Add to ORKGThis study aims to investigate the efficacy of a stacking approach to estimate parameters in treadmill running. Nineteen participants ran on a treadmill at self-selected pace. Ground reaction force and kinematic data were collected. Stacking in machine learning was used to estimate the peak vertical ground reaction force and step time. Good agreement was observed in the test data set for predicted and measured values of the peak vertical ground reaction force component and step time where the ICC values were 0.85 and 0.99 respectively. This suggests stacking may be a feasible approach to estimate kinetic and kinematic parameters during treadmill running
The majority of human gait analysis methods are limited to clinical gait laboratories. The calculati...
Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical r...
Ground reaction forces (GRFs) describe how runners interact with their surroundings and provide the ...
Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biom...
Background: One major drawback in measuring ground-reaction forces during running is that it is time...
Background: Gait event detection of the initial contact and toe off is essential for running gait an...
The majority of human gait analysis methods are limited to clinical gait laboratories. The calculati...
Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical r...
The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parame...
This study aimed to (1) construct a statistical model (SMM) based on the duty factor (DF) to estimat...
The purpose of this study was to compare different approaches for the estimation of biomechanical lo...
The purpose of this study was to use machine learning (i.e., artificial neural network – ANN), to pr...
Duty factor (DF) and step frequency (SF) were previously defined as the key running pattern determin...
Duty factor (DF) and step frequency (SF) were previously defined as the key running pattern determin...
This study explored the use of artificial neural networks in the estimation of runners\u27 kinetics ...
The majority of human gait analysis methods are limited to clinical gait laboratories. The calculati...
Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical r...
Ground reaction forces (GRFs) describe how runners interact with their surroundings and provide the ...
Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biom...
Background: One major drawback in measuring ground-reaction forces during running is that it is time...
Background: Gait event detection of the initial contact and toe off is essential for running gait an...
The majority of human gait analysis methods are limited to clinical gait laboratories. The calculati...
Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical r...
The vertical ground reaction force (vGRF) and its passive and active peaks are important gait parame...
This study aimed to (1) construct a statistical model (SMM) based on the duty factor (DF) to estimat...
The purpose of this study was to compare different approaches for the estimation of biomechanical lo...
The purpose of this study was to use machine learning (i.e., artificial neural network – ANN), to pr...
Duty factor (DF) and step frequency (SF) were previously defined as the key running pattern determin...
Duty factor (DF) and step frequency (SF) were previously defined as the key running pattern determin...
This study explored the use of artificial neural networks in the estimation of runners\u27 kinetics ...
The majority of human gait analysis methods are limited to clinical gait laboratories. The calculati...
Ground reaction forces are often used by sport scientists and clinicians to analyze the mechanical r...
Ground reaction forces (GRFs) describe how runners interact with their surroundings and provide the ...