Abstract
The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities.
Highlights
Sensor-based activity recognition [1] is a very relevant process at the core of smart environments. This type of activity recognition is focused on recognizing the actions of one or more inhabitants within the smart environment based on a series of observations of sub-actions and environmental conditions over a period of finite time. It can be deemed as a complex process that involves the following steps: (i) select and deploy the appropriate sensors to be attached to objects within the smart environment; (ii) collect, store and pre-process the sensor-related data and; (iii) classify activities from the sensor data through the use of activity models
We describe in this work an ontology that has been developed for the mining of Activities of Daily Living (ADL)
We have presented our hybrid methodology for online activity recognition
Summary
Sensor-based activity recognition [1] is a very relevant process at the core of smart environments. This type of activity recognition is focused on recognizing the actions of one or more inhabitants within the smart environment based on a series of observations of sub-actions and environmental conditions over a period of finite time. It can be deemed as a complex process that involves the following steps:. Concepts can be specified as logical combinations of other concepts
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