Print Email Facebook Twitter Fall detection in walking robots by multi-way principal component analysis Title Fall detection in walking robots by multi-way principal component analysis Author Karssen, J.G. Wisse, M. Faculty Applied Sciences Department Biomechanical Engineering Date 2008-05-08 Abstract 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 showthat 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%. Subject BipedsLegged robotsHumanoid robotsRobot dynamicsPose estimation and registration To reference this document use: http://resolver.tudelft.nl/uuid:6237c2b9-f70e-4e97-be1a-c782b3fd3104 Publisher Cambridge University Press Source Robotica, 27 (2009) Part of collection Institutional Repository Document type journal article Rights (c) 2008 Cambridge University Press Files PDF karssen2008.pdf 391.1 KB Close viewer /islandora/object/uuid:6237c2b9-f70e-4e97-be1a-c782b3fd3104/datastream/OBJ/view