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A model (in)validation approach to gait classification.

María Cecilla Mazzaro·Mario Sznaier·Octavia Camps
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Abstract

This paper addresses the problem of human gait classification from a robust model (in)validation perspective. The main idea is to associate to each class of gaits a nominal model, subject to bounded uncertainty and measurement noise. In this context, the problem of recognizing an activity from a sequence of frames can be formulated as the problem of determining whether this sequence could have been generated by a given (model, uncertainty, and noise) triple. By exploiting interpolation theory, this problem can be recast into a nonconvex optimization. In order to efficiently solve it, we propose two convex relaxations, one deterministic and one stochastic. As we illustrate experimentally, these relaxations achieve over 83 percent and 86 percent success rates, respectively, even in the face of noisy data.

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AlgorithmsArtificial IntelligenceBiometryCluster AnalysisComputer SimulationGaitHumansImage EnhancementImage Interpretation, Computer-AssistedInformation Storage and RetrievalJointsLegModels, BiologicalPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificityVideo RecordingClinical TrialJournal ArticleResearch Support, U.S. Gov't, Non-P.H.S.Validation Study

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BibTeXRIS

María Cecilla Mazzaro, Mario Sznaier, Octavia Camps. 2005. A model (in)validation approach to gait classification.. https://doi.org/10.1109/tpami.2005.210

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