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Título : A Generalized Model for Indoor Location Estimation Using Environmental Sound from Human Activity Recognition
Autor : 429892
Fecha de publicación : 27-feb-2018
Editorial : MDPI Publishers
Resumen : The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Severalapproacheshavebeenproposedtotacklethisproblem. Inthispaper,wepresentthe development of an indoor location system which relies on the human activity recognition approach, usingsoundasaninformationsourcetoinfertheindoorlocationbasedonthecontextualinformation of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the locationof the user. Weevaluate the quality of the resulting model in terms ofsensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct.
Descripción : The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Severalapproacheshavebeenproposedtotacklethisproblem. Inthispaper,wepresentthe development of an indoor location system which relies on the human activity recognition approach, usingsoundasaninformationsourcetoinfertheindoorlocationbasedonthecontextualinformation of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the locationof the user. Weevaluate the quality of the resulting model in terms ofsensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct.
URI : http://ricaxcan.uaz.edu.mx/jspui/handle/20.500.11845/1640
ISSN : 2220-9964
Otros identificadores : info:eu-repo/semantics/publishedVersion
Aparece en las colecciones: *Documentos Académicos*-- M. en Ciencias de la Ing.

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