José T. Palma

Ambient Assisted Living system for in-home monitoring of healthy independent elders

In this work, the process followed for the development of a specific Ambient Assisted Living system is presented. The proposed systems has been designed to monitor elders which live alone and want to keep living independently. The process covers all the phases in intelligent system development: requirement analysis, conceptual model specification, architectural design and evaluation. One of the main contributions of the proposed work is an exhaustive evaluation methodology that is integrated in the development process. A relevant characteristic of the evaluation process is that, from initial phases, commercial presentations of the products functionalities is possible. Another important contribution is related with the capability of the system to adapt its behavior to that of the monitored elder. The presented system is called Necesity. It has become a commercial product which is already working and giving service to elders in the South-East of Spain.

Temporal similarity by measuring possibilistic uncertainty in CBR

Similarity is an essential concept in case-based reasoning (CBR). In domains in which time plays a relevant role, CBR systems require good temporal similarity measures to compare cases. Temporal cases are traditionally represented by a set of temporal features, defining time series and temporal event sequences. In the particular situation where these features are not homogeneous (i.e. combination of qualitative and quantitative information), systems find difficulties in performing the CBR cycle. Furthermore, temporal similarity measures cannot directly apply the efficient time series techniques, requiring new approaches to deal with these heterogeneous sequences. To this end, recent proposals are focused on direct matching between pairs of features within sequences, mainly based on classical distances. However, three limitations to the traditional approaches have been identified: (1) they do not consider the implicit temporal relations amongst all features of the sequence (ignoring a large amount of temporal information); (2) they ignore the uncertainty produced in any process of analogy; (3) they are designed to compare pairs of sequences, limiting their use to basic aspects of the Retrieval step of CBR (no benefits on other CBR steps). Temporal constraint networks have proved to be useful tools for temporal representation and reasoning, and can be easily extended to manage imprecision and uncertainty. An approach to solve similarity problems could be the transformation of these heterogeneous sequences into uncertain temporal relations, obtaining a temporal constraint network. The overall uncertainty of this network can be considered as an effective indicator of the sequences similarity. Therefore, this paper proposes a non-classical approach to measure temporal similarity of cases which are heterogeneous temporal event sequences. Given two or more sequences, the temporal similarity is measured by describing a unique temporal scenario of possibilistic temporal relations and calculating the uncertainty produced.

Defining the problem: key element for the success of knowledge management

The objective of this paper is to propose a methodology for applying knowledge management (KM), in which we first focus on explaining problematic areas of an organization by identifying the knowledge core process, before applying KM strategies to those processes. For the methodology, we lean on the larger context of systems thinking to help visualize the whole organization, and it is here that the key factors are identified and a set of strategic criteria is established. The most important criteria are then used to evaluate the associated processes and their respective tasks in order to establish the problematic areas or opportunities where KM initiatives can be applied. This strategy precludes tackling all the organizations problems and allows management to focus on only those processes that provide significant and manageable knowledge. Finally, this study explores and cautiously recommends an unexploited but valuable element to be taken into account when implementing KM initiatives.

Medical knowledge management for specific hospital departments

Medical knowledge representation and management is concerned with how to organise the often vague clinical experience of medical staff required for computable models. However, few knowledge management and acquisition tools have entered routine use, since such tools are not perceived by physicians as part of the clinical information process. An attempt to partially solve this problem, we identify two key aspects of knowledge representation and management tasks. The first is to adopt a medical knowledge standardisation to provide a consistent terminology control and to simplify the integration between knowledge management tools and the health information system. The second is to establish an effective knowledge acquisition process in specific medical fields by adapting knowledge acquisition tools. Therefore, the main goal of this work is to define computational models and to design mechanisms for the effective acquisition and management of medical knowledge in real-life hospital departments. To this end, we analyse the representation of medical knowledge (based on deep-causal models) and the development of knowledge management tools (based on ontologies), integrated within the information processing activities of the clinical user. Finally, we illustrate its applicability in the Intensive Care Unit and Pediatry scenarios.

Computing context-dependent temporal diagnosis in complex domains

Over the years, many Artificial Intelligence (AI) approaches have dealt with the diagnosis problem and its application in complex environments such as medical domains. Model-Based Reasoning (MBR) is one of the approaches that traditionally have tried to solve this problem thanks to its capacity for modelling and reasoning. The consideration of the temporal dimension in these domains is a challenging topic in MBR, especially if temporal imprecision is taken into account. Unfortunately, despite there being many successful MBR systems, there are still two fundamental problems in their development at the aforementioned domains: (1) the degree of dependency between the model used and the domain; and (2) the reutilization of the systems when the domain changes. First this paper proposes a set of basic requirements for the design of Knowledge-Based Systems that will help to solve the problem of temporal diagnosis for environments of high conceptual complexity. From these principles and through a deep analysis of the various approaches present in AI we establish a generic framework that addresses both goals by integrating MBR and ontologies for domain knowledge representation in order to describe a intermediate model representation to facilitate the low dependency between the model and the application domain. Finally, this paper demonstrates the use of the framework by developing a diagnosis system within a real medical environment (Intensive Care Unit) with a step-by-step description of the process, from the architecture through to implementation.

Detecting Domestic Problems of Elderly People: Simple and Unobstrusive Sensors to Generate the Context of the Attended

Unexpected falls and/or heart attacks at home are one of the main accidents the elderly face nowadays. This work focuses on elderly people which yet are independent and live alone in their own house. In such cases, the mentioned accidents may prevent her to ask for help as it is possible that she may lose conscience or stay paralyzed at the floor. In this paper, it is shown how a rule based classifier, designed by using simple a priori knowledge, which incorporates elderlys context information and simple adaptive mechanisms for this information, may be used to detect domestic accidents as quickly as possible.

Temporal Data Mining with Temporal Constraints

Nowadays, methods for discovering temporal knowledge try to extract more complete and representative patterns. The use of qualitative temporal constraints can be helpful in that aim, but its use should also involve methods for reasoning with them (instead of using them just as a high level representation) when a pattern consists of a constraint network instead of an isolated constraint. In this paper, we put forward a method for mining temporal patterns that makes use of a formal model for representing and reasoning with qualitative temporal constraints. Three steps should be accomplished in the method: 1) the selection of a model that allows a trade off between efficiency and representation; 2) a preprocessing step for adapting the input to the model; 3) a data mining algorithm able to deal with the properties provided by the model for generating a representative output. In order to implement this method we propose the use of the Fuzzy Temporal Constraint Network (FTCN) formalism and of a temporal abstraction method for preprocessing. Finally, the ideas of the classic methods for data mining inspire an algorithm that can generate FTCNs as output. Along this paper, we focus our attention on the data mining algorithm.

Ontology Based Approach to the Detection of Domestics Problems for Independent Senior People

In the first decade of the 21st century, there is a tremendous increment in the number of elderly people which live independently in their own houses. In this work, we focus on elderly people which spend almost all the time by their own. The goal of this work is to build an artificial system capable of unobtrusively monitor this concrete subject. In this case, the system must be capable of detecting potential situations of danger (e.g. the person lays unmobilised in the floor or she is suffering some kind of health crysis). This is done without any wearable device but only using a sensor network and an intelligent processing unit within a single and small CPU. This kind of such unbostrusive system makes seniors to augment his or her perception of independence and safeness at home.

Evaluating Case-Base Maintenance algorithms

The success of a Case-Based Reasoning (CBR) system closely depends on its knowledge-base, named the case-base. The life cycle of CBR systems usually implies updating the case-base with new cases. However, it also implies removing useless cases for reasons of efficiency. This process is known as Case-Base Maintenance (CBM) and, in recent decades, great efforts have been made to automatise this process using different kind of algorithms (deterministic and non-deterministic). Indeed, CBR system designers find it difficult to choose from the wealth of algorithms available to maintain the case-base. Despite the importance of such a key decision, little attention has been paid to evaluating these algorithms. Although classical validation methods have been used, such as Cross-Validation and Hold-Out, they are not always valid for non-deterministic algorithms. In this work, we analyse this problem from a methodological point of view, providing an exhaustive review of these evaluation methods supported by experimentation. We also propose a specific methodology for evaluating Case-Base Maintenance algorithms (the @a@b evaluation). Experiment results show that this method is the most suitable for evaluating most of the algorithms and datasets studied.

Defining the semantics of rule-based Web applications through model-driven development

Defining the semantics of rule-based Web applications through model-driven development Rule languages and inference engines incorporate reasoning capabilities to Web information systems. This paper presents an approach for the specification and development of Web applications performing the usual functionalities of data management and incorporating a rule engine for reasoning capabilities. The proposed approach is based on the definition of a high-level representation of the semantics of rule-based applications through a formalism for conceptual modeling combining lightweight ontologies and production rules. These models are used as the source for a model-driven method that applies several transformations to conceptual models generating the rule-based Web application code in an automatic process. As a result, the rule-based Web application embeds a rule engine suitable for deducing information by applying an inference process. The structure of the information managed by the Web application is based on ontology classes, whereas the logical expressions applied in reasoning are obtained from production rules of the model. A rule-based Web application has been developed and evaluated using a supporting tool that implements the ideas presented in this paper.