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Millions of individuals are living with upper extremity amputations, making them potential beneficiaries of hand and arm prostheses. While myoelectric prostheses have evolved to meet amputees' needs, challenges remain related to their control. This research leverages surface electromyography sensors and machine learning techniques to classify five fundamental hand gestures. By utilizing features extracted from electromyography data, we employed a nonlinear, multiple-kernel learning-based support vector machine classifier for gesture recognition. Our dataset encompassed eight young nondisabled participants. Additionally, our study conducted a comparative analysis of five distinct sensor placement configurations. These configurations capture electromyography data associated with index finger and thumb movements, as well as index finger and ring finger movements. We also compared four different classifiers to determine the most capable one to classify hand gestures. The dual-sensor setup strategically placed to capture thumb and index finger movements was the most effective—this dual-sensor setup achieved 90% accuracy for classifying all five gestures using the support vector machine classifier. Furthermore, the application of multiple-kernel learning within the support vector machine classifier showcases its efficacy, achieving the highest classification accuracy amongst all classifiers. This study showcased the potential of surface electromyography sensors and machine learning in enhancing the control and functionality of myoelectric prostheses for individuals with upper extremity amputations. [ABSTRACT FROM AUTHOR]

Given their societal toll and ubiquitous nature, counselors and other mental health professionals inevitably will encounter clients who have experienced racial microaggressions in their practice. In this primer, we examine key issues relative to racial microaggressions and their impact on the lives of members of racial and ethnic minority groups. Drawing on burgeoning research, we also address likely coping strategies. Finally, we offer implications for mental health training and counseling practice. Throughout, we highlight the evident resilience of racial and ethnic minority individuals in the context of racial microaggressions.

The present study sought to examine South Asian Canadian undergraduate students' (N = 7) experiences with racial microaggressions at a research-intensive Canadian university. Participants ranged in age from 19-23 years and comprised various ethnic groups (e.g., Indian, Bangladeshi, Sri Lankan, and Tamil). Data were collected during a semistructured focus group interview and were analyzed using the consensual qualitative research method (Hill, Thompson, & Williams, 1997). Eight themes emerged. Novel themes included the following: "perceived as fresh off the boat," "excluded from social life," "notion that being Brown is a liability," "assumption of ties to terrorism," and "compelled to be a cultural expert." Three additional themes were consistent with prior research on Asian Americans (Sue, Bucceri, Lin, Nadal, & Torino, 2007): "ascription of intelligence in stereotypical domains," "invalidation of interethnic and racial differences," and "treated as invisible." Implications for research and campus interventions are discussed.

Background: Drowsy driving is one of the leading causes of severe accidents worldwide. In this study, an analyzing method based on drowsiness level proposed to detect drowsiness through electroencephalography (EEG) measurements and vehicle dynamics data. Methods: A driving simulator was used to collect brain data in the alert and drowsy states. The tests were conducted on 19 healthy men. Brain signals from the parietal, occipital, and central parts were recorded. Observer Ratings of Drowsiness (ORD) were used for the drowsiness stages assessment. This study used an innovative method, analyzing drowsiness EEG data were in respect to ORD instead of time. Thirteen features of EEG signal were extracted, then through Neighborhood Component Analysis, a feature selection method, 5 features including mean, standard deviation, kurtosis, energy, and entropy are selected. Six classification methods including K-nearest neighbors (KNN), Regression Tree, Classification Tree, Naive Bayes, Support vector machines Regression, and Ensemble Regression are employed. Besides, the lateral position and steering angle as a vehicle dynamic data were used to detect drowsiness, and the results were compared with classification result based on EEG data. Results: According to the results of classifying EEG data, classification tree and ensemble regression classifiers detected over 87.55% and 87.48% of drowsiness at the moderate level, respectively. Furthermore, the classification results demonstrate that if only the single-channel P4 is used, higher performance can achieve than using data of all the channels (C3, C4, P3, P4, O1, O2). Classification tree classifier and regression classifiers showed 91.31% and 91.12% performance with data from single-channel P4. The best classification results based on vehicle dynamic data were 75.11 through KNN classifier. Conclusion: According to this study, driver drowsiness could be detected at the moderate drowsiness level based on features extracted from a single-channel P4 data. [ABSTRACT FROM AUTHOR]