A dynamic approach to recognize activities in WSN

The essence of context awareness has changed the revolution of ubiquitous computing, and the wireless sensor network technologies paved the way towards many applications. Activity recognition is a key component in identifying the context of a user for providing services based on the application. In this study, we propose a context management model that is based on activity recognition. The model is composed of four components: a set of sensors, a set of activities, a backend server with machine learning algorithms, and a GUI application for the interaction with the user. A prototype is developed to show the usability of the proposed model. As a pilot testing, only accelerometer data of an Android phone is used to identify the activities of daily living (ADLs): sitting, standing, walking, and jogging. A good accuracy of results that is about 96% on average is achieved in all activities.