Time-Window Based Data Segmentation

Abstract

This chapter presents a novel approach to real-time sensor data segmentation for continuous activity recognition. Central to the approach is a dynamic segmentation model, based on the notion of varied time windows, which can shrink and expand the segmentation window size by using temporal information of sensor data and activities as well as the state of activity recognition. The chapter first analyses the characteristics of activities of daily living from which the segmentation model that is applicable to a wide range of activity recognition scenarios is motivated and developed. It then describes the working mechanism and relevant algorithms of the model in the context of ontology-based activity recognition. An example case study has been undertaken in a number of experiments to evaluate and demonstrate the developed approach in a prototype system.