Open AccessFall detection in walking robots by multi-way principal component analysis
Author(s) -
J. G. Daniël Karssen,
Martijn Wisse
Publication year - 2008
Publication title -
robotica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.476
H-Index - 64
eISSN - 1469-8668
pISSN - 0263-5747
DOI - 10.1017/s0263574708004645
Subject(s) - principal component analysis , computer science , artificial intelligence , component (thermodynamics) , pattern recognition (psychology) , computer vision , thermodynamics , physics
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%.
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