The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.

Analysis of human behavior recognition algorithms based on acceleration data

BRUNO, BARBARA;MASTROGIOVANNI, FULVIO;SGORBISSA, ANTONIO;VERNAZZA, TULLIO;ZACCARIA, RENATO UGO RAFFAELE
2013

Abstract

The automatic assessment of the level of independence of a person, based on the recognition of a set of Activities of Daily Living, is among the most challenging research fields in Ambient Intelligence. The article proposes a framework for the recognition of motion primitives, relying on Gaussian Mixture Modeling and Gaussian Mixture Regression for the creation of activity models. A recognition procedure based on Dynamic Time Warping and Mahalanobis distance is found to: (i) ensure good classification results; (ii) exploit the properties of GMM and GMR modeling to allow for an easy run-time recognition; (iii) enhance the consistency of the recognition via the use of a classifier allowing unknown as an answer.
9781467356411
9781467356435
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11567/632196
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