Framework for activity sensing in smart home

Abstract

The ability to accurately identify the different activities of daily living (ADLs) is considered as one of the basis to foster new technological solutions inside the home, including services to prolong independent living of the elderly. However, the automated detection of activities is a challenging research area. Existing activity monitoring systems suffer from some practical limitations, such as acceptance, due to privacy issues, as well as technical limitations, such as the complexity of activity recognition, imbalanced data classes or lack of common and suitable representations of the context information.This Thesis proposes a framework for sensing activities in smart home. The main novelty of this research work lies in the application of different approaches simultaneously to obtain a more accurate and proper detection of activities in real environments. The final framework that represents the mixture of the aforementioned approaches includes the contributions described in the following. Firstly, a characterization of the main activities considered in smart home scenarios is proposed which are targeted to older people's independent living. Secondly, it is proposed to use an ensemble of heterogeneous classifiers to recognize twelve activities, including very short-duration transitional activities usually discarded in most of the previous works. We also specify a novel segmentation method that dynamically detects the appropriate starting position of windows from accelerometer data, for the purposes of activity recognition. Thirdly, a human activity ontology is presented to support explicit and flexible representation of reasoning based on activity context information. This ontology has been developed by following the NeON methodology and it is linked with existing technologies capable to handle real-life aspects such as time, space, relations between activities, etc. III ResumenLa habilidad de identificar de forma precisa las distintas actividades de la vida diaria (ADL, Activity of Daily Life) es considerada como una de las bases que potencian el desarrollo de nuevas soluciones tecnológicas dentro del hogar, incluyendo los servicios para prolongar la vida independiente de las personas de edad avanzada. Sin embargo la detección automática de actividades es un área que aún está en las fases iniciales de investigación. Los sistemas de monitorización de actividades existentes sufren de limitaciones prácticas como la aceptación de los sensores por parte de los usuarios debido a la pérdida de privacidad, y limitaciones tecnológicas como el reconocimiento de actividades complejas, datos no preparados para poder ser procesados adecuadamente o la falta de esquemas de representación de información de contexto comunes y adecuados Esta tesis presenta un marco de trabajo para la detección de actividades en el hogar digital. La principal novedad de la tesis radica en la aplicación de distintas aproximaciones de forma simultánea para obtener una detección de actividades más precisa y acorde a escenarios reales.El ...