While activity recognition has been shown to be valuable for pervasive computing applications, less work has focused on techniques for forecasting the future occurrence of activities. We present an activity forecasting method to predict the time that will elapse until a target activity occurs. This method generates an activity forecast using a regression tree classifier and offers an advantage over sequence prediction methods in that it can predict expected time until an activity occurs. We evaluate this algorithm on real-world smart home datasets and provide evidence that our proposed approach is most effective at predicting activity timings. Keywords activity forecasting; activity recognition; regression trees; smart homes Introduction A significant component of any smart assistive technology is the ability to predict future occurrences of a user's activities based on current and past context. The ability of a system to forecast what activities the user will perform in the future allows the system to take anticipatory action to help the user complete those activities. Activity prediction can thus transform contextaware systems into activity-predictive systems. One type of system that can benefit from the use of an activity prediction algorithm is the smart environment. A smart environment is a physical environment that is equipped to sense the state of its inhabitants and their surroundings in order to act to improve the wellbeing of the inhabitants and the environment [1,2]. Smart environments employ a variety of sensors to monitor their inhabitants and provide prompts and other actions to help complete those activities and provide for inhabitant safety. In activity prediction, we desire to create a system which can forecast the number of time units (be it seconds, hours, or days) until future activities occur. The challenge lies in optimally using collected sensor data to inform activity forecasts without human intervention. Although the data can provide a wealth of information about user activities, it can be challenging to extract useful attributes to use in forecasting models. The relationship between sensor data and future activities is complex and nonlinear. Previous methods rely on user input and pre-defined rules, limiting their scope and flexibility for use in varied applications. An effective activity predictor needs to be able to discover and utilize deeper activity complexities and model them appropriately. In this article, we introduce an activity forecasting algorithm that predicts future activity occurrences. Our algorithm, which we call AF, predicts the number of time units that will elapse until the next occurrence of a particular activity based on current and past sensor events. AF utilizes information about recent sensor events to generate both discrete-event and samplingbased features to provide improved prediction accuracy. These features can then be used with common machine learning models to learn the complex relationships between sensor data and
Decision trees have been used to predict the time a certain activity would happen REF .
Forecasting occurrences of activities
Pervasive and mobile computing
Pervasive and mobile computing
Computer Science Medicine