Pattern recognition algorithms have introduced increasingly sophisticated solutions. However, many datasets are far from perfect; for example, they may include inconsistencies and have missing data, which may interfere with the classification process. Thus, the use of paraconsistent logic can provide a compelling quantitative analysis approach in classification algorithms because it deals directly with inaccurate, inconsistent and incomplete data. Paraconsistent logic is considered a nonclassical logic, which enables the processing of contradictory signals in its theoretical structure without invalidating the conclusions. In this context, the proposed approach aggregates the power of hybrid classifiers, the low noise susceptibility of the random forest approach and the robustness of paraconsistent logic to provide an intelligent treatment of contradictions and uncertainties in datasets. The proposed method is called paraconsistent random forest. The computational results demonstrated that paraconsistent random forest could classify several databases with satisfactory accuracy in comparison with state-of-the-art methods, namely, LDA, KNN, and SVM. Regarding imperfect datasets, the proposed approach significantly outperforms most of these methods in terms of prediction accuracy.

The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carried out highlighting the advantages of using muscle signal in order to control rehabilitation devices, such as experimental prostheses. This paper presents a study investigating the use of forearm surface electromyography (EMG) signals for classification of several movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an artificial neural network to process signal features to recognize performed movements. The average accuracy reached for the classification of six different movements was 68-88%.

Surface electromyography (sEMG) analysis is becoming increasingly popular in a broad variety of applications. Despite satisfactory classification rates are frequently obtained through the use of Computational Intelligence (CI) algorithms, there are some issues mostly related to the data acquisition which are not properly addressed in current studies. In this paper we aim to present a method capable of mitigate the noise in the sEMG acquisition caused mainly by loose or misplaced non-invasive electrodes. To address this issue we propose an auto-adaptive artificial neural network (AAANN) capable of identify this two anomalies in the signal and retrain itself discarding the causative channels. Once the AAANN is retrained it is possible to retrieve information of only the most significant channels which increase the accuracy rate of the CI method. The method was tested on a database containing five able-bodied subjects and four amputee subjects of both sex. The average classification accuracy for the adaptive input selection method was 83,96 ± 6,5% for the able-bodied subjects and 61,15 ± 7,7% for the amputees subjects against 72,06 ± 8,0% in able-bodied subjects and 39,77 ± 10,6% for the amputees subjects considering the non-adaptive approach. Both systems make use of the AAANN to classify 9 distinguish upper-limb movements with different degrees of freedom.