Spatial and Temporal aspects for pattern representation and discovery in Intelligent Environments Asier Aztiria1 and Juan Carlos Augusto2 and Alberto Izaguirre3 Abstract. Core to the development of an intelligent environment is the understanding of where, when, and for how long, actions take place. Our work addresses the process of discovering patterns of behaviour which may reflect preferences of a user on the use of devices. A solution to this problem is essential for the automatization of environments like Smart Homes so that user preferences can be learnt automatically by the system. We present a language that allows us to represent in a clear and non ambiguous way useful patterns that can occur in intelligent environments like Smart Homes. The language is coupled with an algorithm to learn patterns, related to the proposed language, from data collected by sensors.
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INTRODUCTION
Terms such as Ubiquitous Computing [15] and Ambient Intelligence (AmI) (see for example, [10], [9] and [3], refer to an intelligent environment, such as a smart home, where a place is enriched by means of sensors and effectors to provide “a digital environment that proactively, but sensibly, supports people in their daily lives” [6]. In order to achieve this goal, the environment must know habits, preferences and ‘normal’ behaviours of the user to act in agreement with them, therefore a learning process that can discover patterns is necessary. This allows the system to adapt itself dynamically to the preferences of the user and therefore to be more effective (useful to the user). Every act of a person situated in an environment is performed in a spatio-temporal dimension. Therefore the learning process has to take this into consideration. Our system tackles that explicitly. We present in this paper the core of the PUB (Patterns of User Behavior) system: a language to represent patterns, LP U B , and an associated algorithm, AP U B . This system targets the discovery of patterns that consider spatial and temporal aspects from the data collected by sensors. The rest of the paper is organized as follows. Section 2 describes the type of spatial and temporal aspects which are typical in AmI environments and also describes the type of information we assume is collected by sensors. Section 3 focuses on the description of LP U B and illustrates several possible types of patterns that can be represented with it. Section 4 explains how those patterns are obtained from sensor data by using AP U B . Related work is mentioned in Section 5. Section 6 explains the current and future work we are conducting in this line of research. Finally Section 7 concludes with a summary and some reflections on the advances reported. 1 2 3
University of Mondragon, Spain, email:
[email protected] University of Ulster, United Kingdom, email:
[email protected] University of Mondragon, Spain, email:
[email protected]
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SPATIO-TEMPORAL ASPECTS AND SENSING
The AmI paradigm is based in the hypothesis that enriching an environment with technology, e.g. through networked sensors, and understanding user goals in daily activities (by analyzing sensed data) these systems can make the life of citizens easier, as well as increasing the security and efficiency.
2.1 Spatial Aspects In the AmI vision, the electronic items of an intelligent environment such as lights, television, kettle, etc., and furniture (sofa, bed, etc.) are fitted with sensors (for example RFID tags) and they provide information about the interaction between the user and those objects. The location of those devices provide us valuable spatial information on where the actions take place. Other sensors, e.g., motion sensors, which can be distributed in all rooms can also provide valuable information that help us to identify broad regions (e.g., the kitchen) where the activities take place. Sensors like RFID tags can be fitted in every door and detect the occurrence of transitions in between two such regions (e.g., someone crossing through the door that stands in between the kitchen and the reception room). Both type of sensors put together can be used to infer the direction of a trajectory (e.g., the door as a transition area is being used by a person travelling from region ‘kitchen’ to region ‘reception’, and not viceversa).
2.2 Temporal Aspects If the aim of the learning process is to discover user’s patterns in order to be able to describe his/her behaviour, temporal aspects must be considered as high-priority ones. This is not only because user’s actions happen in a specific time, but because most of the time user’s actions are related among them in terms of time. For example, a task like ‘preparing breakfast’ can be understood as a group of actions executed in one of a few sensible ways like first turn on the kettle and meanwhile get a cup, then put a tea bag inside, wait until water boiled, etc. The best way of describing those activities is by using temporal relations.
2.3 Sensors and their nature Information coming from sensors is essential to discover automatically user preferences, habits or ‘normal’ behaviour. But, not all sensors provide information of the same nature. We will distinguish here in between those that measure user actions directly and those that provide information about the context:
• (type O) Sensors installed in objects (devices, furniture, domestic appliances, etc.); They provide direct information about user actions, e.g. a sensor installed in a light switch indicates when the user has switched that light on or off. • (type C) Context sensors; They provide a continuous information about the environment, e.g. a temperature sensor installed in a room will measure room temperature continuously. Although user actions, e.g., changing the setting of the thermostat, influence their future measurements they do not provide direct information about the user’s actions. • (type M) Motion sensors; They indicate the location of the user at all time, e.g. a motion sensor installed in the bedroom can help to infer (for example in connection with an RFID sensor in the door) if the user is inside the bedroom. At the time of learning patterns, it is necessary to combine various sensors to discover useful and accurate patterns. Understanding the role that each type of information plays in the process of learning patterns is crucial to use them properly.
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objects fitted with O-type sensors and his/her presence also produces as a side effect stimuli of M-type sensors in a specific location. All them can trigger a pattern, for example (for simplicity we omit the prefices ‘occur’ and ‘context’ from the rules): ‘ON (Osof a , ‘on’, t0 ) IF time of day is [13:00:00-14:00:00] THEN do (TurnOn, Television, t) when t = t0 + 2s’ (1) ‘ON (Mbedroom , ‘on’) IF Bedroom light level is ,07:00:00) and(
