An EMG dataset for Arabic sign language alphabet letters and numbers.

Nowadays, surface electromyography (sEMG) is evolving as a technology for hand gesture recognition. Detailed studies have revealed the capacity of EMG signals to access detailed information, particularly in the classification of hand gestures. Indeed, this advancement emerges as an interesting element in refining the recognition and interpretation of sign languages and exploring deeper into the phonology of signed languages. Aligned with this advancement and the need for a reliable and mobile sign language recognition system, we introduce a specialized sEMG dataset, acquired using the Myo armband. This device is adept at capturing recordings at frequencies of up to 200 Hz. The dataset focuses on the 28 letters of the Arabic alphabet and 10 digits using hand gestures, with each gesture captured into 400 frames. This considerable collection of 18,716 samples was achieved with the cooperation of three contributors, providing a varied and comprehensive range of gestural data.

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review.

The analysis and recognition of sign languages are currently active fields of research focused on sign recognition. Various approaches differ in terms of analysis methods and the devices used for sign acquisition. Traditional methods rely on video analysis or spatial positioning data calculated using motion capture tools. In contrast to these conventional recognition and classification approaches, electromyogram (EMG) signals, which measure muscle electrical activity, offer potential technology for detecting gestures. These EMG-based approaches have recently gained attention due to their advantages. This prompted us to conduct a comprehensive study on the methods, approaches, and projects utilizing EMG sensors for sign language handshape recognition. In this paper, we provided an overview of the sign language recognition field through a literature review, with the objective of offering an in-depth review of the most significant techniques. These techniques were categorized in this article based on their respective methodologies. The survey discussed the progress and challenges in sign language recognition systems based on surface electromyography (sEMG) signals. These systems have shown promise but face issues like sEMG data variability and sensor placement. Multiple sensors enhance reliability and accuracy. Machine learning, including deep learning, is used to address these challenges. Common classifiers in sEMG-based sign language recognition include SVM, ANN, CNN, KNN, HMM, and LSTM. While SVM and ANN are widely used, random forest and KNN have shown better performance in some cases. A multilayer perceptron neural network achieved perfect accuracy in one study. CNN, often paired with LSTM, ranks as the third most popular classifier and can achieve exceptional accuracy, reaching up to 99.6% when utilizing both EMG and IMU data. LSTM is highly regarded for handling sequential dependencies in EMG signals, making it a critical component of sign language recognition systems. In summary, the survey highlights the prevalence of SVM and ANN classifiers but also suggests the effectiveness of alternative classifiers like random forests and KNNs. LSTM emerges as the most suitable algorithm for capturing sequential dependencies and improving gesture recognition in EMG-based sign language recognition systems.

Are we there yet? A systematic literature review of Open Educational Resources in Africa: A combined content and bibliometric analysis.

Although several studies have been conducted to summarize the progress of open educational resources (OER) in specific regions, only a limited number of studies summarize OER in Africa. Therefore, this paper presents a systematic literature review to explore trends, themes, and patterns in this emerging area of study, using content and bibliometric analysis. Findings indicated three major strands of OER research in Africa: (1) OER adoption is only limited to specific African countries, calling for more research and collaboration between African countries in this field to ensure educational equity; (2) most of the OER initiatives in Africa have focused on the creation process and neglected other important perspectives, such as dissemination and open educational practices (OEP) using OER; and (3) on top of the typical challenges for OER adoption (e.g., infrastructure), other personal challenges were identified within the African context, including culture, language, and personality. The findings of this study suggest that more initiatives and cross-collaborations with African and non-African countries in the field of OER are needed to facilitate OER adoption in the region. Additionally, it is suggested that researchers and practitioners should consider individual differences, such as language, personality and culture, when promoting and designing OER for different African countries. Finally, the findings can promote social justice by providing insights and future research paths that different stakeholders (e.g., policy makers, educators, practitioners, etc.) should focus on to promote OER in Africa.

Nonlinear and nonstationary framework for feature extraction and classification of motor imagery.

In this work we investigate a nonlinear approach for feature extraction of Electroencephalogram (EEG) signals in order to classify motor imagery for Brain Computer Interface (BCI). This approach is based on the Empirical Mode Decomposition (EMD) and band power (BP). The EMD method is a data-driven technique to analyze non-stationary and nonlinear signals. It generates a set of stationary time series called Intrinsic Mode Functions (IMF) to represent the original data. These IMFs are analyzed with the power spectral density (PSD) to study the active frequency range correspond to the motor imagery for each subject. Then, the band power is computed within a certain frequency range in the channels. Finally, the data is reconstructed with only the specific IMFs and then the band power is employed on the new database. The classification of motor imagery was performed by using two classifiers, Linear Discriminant Analysis (LDA) and Hidden Markov Models (HMMs). The results obtained show that the EMD method allows the most reliable features to be extracted from EEG and that the classification rate obtained is higher and better than using only the direct BP approach.