Neighbourhood counting for activity detection from time series sensor data

Health status along with assistive support requirements can be assessed by measures of activities of daily living. Advances in pervasive sensing and intelligent reasoning pave a way to monitor, i.e. detect and recognise, activities automatically and unobtrusively. The first task in monitoring activities is to detect when an activity has taken place based on a time series of sensor activation events. Inspired by the concepts of dynamic time warping and neighborhood counting matrix in similarity measures, this paper proposes a novel method to segment streams of sensor events for activity detection. Sensor segments may then be used as inputs to evidential ontology networks of activities for activity recognition.