Segmenting sensor data for activity monitoring in smart environments
Within a smart environment, sensors have the ability to perceive changes of the environment itself and can therefore be used to infer high-level information such as activity behaviours. Sensor events collected over a period of time may contain several activities. The fundamental process of any automatic activity monitoring system is therefore to process streams of sensor events and detect occurrences of activities. In this study, we propose three segmentation algorithms to separate time series sensor data into segments to be further processed by an activity recognition algorithm. A preliminary evaluation of the approaches developed has been conducted on a data set collected from a single person living in an apartment over a period of 28days. The results show that the proposed approaches can segment sensor data to detect activities and infer sensor segments to recognise activities with accuracies of 81.6, 81.6 and 82.9%, respectively.
smart environments activity detection evidential modelling time series sensor segmentation