Muscular skeletal disorder is a difficult challenge faced by the working population. Motion capture (MoCap) is used for recording the movement of people for clinical, ergonomic and rehabilitation solutions. However, knowledge barriers about these MoCap systems have made them difficult to use for many people. Despite this, no state-of-the-art literature review on MoCap systems for human clinical, rehabilitation and ergonomic analysis has been conducted. A medical diagnosis using AI applies machine learning algorithms and motion capture technologies to analyze patient data, enhancing diagnostic accuracy, enabling early disease detection and facilitating personalized treatment plans. It revolutionizes healthcare by harnessing the power of data-driven insights for improved patient outcomes and efficient clinical decision-making. The current review aimed to investigate: (i) the most used MoCap systems for clinical use, ergonomics and rehabilitation, (ii) their application and (iii) the target population. We used preferred reporting items for systematic reviews and meta-analysis guidelines for the review. Google Scholar, PubMed, Scopus and Web of Science were used to search for relevant published articles. The articles obtained were scrutinized by reading the abstracts and titles to determine their inclusion eligibility. Accordingly, articles with insufficient or irrelevant information were excluded from the screening. The search included studies published between 2013 and 2023 (including additional criteria). A total of 40 articles were eligible for review. The selected articles were further categorized in terms of the types of MoCap used, their application and the domain of the experiments. This review will serve as a guide for researchers and organizational management.

Natural language processing (NLP) is the art of investigating others’ positive and cooperative communication and rapprochement with others as well as the art of communicating and speaking with others. Furthermore, NLP techniques may substantially enhance most phases of the information-system lifecycle, facilitate access to information for users, and allow for new paradigms in the usage of information-system services. NLP also has an important role in designing the study, presenting two fields converging on one side and overlapping on the other, namely the field of the NAO-robot world and the field of education, technology, and progress. The selected articles classified the study into four categories: special needs, kindergartens, schools, and universities. Our study looked at accurate keyword research. They are artificial intelligence, learning and teaching, education, NAO robot, undergraduate students, and university. In two fields of twelve journals and citations on reliable/high-reputation scientific sites, 82 scientific articles were extracted. From the Scientific Journal Rankings (SJR) website, the study samples included twelve reliable/high-reputation scientific journals for the period from 2014 to 2023 from well-known scientific journals with a high impact factor. This study evaluated the effect of a systematic literature review of NAO educational robots on language programming. It aimed to be a platform and guide for researchers, interested persons, trainees, supervisors, students, and those interested in the fields of NAO robots and education. All studies recognized the superiority and progress of NAO robots in the educational field. They concluded by urging students to publish in highly influential journals with a high scientific impact within the two fields of study by focusing on the study-sample journals.

This article provides a comprehensive review of deep learning-based blood vessel segmentation of the brain. Cerebrovascular disease develops when blood arteries in the brain are compromised, resulting in severe brain injuries such as ischemic stroke, brain hemorrhages, and many more. Early detection enables patients to obtain more effective treatment before becoming critically unwell. Due to the superior efficiency and accuracy compared to manual segmentation and other computer-assisted diagnosis procedures, deep learning algorithms have been extensively deployed in brain vascular segmentation. This study examined current articles on deep learning-based brain vascular segmentation, which examined the proposed methodologies, particularly the network architectures, and determined the model trend. We evaluated challenges and crucial factors associated with the application of deep learning to brain vascular segmentation, as well as future research prospects. This paper will assist researchers in developing more sophisticated and robust models in the future to develop deep learning solutions.

The deep learning-based image inpainting models discussed in this review are critical image processing techniques for filling in missing or removed regions in static planar images, and they have been extensively researched and applied. However, due to the rapid advancements in deep learning technologies, existing review studies exhibit shortcomings in (1) providing a comprehensive and systematic classification of strategies for these models, (2) analyzing loss functions and evaluation metrics, and (3) organizing and categorizing data and application domains. To address these three aspects, this paper provides a systematic literature review of deep learning-based inpainting models. First, we rigorously classify the strategies used in image inpainting networks into four categories: layer-based, connection-based, multi-network, and multi-modal inpainting strategies. This classification is determined by the composition of the models, ensuring it comprehensively covers the vast majority of inpainting network strategies. Second, to facilitate a more effective study and comparison of loss functions and evaluation metrics, we categorize them into pixel-based, feature-based, and model-based approaches, conducting a thorough analysis of each. Finally, we categorize image inpainting applications into three main types according to dataset characteristics: natural image inpainting, detection image inpainting, and artistic image inpainting, offering insights into each specific domain.

Skin cancer especially when detected early can be easily treated, but its diagnosis is complicated by the minimal difference in the appearance of early lesions and the requirement of a precise diagnostic technique. The goal of this intensive literature review is to evaluate the progressive enhancements of deep learning (DL) and machine learning (ML) methods for transferring early-stage skin cancer identification in terms of accuracy and in terms of usability for real-world clinical applications. By using support vector machines, convolutional neural networks, and ensemble methods, we assess the performance of such algorithms in the classification and segmentation of skin lesions within various datasets. The challenges outlined in the review include the following: first, sparsity of data, second, variation in the looks in lesions, and third, imbalance of data within classes. Furthermore, issues that are still open to investigation are also presented, including the restricted number of algorithms for which the developed DL/ML models can be interpretable and the variability of the results assessment criteria used in different investigations. We then propose possible approaches to these issues such as data augments, multimodal learning, and the inclusion of explainable artificial intelligence approaches. The strengths of the present study consist of a comprehensive review of the limitations of contemporary methodologies and recommendations for future research on DL/ML-based systems for the early diagnosis of skin cancer. This research aims to highlight the best techniques and identify areas for future improvement. The study highlighted the key challenges of evaluating skin lesion segmentation and classification techniques, for instance, small sample size dataset, or selective and random image acquisition or even racial prejudice.

Identifying the gender of a person and his age by way of speaking is considered a crucial task in computer vision. It is a very important and active research topic with many areas of application, such as identifying a person, trustworthiness, demographic analysis, safety and health knowledge, visual monitoring, and aging progress. Data matching is to identify the gender of the person and his age. Thus, the study touches on a review of many research papers from 2016 to 2022. At the heart of the topic, many systematic reviews of multimodal pedagogies in Age and Gender Estimation for Adaptive were undertaken. However, no current study of the theme concerns connected to multimodal pedagogies in Age and Gender Estimation for Adaptive Learning has been published. The multimodal pedagogies in four different databases within the keywords indicate the heart of the topic. A qualitative thematic analysis based on 48 articles found during the search revealed four common themes, such as multimodal engagement and speech with the Human-Robot Interaction life world. The study touches on the presentation of many major concepts, namely Age Estimation, Gender Estimation, Speaker Recognition, Speech recognition, Speaker Localization, and Speaker Gender Identification. According to specific criteria, they were presented to all studies. The essay compares these themes to the thematic findings of other review studies on the same topic such as multimodal age, gender estimation, and dataset used. The main objective of this paper is to provide a comprehensive analysis based on the surveyed region. The study provides a platform for professors, researchers, and students alike, and proposes directions for future research.