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Communication Dans Un Congrès Année : 2017

Swallowing sound recognition at home using GMM

Résumé

Aiming for autonomous living for the people after a stroke is the challenge these days especially for swallowing disorders or dysphagia. It is in this context that the e-swallhome project proposes to develop tools, from hospital care until the patient returns home, which are able to monitor in real time the process of swallowing. This paper proposes a non-invasive acoustic based method to differentiate between swallowing sounds and other sounds in normal ambient environment during food intake. Gaussian Mixture Models (GMM) adapted through the Expectation Maximization (EM) algorithm was employed for classification and test combination using the leave-one-out approach according to the small amount of data in our database. Results provide a good recognition rate of 100 %
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Dates et versions

hal-01692422 , version 1 (25-01-2018)

Identifiants

  • HAL Id : hal-01692422 , version 1

Citer

Hajer Khlaifi, Dan Istrate, Jacques Demongeot, Jérôme Boudy, Dhafer Malouche. Swallowing sound recognition at home using GMM. Journées d'Etude sur la TéléSANté, 6ème edition, Pôle Capteurs, Université d'Orléans, May 2017, Bourges, France. ⟨hal-01692422⟩
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