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.

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.

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.

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.