Feature evaluation for myoelectric pattern recognition of multiple nearby reaching targets.

Intention detection of the reaching movement is considerable for myoelectric human and machine collaboration applications. A comprehensive set of handcrafted features was mined from windows of electromyogram (EMG) of the upper-limb muscles while reaching nine nearby targets like activities of daily living. The feature selection-based scoring method, neighborhood component analysis (NCA), selected the relevant feature subset. Finally, the target was recognized by the support vector machine (SVM) model. The classification performance was generalized by a nested cross-validation structure that selected the optimal feature subset in the inner loop. According to the low spatial resolution of the target location on display and following the slight discrimination of signals between targets, the best classification accuracy of 77.11 % was achieved for concatenating the features of two segments with a length of 2 and 0.25 s. Due to the lack of subtle variation in EMG, while reaching different targets, a wide range of features was applied to consider additional aspects of the knowledge contained in EMG signals. Furthermore, since NCA selected features that provided more discriminant power, it became achievable to employ various combinations of features and even concatenated features extracted from different movement parts to improve classification performance.

Predicting the Cognitive Ability of Young Women Using a New Feature Selection Algorithm.

Cognitive abilities are the capabilities to perform mental processes that include executive function, comprehension, decision-making, work performance, and educational attainment. This study aimed to investigate the relationship between several biomarkers and individuals' cognitive ability using various machine learning methods. A total of 144 young women aged between 18 and 24 years old were recruited into the study. Cognitive performance was assessed using a standard questionnaire. A panel of biochemical, hematological, inflammatory, and oxidative stress biomarkers in serum and urine was measured for all participants. A novel combination of feature selection and feature scoring techniques within a hierarchical ensemble structure has been proposed to identify the most effective features in recognizing the importance of various biomarker signatures in cognitive abilities classification. Multiple feature selection methods were employed in conjunction with different classifiers to construct this model. In this manner, using three filter methods, the scores of each feature were considered. The combination of high-scoring features for each filter method was stored as the primary feature subset. A high-accuracy feature subset was selected by using a wrapper method. The collection of highly scored features from each filter method formed the primary feature subset. A wrapper method was also employed to select a feature subset with high accuracy. To ensure robustness and minimize random variations in the feature subset search process, a repeative tenfold cross-validation was conducted. The most frequently recurring features were determined. This iterative step facilitated the identification of an optimal feature subset, effectively reducing the dimensionality of features while maintaining accuracy. Among the 47 extracted factors, serum level of NOx (nitrite ± nitrate), alkaline phosphatase (ALP), and phosphate as well as blood platelet count (PLT) was entered into the model of cognitive abilities with the highest accuracy of approximately 70.9% using a decision tree classifier. Therefore, the serum levels of NOx, ALP, phosphate, and blood PLT count may be important markers of the cognitive abilities in apparently healthy young women. These factors my provide a simple procedure to identify mental abilities and earlier cognitive decline in healthy adults.