affiliated with LISSI-UPEC laboratory
Ubiquitous Intelligence
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Ubistruct Living Lab

By Abdelghani Chibani

The Ubistruct living lab is devoted for designing and experimenting ubiquitous intelligence technologies that can be used for services composition, activity recognition, context aware services adaptation. We target mostly Ambient Assisted Living (AAL) and Pervasive healthcare services. The infrastructure of the living lab is built within the new premises of the LISSI research laboratory. Inside, we can make realistic experiments in the context of activities of daily living in a smart environment. The latter consists of a kitchen, bathroom, bedroom and living room. The living lab infrastructure is modular and can be reconfigured to meet different experimentation requirements and scenarios, thanks to the use of a variety of wireless and mobile furniture and equipment that can be found in the market. The latter range from wireless sensor networks and actuators to smart devices such as smartphones, tablets and a mobile robot. The hardware infrastructure is described in the following.

Semantic monitoring of the context of the activities of daily living

Ubiquitous robots play a significant role in fostering traditional ambient and assisted living. For instance, companion robots can accurately and closely monitor humans daily activities and wellbeing thanks to their ability of autonomously moving, sensing the environment, recognizing and tracking features of interest, making different sorts of reasoning tasks and triggering context aware reactive actions. In this scenario we demonstrate the feasibility of a cognitive component for context aware monitoring based on ontologies and inference rules according to the close world assumption reasoning. Context awareness can be defined as the capacity of a cognitive entity to measure, detect and infer a set of contextual features that are identifiable in time and space. Let us consider the case where Nathan, who have a kind of hypotension issues, goes directly to the gymnasium Kitchen after finishing his football game, to drink some water. Sort time after opening the tap and drinking a glass of water, he suddenly fall down shortly. Current monitoring system do/cannot detect such event when they happen and usually persons do not trigger alarms. The robot, which is able to make semantic correlations and production inferences, can move and recognize the context and decide to trigger an alarm in the case of real emergency. In this scenario we show how the design of inference rules is made separately whiteout considering the effective description of the real world sensors and actuators. The reasoning core of the system can make autonomously the semantic mapping between the raw data sent by the wireless sensors and the ontology and inference rules is done under a Closed-World Assumption (CWA), which means that all statements that have not been mentioned explicitly to be true are necessarily false. In contrast the OWL based reasoning that uses an Open-World Assumption (OWA), when the reasoner is asked on the truth value of a missing information it does not provide any response and the query/rule is simply ignored.

Detection and handling of conflicting situations

The execution of temporal projection tasks represents a popular formal technique in the field of cognitive robotics, while its practical significance is also evidenced in recent implementations of autonomous systems. Its use in AmI domains can provide an extra leverage in achieving proactive behavior. The following example demonstrates a case that has been modeled in Event calculus and implemented and experimented in the living lab. Imagine that the inhabitant, while being at the kitchen, turns on the Kettle, Oven or hot plate and places a pot containing milk. The content of the pot will begin to heat up and eventually start to boil. There is no dedicated sensor measuring the temperature of the milk; we rely on commonsense derivations in order to model this behavior. As such, the system can also expect that the boiling point will be reached after a while, and therefore sets a timer to become aware of this incident. The objective of the monitoring system is to identify potentially conflicting situations both at the present time and in the future, in order to trigger different types of alerts and recommendations in an as less intrusive manner as possible. In this case, by performing temporal projection with a time window of more than 3 minutes the system will predict that the milk will start to boil; yet, since the user can reasonably be assumed to be in the kitchen, no preventive action needs to be taken and any alert can be postponed until a later point.

Returning in real-time mode, imagine that the inhabitant then leaves the kitchen, enters the bathroom and turns on the bathtub faucet causing water to start filling the bathtub. A progression of the world state now will allow the system to identify that the two parallel activities of milk heating up and water filling the bathtub will demand the user’s attention at approximately the same time at two different locations: he should stop the water from reaching the rim of the bathtub while also turn off the hot plate in the kitchen. Although the critical situation refers to a future point in time and it is not certain that it will actually occur, a warning message is more appropriate to be placed in the present state. The reasoning system will infer on the following: move the robot to the inhabitant current location and initiate an audio message to warn him about the situation and suggest him to turn off the kettle, the oven or the hot plate. In different cases, the reasoning system may instead decide to take initiative, such as to turn off the appliance on the inhabitant's behalf.

Semantic Reasoning for Natural and Seamless interactions between robots, humans and smart objects

When Eden goes to the grocery store, do she always remember to capture all the required food before departing. The answer is commonly, ”no”, as humans often forget to check many common items. For example, while at the store she don’t know the amount of milk available at home, a call can typically be made to someone at home (Steve) to query the status of milk. If there is no human at home to call the problem then falls to another actor in the system to answer the common query. Why not ask the refrigerator or the companion robot to determine this status? In this case, the task of determining the milk status is indistinguishable between another human, a robot or any other machine like the refrigerator. Which system actor answers the query of the milk status does not matter. The key point is the adequate capability present to answer the query. In this case, we present a scenario where non-humans can interact together to solve ubiquitous problems.

This scenario captures the milk dilemma and a possible solution where machines and sensors can assist in answering the query. In this scenario, let us assume that reasoning system of the refrigerator will notify the companion robot each time when a food (milk) is missing or if it becomes bad. When Eden approaches a grocery store, her smart phone will recognize the proximity to the grocery store an notifies the smart objects at home. The robot and the refrigerator will capture this notification and only the robot has the reasoning capability for dealing with that. It will decide to contact Eden asking her to buy the milk. We consider that Eden will accept and may ask if is there some yogurt. When the refrigerator and the robot reasoners fail in providing an answer by querying their local knowledge bases, the robot decides to ask the closest person “Steve” to get the missing information and forward the answer to Eden. Semantic reasoning, natural interactions and context awareness are the key concepts that makes the intelligence of actors. To further this innovation, the relationship between Humans, software Agents, Robots, Machines and Sensors (HARMS) must approach that of indistinguishability in multi agents systems communication. In fact, the whole concept of indistinguishability is novel and useful in terms of capability based organizations, where the system selects a task for execution, based on the capability of some agent (or other HARMS actor) given its capability to accomplish the selected task or solve a goal. All available actors with that specific capability allow the choice to be indistinguishable. Communication is the medium to enable indistinguishability, but is useful in an organization setting where group rational decisions and choices are made.