A multi-modal approach for activity classification and fall detection

The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adult...

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Detalles Bibliográficos
Autores: Castillo Montoya, José Carlos, Fernández Caballero, Antonio, Carneiro, Davide, Serrano Cuerda, Juan, Neves, José, Novais, Paulo
Tipo de recurso: artículo
Fecha de publicación:2014
País:España
Institución:Universidad de Castilla-La Mancha
Repositorio:RUIdeRA. Repositorio Institucional de la UCLM
OAI Identifier:oai:ruidera.uclm.es:10578/3695
Acceso en línea:http://hdl.handle.net/10578/3695
Access Level:acceso abierto
Palabra clave:Ingenierías
Descripción
Sumario:The society is changing towards a new paradigm in which an increasing number of old adults live alone. In parallel, the incidence of conditions that affect mobility and independence is also rising as a consequence of a longer life expectancy. In this paper, the specific problem of falls of old adults is addressed by devising a technological solution for monitoring these users. Video cameras, accelerometers and GPS sensors are combined in a multi-modal approach to monitor humans inside and outside the domestic environment. Machine learning techniques are used to detect falls and classify activities from accelerometer data. Video feeds and GPS are used to provide location inside and outside the domestic environment. It results in a monitoring solution that does not imply the confinement of the users to a closed environment.

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