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.

Author Keywords: Knowledge discovery; Data mining techniques; Expert knowledge

Article Outline

1. Introduction

2. Overview of the system

3. Expert knowledge

3.1. Functions

3.2. Rules

3.3. Isokinetic models

3.4. Combining different knowledge representations

4. Data mining

4.1. Pre-processing

4.2. Detecting injury patterns in isokinetic exercises

4.2.1. Method for discovering typical injury patterns

4.2.2. Algorithm for discovering similar patterns

4.3. Application to detecting injuries

5. Evaluation and application of the discovered knowledge

6. Conclusions

Acknowledgements

References

Corresponding author. Tel.: +34-91-3522546; fax: +34-91-3526388; email: falonso@fi.upm.es