Juan Pedro Caraça-Valente

Discovering similar patterns in time series

In this paper, we describe the process of discovering underlying knowledge in a set of isokinetic tests, using a new algorithm to find similar patterns in a set of temporal series. An isokinetic machine is basically a physical support on which patients exercise one of their joints, in this case the knee, according to different ranges of movement and at a constant speed. The data on muscle strength supplied by the machine are processed by an expert system that has built-in knowledge elicited from an expert in isokinetics. It cleans and pre-processes the data and conducts an intelligent analysis of the parameters and morphology of the isokinetic curves. Then, Data Mining methods based on the discovery of sequential patterns in time series by means of which to find similarities and differences among exercises were applied to the processed information to characterise injuries of those patients. The results obtained were applied in two environments: one for the blind and another for elite athletes.

Combining expert knowledge and data mining in a medical diagnosis domain

Abstract The medical diagnosis system described here uses underlying knowledge in the isokinetic domain, obtained by combining the expertise of a physician specialised in isokinetic techniques and data mining techniques applied to a set of existing data. An isokinetic machine is basically a physical support on which patients exercise one of their joints, in this case the knee, according to different ranges of movement and at a constant speed. The data on muscle strength supplied by the machine are processed by an expert system that has built-in knowledge elicited from an expert in isokinetics. It cleans and pre-processes the data and conducts an intelligent analysis of the parameters and morphology of the isokinetic curves. Data mining methods based on the discovery of sequential patterns in time series and the fast Fourier transform, which identifies similarities and differences among exercises, were applied to the processed information to characterise injuries and discover reference patterns specific to populations. The results obtained were applied in two environments: one for the blind and another for elite athletes.

Discovering similar patterns for characterising time series in a medical domain

In this article, we describe the process of discovering similar patterns in time series and creating reference models for population groups in a medical domain, and particularly in the field of physiotherapy, using data mining techniques on a set of isokinetic data. The discovered knowledge was evaluated against the expertise of a physician specialising in isokinetic techniques, and applied in the I4 (Intelligent Interpretation of Isokinetic Information) project developed in conjunction with the Spanish National Centre for Sports Research and Sciences and the School of Physiotherapy of the Spanish National Organisation for the Blind for muscular diagnosis and rehabilitation, injury prevention, training evaluation and planning, etc., of elite and blind athletes.

Functions, rules and models: three complementary techniques for analyzing strength data

Isokinetics systems are now a leading technology for assessing muscle strength and diagnosing muscle injuries. These systems are very expensive, for which reason they should be put to the best possible use. However, the computer interfaces that now come with isokinetics systems only provide a simple graphical display of the strength data, that is, do not interpretate the data. This paper presents the first phase of the I4 (Interface for Intelligent Interpretation of Isokinetie Data) project, which output two computer systems: ISODEPOR and ISOCIH. Both applications provide simple and effective interaction with the LIDO Isokinetics Machine, implementing expertise in the assessment of the data through a knowledge representation mechanism that includes functions, rules and isokinetic models. The main difference between the applications is that while ISODEPOR was built for sports physicians, ISOCIN was built for blind physicians, accounting for their specific impairments.

Knowledge Discovery Using Medical Data Mining

In this paper we describe the process of discovering underlying knowledge in a set of isokinetic tests (continuous time series) using data mining techniques. The methods used are based on the discovery of sequential patterns in time series and the search for similarities and differences among exercises. They were applied to the processed information in order to characterise injuries and discover reference models specific to populations. The discovered knowledge was evaluated against the expertise of a physician specialised in isokinetic techniques and applied in the I4 project (Intelligent Interpretation of Isokinetic Information).

Analysis of Strength Data Based on Expert Knowledge

Isokinetics systems are now a leading technology for assessing muscle strength and diagnosing muscle injuries. Although expensive, these systems are equipped with computer interfaces that provide only a simple graphical display of the strength data and do not interpret the data. This paper presents the I4 System (Interface for Intelligent Interpretation of Isokinetic Data), developed as a knowledge-based system, which provides an expert knowledge-based analysis of the isokinetic curves. The system was later extended with a KDD architecture for characterising injuries and creating reference models.

Intelligent Interpretation of Strength Data

Isokinetics systems are now a leading technology for assessing muscle strength and diagnosing muscle injuries. These systems are very expensive, for which reason they should be put to the best possible use. However, the computer interfaces that come with isokinetics systems are extremely poor and do not provide for the system to be exploited to its full potential. This paper presents the project 14 (Interface for Intelligent Interpretation of Isokinetic Data) and two computer systems obtained in the project: ISODEPOR and ISOCIN Both applications provide simple and effective interaction with the LIDO Isokinetics Machine, that produces a huge amount of strength data in each isokinetics test. These data are interpreted and presented to the user, who interacts with the information by means of a powerful graphic display system. Additionally, the applications have been equipped with a series of intelligent strength data analysis functions that implement expertise.

Towards Symbolic Data Mining in Numerical Time Series

The analysis of time series databases is very important in areas like medicine, engineering or finance. Most of the approaches that address this problem are based on numerical algorithms calculating distances, clusters, index trees, etc. We have developed a numerical pattern discovery algorithm to find similar patterns to characterize time series, with good results in the isokinetics domain.

Impoving Inductive Learning in Real-World Domains through the Identification of Dependencies: the TIM Framework

In this paper we describe TIM (Total Induction Method), a framework that empowers inductive learning in real domains by the construction of new higher level features based on the relations between the descriptors of the initial training set. A new method, named FDD, for discovering functional dependencies within the data is outlined, and details regarding its relevance for constructive learning are provided. Two examples of their application in real - world domains are given.