An Evidential Fusion Rule for Ambient Intelligence for Activity Recognition

International audienceThese last years, a lot of combination rules emerged in order to model the situations of belief fusion. These rules can be classified in two different classes. However, these rules do not differentiate between focal elements in the combination step which produce counterintuitive results in some situations. Motivated by this observation, we propose a new combination rule which hybrids the strategies of these two classes. Our rule is two-step operator where the averaging step comes first, and then the conflict redistribution step. Experimental studies are conducted on a real smart home dataset to show the accuracy of our rule in ubiquitous-assisted living situation

Evidence Combination Based on CSP Modeling

International audienceThe evidence theory and its variants are mathematical formalisms used to represent uncertain as well as ambiguous data. The evidence combination rules proposed in these formalisms agree with Bayesian probability calculus in special cases but not in general. To get more reconcilement between the belief functions theory with the Bayesian probability calculus, this work proposes a new way of combining beliefs to estimate combined evidence. This approach is based on the Constraint Satisfaction Problem modeling. Thereafter, we combine all solutions of these constraint problems using Dempster's rule. This mathematical formalism is tested using information system security risk simulations. The results show that our model produces intuitive results and agrees with the Bayesian probability calculus

An Evidential Fusion Approach for Activity Recognition in Ambient Intelligence Environments

International audienceWith the growing emergence of ambient intelligence, ubiquitous computing, sensor networks and wireless networking technologies, "ubiquitous networked robotics" is becoming an active research domain of intelligent autonomous systems. It targets new innovative applications in which robotic systems will become part of these networks of artifacts to provide novel capabilities and various assistive services anywhere and anytime, such as healthcare and monitoring services for elderly in Ambient Assisted Living (AAL) environments. Situation recognition, in general, and activity recognition, in particular, provide an added value on the contextual information that can help the ubiquitous networked robot to autonomously provide the best service that meet the needs of the elderly. Dempster-Shafer theory of evidence and its derivatives are an efficient tool to handle uncertainty and incompleteness in smart homes and ubiquitous computing environments. However, their combination rules yield counter-intuitive results in high conflicting activities. In this paper, we propose a new approach to support conflict resolution in activity recognition in AAL environments. This approach is based on a new mapping for conflict evidential fusion to increase the efficiency and accuracy of activity recognition. It gives intuitive interpretation for combining multiple sources in all conflicting situations. The proposed approach, evaluated on a real world smart home dataset, achieves 78% of accuracy in activity recognition. The obtained results outperform those obtained with the existing combination rules

Advanced Context modeling and Reasoning for ambient intelligence environments and ubiquitous robotics

International audienceno abstrac

Multi-observer Decision Making Approach Using Power Fuzzy Soft Sets

International audienceIn the present paper, a method based on a new concept called power fuzzy soft set is proposed for multi-observer decision making problems under uncertain information. The new method applies a weighted conjunctive operator to aggregate these sets into a reliable resultant power fuzzy soft set from the input data set. To decide among the alternatives, a new ranking algorithm is introduced. The effectiveness and feasibility of this method are demonstrated by comparing it to algorithms based on the maximum score in decision making

Generalized Fuzzy Soft Set Based Fusion Strategy for Activity Classification in Smart Home

International audienceIn recent years, a plethora of different studies for design of traditional ensemble classifiers has been proposed in order to improve final recognition accuracy. However, among the ensemble classifiers, combination methods are focused on building independent classifiers of the same or different algorithms using majority voting methods. In this paper, we present a new fusion scheme for ensemble classifiers based on a new concept called Generalized Fuzzy Soft Set (GFSS), which we apply in activity classification. Essentially, we apply a weighted aggregate operator to the output of each classifier in order to fuse the GFSS into a more reliable classifier. The proposed fusion method is based on a new ranking algorithm to classify activities. We show that the proposed method produces more accurate results than the best single classifier and its effectiveness is demonstrated by comparing it with single classifier in terms of activity recognition accuracy

An evidential fusion approach for activity recognition under uncertainty in ambient intelligence environments

International audienceno abstrac

Context Awareness in Uncertain Pervasive Computing and Sensors Environment

International audienceBuilding context-aware pervasive computing systems - such as ambient intelligent spaces or ubiquitous robots - needs to take into account the quality of contextual information collected from sensors. Such information are often inaccurate, uncertain or subject to noise due to environment and user dynamics. Dempster-Shafer theory has been extensively adopted to handle uncertainty in situation and activity recognition. This theory is used to represent, manipulate and decide under uncertainty. However, combining information using Dempster's rule may produce counterintuitive decision in highly conflicting evidences due to sources failure. Recently, a variety of rules were proposed to overcome such drawback. Inspired by Murphy's rule, we propose in this paper a new rule called “Weighted Average Combination Rule” (WACR) to deal with context recognition in highly dynamic environment such as ambient intelligence spaces. The proposed WACR rule is based on evidence arithmetic average and cardinality. WACR rule was applied to some conflictual evidence examples and has been shown to reap more appropriate decisions than other alternative rules for decision-making in activity-aware systems. To demonstrate the applicability and performance of our approach, we have studied a scenario of context recognition in an ambient intelligent environment. In this scenario, we simulated a smart kitchen composed of status devices and RFID sensors that allow determining what is the artifact in use by the inhabitant and for which activity

Context Awareness in Uncertain Pervasive Computing and Sensors Environment

International audienceBuilding context-aware pervasive computing systems - such as ambient intelligent spaces or ubiquitous robots - needs to take into account the quality of contextual information collected from sensors. Such information are often inaccurate, uncertain or subject to noise due to environment and user dynamics. Dempster-Shafer theory has been extensively adopted to handle uncertainty in situation and activity recognition. This theory is used to represent, manipulate and decide under uncertainty. However, combining information using Dempster's rule may produce counterintuitive decision in highly conflicting evidences due to sources failure. Recently, a variety of rules were proposed to overcome such drawback. Inspired by Murphy's rule, we propose in this paper a new rule called “Weighted Average Combination Rule” (WACR) to deal with context recognition in highly dynamic environment such as ambient intelligence spaces. The proposed WACR rule is based on evidence arithmetic average and cardinality. WACR rule was applied to some conflictual evidence examples and has been shown to reap more appropriate decisions than other alternative rules for decision-making in activity-aware systems. To demonstrate the applicability and performance of our approach, we have studied a scenario of context recognition in an ambient intelligent environment. In this scenario, we simulated a smart kitchen composed of status devices and RFID sensors that allow determining what is the artifact in use by the inhabitant and for which activity

New Evidence Combination Rules for Activity Recognition in Smart Home

International audienceThe evidence theory and its propositional conflict redistribution variant rules are mathematical formalisms used to represent uncertain as well as ambiguous data. The evidence combination rules proposed in these formalisms do not satisfy the idempotence property. However, in a variety of applications, it is desirable that the evidence combination rules satisfy this property. In response to this challenge, the present work proposes a new formalism for reasoning under uncertainty based on new consensus and conflicting of evidence concepts. This mathematical formalism is evaluated using a real world activity recognition problem in smart home environment. The results show that one rule of our formalism respects the idempotence property and improves the accuracy of activity recognition