Advances in Human-Computer Interaction

In the day-to-day life of communities, good communication channels are crucial for mutual understanding. The hearing-impaired community uses sign language, which is a visual and gestural language. In terms of orientation and expression, it is separate from written and spoken languages. Despite the fact that sign language is an excellent platform for communication among hearing-impaired persons, it has created a communication barrier between hearing-impaired and non-disabled people. To address this issue, researchers have proposed sign language to text translation systems for English and other European languages as a solution. The goal of this research is to design and develop an Amharic digital text converter system using Ethiopian sign language. The proposed system was created with the help of two key deep learning algorithms: a pretrained deep learning model and a Long Short-Term Memory (LSTM). The LSTM was used to extract sequence information from a sequence of image frames of a specific sign language, while the pretrained deep learning model was used to extract features from single frame images. The dataset used to train the algorithms was gathered in video format from Addis Ababa University. Prior to feeding the obtained dataset to the deep learning models, data preprocessing activities such as cleaning and video to image frame segmentation were conducted. The system was trained, validated, and tested using 80%, 10%, and 10% of the 2475 images created during the preprocessing step. Two pretrained deep learning models, EfficientNetB0 and ResNet50, were used in this investigation, and they attained an accuracy of 72.79%. In terms of precision and f1-score, ResNet50 outperformed EfficientNetB0. For the proposed system, a graphical user interface prototype was created, and the best performing model was chosen and implemented. The proposed system can be utilized as a starting point for other researchers to improve upon, based on the outcomes of the experiment. More high-quality training datasets and high-performance training machines, such as GPU-enabled computers, can be added to the system to improve it.

Virtual Reality (VR) and Augmented Reality (AR) have revolutionized technology and taken the world by storm. They established the foundation for numerous future innovations. Virtual and augmented reality are now widely employed to improve user experiences in various areas. Over time, more and more companies and businesses have begun to use this cutting-edge technology to improve their products and services. Recently, the attention to VR and AR has exploded with the concept “Metaverse” surfacing in mainstream media. Many major companies have already set their goals in motion and are working on building the core of their metaverses. This review paper focuses on explaining the concept of the metaverse, its history, and its associated benefits. Through a survey, it helps understand people’s concerns with the metaverse and how it can impact and affect humans mentally, physically, and psychologically. The analysis of this paper can help humans prepare themselves for what the new technologies have to offer, in addition to assisting companies in building a flawless metaverse.

The technical improvements in healthcare sector today have given rise to many new inventions in the field of artificial intelligence. Patterns for disease identification are carried out, and the onset of prediction of many diseases is detected. Diseases include diabetes mellitus disease, fatal heart diseases, and symptomatic cancer. There are many algorithms that have played a critical role in the prediction of diseases. This paper proposes an ML based approach for diabetes mellitus disease prediction. For diabetes prediction, many ML algorithms are compared and used in the proposed work, and finally the three ML classifiers providing the highest accuracy are determined: RF, GBM, and LGBM. The accuracy of prediction is obtained using two types of datasets. They are Pima Indians dataset and a curated dataset. The ML classifiers LGBM, GB, and RF are used to build a predictive model, and the accuracy of each classifier is noted and compared. In addition to the generalized prediction mechanism, the data augmentation technique is also used, and the final accuracy of prediction is obtained for the classifiers LGBM, GB, and RF. A comparative study and demonstration between augmentation and non-augmentation are also discussed for the two datasets used in order to further improve the performance accuracy for predicting diabetes disease.

Despite the efforts of emerging technologies in the healthcare system, there is still a slower rate of acceleration in prehospital settings compared with the hospitals in digital transformation adaptation. The acknowledgment that digital transformation is significant to healthcare is reflected in planning for the future of digital healthcare. Thus, this study aimed to measure the usability of the electronic patient care report (ePCR) system among emergency medical services (EMS) staff who work in prehospital settings. A descriptive cross-sectional correlation study was used. Two hundred fifty EMS staff who are working in the prehospital setting at Saudi Red Crescent Authority in the Kingdom of Saudi Arabia were surveyed, and the response rate was 79.2% (198). An adapted tool of the Computer System Usability Questionnaire survey was used to collect data. The data were coded numerically and subjected to descriptive and inferential statistical analysis including Pearson’s correlation coefficient using the statistical software (SPSS 21). The majority of the participants rate their ePCR system as “useable” at a high level with a score of 3.41 (SD = 1.021). The overall mean of the ePCR system’s three subscales: system usefulness, information quality, interface quality, and overall satisfaction were 3.39 (SD = 1.152), 3.30 (SD = 1.052), 3.57 (SD = 1.064), and 3.37 (SD = 1.239), respectively. The least liked aspect of ePCR system software was information quality 81 (40.9%). Furthermore, there was a significant correlation between the age of EMS staff and the usability of the ePCR system (r = −0.150, ). The results suggest that healthcare institutions’ policy and decision-makers pay close attention to performing standardized training for the staff on their ePCR system before going to the field to increase efficiency and productivity. Furthermore, the users in this study identified other system features that, if included, could have enhanced usability, and improved functions and capabilities of the design to meet the EMS staff’s expectations.

Facial Expression Recognition (FER) is an active research field at present. Deep learning is a good method that is widely used in this field but it has extreme hardware requirements and it is hard to apply in normal terminal devices. So, many other methods are being researched to apply FER in such devices and systems. This work proposes fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features in facial expression recognition to enhance the recognition rate that can apply FER in the kind of devices and systems. The work included the main steps such as the technique of cropping an input face image from a camera or dataset, the approach of dividing face images into nonoverlap regions for extracting LBP features, applying the fresh modeling of Combined Gray Local Binary Pattern (CGLBP) for extracting features, using uniform feature to reduce the lengths of descriptors, and finally using Support Vector Machine (SVM) for emotion classification. Four popular facial emotion datasets are used in experiments and their results demonstrate that the recognition rate of the proposed method is better in comparison with two types of existent features: Local Binary Pattern (LBP) and Combined Local Binary Pattern (CLBP). The accuracy of experiments performed on four facial expression datasets with different sizes is from about 95% to more than 99%.

Due to the importance of design guidelines in facilitating user experience and promoting efficiency, it is essential to determine the effectiveness of certain design guidelines for a specific population. There have been a number of challenges reported in the literature when designing learning courses for Arabic users. Confirming the suitability of design guidelines for specific users’ needs can be challenging in the context of the Arabic language. This is mainly due to the unique characteristics of this language, which contribute to users’ satisfaction with the interface. This study evaluated the feasibility of using Arabic User Interface (UI) guidelines for tablet PCs. The UI guidelines were developed, evaluated, and refined using the Delphi technique. A total of six UI design experts were recruited for this study. The results revealed a number of guidelines that can be used in the design of Arabic UI. The proposed guidelines can standardise the design of Arabic UI by offering future directions on how to effectively apply design principles for tablet PCs.