A graph based method for timed up & go test qualification using inertial sensors

A graph based classifier is proposed to recognize the different time phases of the up & go test based on signals collected by an inertial sensor set on a person chest. This test being a sequential set of actions, a graph is used to model it and enforce the classification algorithm to estimate a solution with this constraint. The graph is described by a Markov chain A(m). Based on the hidden Markov model theoretical framework which by construction fits with this kind of modelling, the proposed method extends this framework to other classifiers: Bayes, LDA and SVM are discussed in this paper. These classifiers and their graph enforced versions are applied and their results compared to the analysis of the timed up & go test to recognize its different phases.