Adaptação do paradigma orientado a notificações para desenvolvimento de sistemas fuzzy

This work proposes to adjust the Notification Oriented Paradigm (NOP) so that it provides support to fuzzy concepts. NOP is inspired by elements of imperative and declarative paradigms, seeking to solve some of the drawbacks of both. By decomposing an application into a network of smaller computational entities that are executed only when necessary, NOP eliminates the need to perform unnecessary computations and helps to achieve better logical-causal uncoupling, facilitating code reuse and application distribution over multiple processors or machines. In addition, NOP allows to express the logical-causal knowledge at a high level of abstraction, through rules in IF-THEN format. Fuzzy systems, in turn, perform logical inferences on causal knowledge bases (IF-THEN rules) that can deal with problems involving uncertainty. Since PON uses IF-THEN rules in an alternative way, reducing redundant evaluations and providing better decoupling, this research has been carried out to identify, propose and evaluate the necessary changes to be made on NOP allowing to be used in the development of fuzzy systems. After that, two fully usable materializations were created: a C++ framework, and a complete programming language (LingPONFuzzy) that provide support to fuzzy inference systems. From there study cases have been created and several tests cases were conducted, in order to validate the proposed solution. The test results have shown a significant reduction in the number of rules evaluated in comparison to a fuzzy system developed using conventional tools (frameworks), which could represent an improvement in performance of the applications.

Correlação entre o sinal mecanomiográfico e a escala modificada de Ashworth durante avaliação clínica da espasticidade

Spasticity is a common disorder in people who have upper motor neuron injury. The involvement may occur at different levels. The Modified Ashworth Scale (MAS) is the most used method to measure involvement levels. But it corresponds to a subjective evaluation. Mechanomyography (MMG) is an objective technique that quantifies the muscle vibration during the contraction and stretching events. So, it may assess the level of spasticity accurately. This study aimed to investigate the correlation between spasticity levels determined by MAS with MMG signal in spastic and not spastic muscles. In the experimental protocol, we evaluated 34 members of 22 volunteers, of both genders, with a mean age of 39.91 ± 13.77 years. We evaluated the levels of spasticity by MAS in flexor and extensor muscle groups of the knee and/or elbow, where one muscle group was the agonist and one antagonist. Simultaneously the assessment by the MAS, caught up the MMG signals. We used a custom MMG equipment to register and record the signals, configured in LabView platform. Using the MatLab computer program, it was processed the MMG signals in the time domain (median energy) and spectral domain (median frequency) for the three motion axes: X (transversal), Y (longitudinal) and Z (perpendicular). For bandwidth delimitation, we used a 3rd order Butterworth filter, acting in the range of 5-50 Hz. Statistical tests as Spearman's correlation coefficient, Kruskal-Wallis test and linear correlation test were applied. As results in the time domain, the Kruskal-Wallis test showed differences in median energy (MMGME) between MAS groups. The linear correlation test showed high linear correlation between MAS and MMGME for the agonist muscle as well as for the antagonist group. The largest linear correlation occurred between the MAS and MMG ME for the Z axis of the agonist muscle group (R2 = 0.9557) and the lowest correlation occurred in the X axis, for the antagonist muscle group (R2 = 0.8862). The Spearman correlation test also confirmed high correlation for all axes in the time domain analysis. In the spectral domain, the analysis showed an increase in the median frequency (MMGMF) in MAS’ greater levels. The highest correlation coefficient between MAS and MMGMF signal occurred in the Z axis for the agonist muscle group (R2 = 0.4883), and the lowest value occurred on the Y axis for the antagonist group (R2 = 0.1657). By means of the Spearman correlation test, the highest correlation occurred between the Y axis of the agonist group (0.6951; p <0.001) and the lowest value on the X axis of the antagonist group (0.3592; p <0.001). We conclude that there was a significantly high correlation between the MMGME and MAS in both muscle groups. Also between MMG and MAS occurred a significant correlation, however moderate for the agonist group, and low for the antagonist group. So, the MMGME proved to be more an appropriate descriptor to correlate with the degree of spasticity defined by the MAS.