3D Motion
33%
Pressure Insole
33%
Generalized Predictor
33%
Spatiotemporal Gait Parameters
33%
Gait Metrics
33%
Foot Pressure Data
33%
Gait Parameter Estimation
33%
Machine Leaning
33%
Stride Length
33%
Outdoor Running
33%
Gait Parameters
33%
Stride Duration
33%
Clinical Gait
33%
Vertical Analysis
33%
Aided INS
33%
Forward Analysis
33%
INIS
applications
100%
machine learning
100%
humans
100%
performance
50%
data
50%
environment
50%
motion
50%
learning
50%
outdoors
50%
solutions
25%
metrics
25%
devices
25%
speed
25%
length
25%
computer codes
25%
tracks
25%
variations
25%
equipment
25%
prediction
25%
velocity
25%
capture
25%
laboratories
25%
curves
25%
gps
25%
oscillations
25%
data processing
25%
Engineering
Machine Learning Method
100%
Reaction Force
100%
Gait Analysis
100%
Ground Contact
66%
Contact Time
66%
Deep Learning Method
66%
Performance Analysis
33%
Two Dimensional
33%
Parameter Estimation
33%
Inertial Navigation System
33%
Pressure Data
33%
Learning Approach
33%
Metrics
33%
Processing Software
33%
Learning System
33%
Global Positioning System
33%
Human Motions
33%