Fall detection in walking robots by multi-way principal component analysis

Abstract

SUMMARY Large disturbances can cause a biped to fall. If an upcoming fall can be detected, damage can be minimized or the fall can be prevented. We introduce the multi-way principal component analysis (MPCA) method for the detection of upcoming falls. We study the detection capability of the MPCA method in a simulation study with the simplest walking model. The results of this study show that the MPCA method is able to predict a fall up to four steps in advance in the case of single disturbances. In the case of random disturbances the MPCA method has a successful detection probability of up to 90%.