Fall-KAN: Fall impact time estimation Kolmogorov-Arnold Network

A new application based on the Kolmogorov-Arnold Network (KAN) for estimating the time of impact of the fall is presented (Fall-KAN). The SisFall data set that contains fall scenarios with real subjects is used, and the approach is employed to estimate the impact time with the ground during a fall. The performance of the proposed approach is evaluated by comparing it with Linear Regression, Regression Tree, SVM, and MLP using the SisFall dataset in terms of RMSE and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$R^{2}$</tex>. Fall-KAN achieved a RMSE of approximately 150 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$ms$</tex> after a single training epoch, which is more than 3% lower than other ML methods. It also demonstrated superior robustness in estimating the time of impact during a fall, with a low initial prediction error and a small settling value only close to the impact.