Force Shadows: An Online Method to Estimate and Distribute Vertical Ground Reaction Forces from Kinematic Data

Area of Science:

• Biomechanics
• Human Motion Analysis
• Kinetic Modeling

Background:

• Kinetic models of human motion require accurate boundary conditions, often defined by ground reaction forces (GRF).
• Reconstructing GRF, particularly during the double support phase (multiple foot-ground contacts), presents a significant challenge due to ill-defined predictions.
• Existing methods struggle to provide unique and reliable GRF estimations for all phases of motion.

Purpose of the Study:

• To develop a novel method for reconstructing and distributing vertical ground reaction forces (vGRF) to individual feet.
• To address the challenge of vGRF estimation during the double support phase using solely kinematic data.
• To provide a unique and computationally efficient solution applicable to arbitrary human motions.

Main Methods:

• Introduction of the biomechanically inspired Force Shadow Method (FSM).
• Development of a kinematic-based function incorporating anatomical foot models and mimicking hip muscle activation.
• Utilizing inertial human motion capture for data acquisition.

Main Results:

• The Force Shadow Method (FSM) successfully reconstructs and distributes vertical GRF (vGRF) for each foot, including during double support.
• High correlations (0.39–0.94 for double support, 0.83–0.87 for walking) were achieved when comparing FSM estimations with pressure plate measurements.
• The method demonstrates applicability beyond laboratory settings and offers low computational complexity for online estimation.

Conclusions:

• The Force Shadow Method (FSM) provides a robust and unique solution for estimating vertical ground reaction forces (vGRF) from kinematic data.
• This approach overcomes limitations in reconstructing vGRF during the double support phase.
• The method's efficiency and accuracy suggest significant potential for real-time biomechanical analysis and applications in various human motion scenarios.