Ground Reaction Forces and Moments Estimation from Embedded Insoles using Machine Learning Regression Models

The objective of this paper was to assess the possibility of estimating 6D ground reaction forces and moments during continuous double supports exercises using instru-mented force insoles. Thanks to machine learning regression, the study evaluated the performance of an embedded solution in comparison to a reference laboratory grade force plate. While insoles were validated in the context of gait, few studies investigated their accuracy in estimating ground reaction forces and moments for rehabilitation exercises with both feet on the ground. Thus, popular ankle and hip strategies, squat and hula hoop excercises were investigated. The estimation accuracy was reported with a low average error of 1.6 ± 0.3% of the body weight and 1.2 ± 0.3% of the body weight times the body height along with a moderate correlation when using solely features extracted from insoles measurements. These results demonstrated the possibility of using embedded solutions to estimate the full ground reaction wrench if the learning process was applied for each specific task separately.