Title :
A graph based method for timed up & go test qualification using inertial sensors
Author :
Jallon, Pierre ; Dupre, Benjamin ; Antonakios, Michel
Author_Institution :
CEA, MINATEC Campus, Grenoble, France
Abstract :
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.
Keywords :
belief networks; hidden Markov models; pattern classification; sensors; signal classification; support vector machines; LDA; Markov chain; SVM; classification algorithm; graph based classifier; hidden Markov model; inertial sensor; Bayesian methods; Hidden Markov models; Kernel; Magnetic sensors; Markov processes; Support vector machines; Classifiers; HMM; LDA; SVM; graph based method;
Conference_Titel :
Acoustics, Speech and Signal Processing (ICASSP), 2011 IEEE International Conference on
Conference_Location :
Prague
Print_ISBN :
978-1-4577-0538-0
Electronic_ISBN :
1520-6149
DOI :
10.1109/ICASSP.2011.5946497