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

Abstract

Kinetic models of human motion rely on boundary conditions which are defined by the interaction of the body with its environment. In the simplest case, this interaction is limited to the foot contact with the ground and is given by the so called ground reaction force (GRF). A major challenge in the reconstruction of GRF from kinematic data is the double support phase, referring to the state with multiple ground contacts. In this case, the GRF prediction is not well defined. In this work we present an approach to reconstruct and distribute vertical GRF (vGRF) to each foot separately, using only kinematic data. We propose the biomechanically inspired force shadow method (FSM) to obtain a unique solution for any contact phase, including double support, of an arbitrary motion. We create a kinematic based function, model an anatomical foot shape and mimic the effect of hip muscle activations. We compare our estimations with the measurements of a Zebris pressure plate and obtain correlations of 0.39≤r≤0.94 for double support motions and 0.83≤r≤0.87 for a walking motion. The presented data is based on inertial human motion capture, showing the applicability for scenarios outside the laboratory. The proposed approach has low computational complexity and allows for online vGRF estimation.

Differences in the Lateral and Vertical Jump Performances of Elite Male Basketball Players—An Axial Stabilization Training Program

“…Similarly, some studies have confirmed that the RFD is indicated by the slope of the force-time curve. The lower limbs could exert a greater force in a shorter period of time, which also shows that the number of muscle fibers determines the amount of force generated [45][46][47]. Some studies have pointed out that an excellent vertical jump is dominated by the hip joint.…”

Precision strength training: Data-driven artificial intelligence approach to strength and conditioning

“…In sports performance laboratories, the force plates are routinely used [526,759], but recently low-cost insoles [195,250,378,612,795,864] and Nintendo R Wii Balance Board TM [543] have been investigated as alternatives as the cost of force plates can be prohibitive in some cases. Recently, nonlearning mathematical [863], and deep learning models have been developed to approximate the forces generated by the athlete, solely from nonkinetic measurements, such as from kinematic data [323,375,724]. These approaches naturally require development datasets with the both modalities (kinematic and kinetic) in order to validate the approximation accuracy of the model when deploying to real-world use just with the kinematic modality.…”