How a Diverse Research Ecosystem Has Generated New Rehabilitation Technologies: Review of NIDILRR’s Rehabilitation Engineering Research Centers

Over 50 million United States citizens (1 in 6 people in the US) have a developmental, acquired, or degenerative disability. The average US citizen can expect to live 20% of his or her life with a disability. Rehabilitation technologies play a major role in improving the quality of life for people with a disability, yet widespread and highly challenging needs remain. Within the US, a major effort aimed at the creation and evaluation of rehabilitation technology has been the Rehabilitation Engineering Research Centers (RERCs) sponsored by the National Institute on Disability, Independent Living, and Rehabilitation Research. As envisioned at their conception by a panel of the National Academy of Science in 1970, these centers were intended to take a “total approach to rehabilitation”, combining medicine, engineering, and related science, to improve the quality of life of individuals with a disability. Here, we review the scope, achievements, and ongoing projects of an unbiased sample of 19 currently active or recently terminated RERCs. Specifically, for each center, we briefly explain the needs it targets, summarize key historical advances, identify emerging innovations, and consider future directions. Our assessment from this review is that the RERC program indeed involves a multidisciplinary approach, with 36 professional fields involved, although 70% of research and development staff are in engineering fields, 23% in clinical fields, and only 7% in basic science fields; significantly, 11% of the professional staff have a disability related to their research. We observe that the RERC program has substantially diversified the scope of its work since the 1970’s, addressing more types of disabilities using more technologies, and, in particular, often now focusing on information technologies. RERC work also now often views users as integrated into an interdependent society through technologies that both people with and without disabilities co-use (such as the internet, wireless communication, and architecture). In addition, RERC research has evolved to view users as able at improving outcomes through learning, exercise, and plasticity (rather than being static), which can be optimally timed. We provide examples of rehabilitation technology innovation produced by the RERCs that illustrate this increasingly diversifying scope and evolving perspective. We conclude by discussing growth opportunities and possible future directions of the RERC program

Validation of a development methodology and tool for IoT-based systems through a case study for visually impaired people

In this article, we validate the Test-Driven Development Methodology for Internet of Things (IoT)-based Systems (TDDM4IoTS) and its companion tool, called Test-Driven Development Tool for IoT-based Systems (TDDT4IoTS). TDDM4IoTS consists of 11 stages, including activities ranging from system requirements gathering to system maintenance. To evaluate the effectiveness of TDDM4IoTS and TDDT4IoTS, in the last four academic years from 2019, System Engineering students have developed several IoT-based systems as part of their training, from the sixth semester (third academic year). Ñawi (phonetically, Gnawi), which is the case study presented herein, is one of them, and intends to assist visually impaired people to move through open environments. Ñawi consists of a device, a mobile application and a web application. The device interacts with the environment and issues alerts to the user whenever it recognizes obstacles in their path. The mobile application targets two user roles: assisted person and caregiver. Assisted people can use the device and log in into a server when they leave home, so that the mobile application identifies and notifies obstacles in their path. All the collected data is gathered into the server, so that caregivers receive notifications and can monitor the location of their assisted people at any place and time. The web application allows caregivers to query and view more extensive information (details of events, trajectories, etc.). TDDM4IoTS has been evaluated regarding both the roles of the project members and the development cycle stages. A survey was used to evaluate the methodology. Out of a total of 47 respondents, 30 had used TDDM4IoTS and 96.66% of them were very satisfied or satisfied, with nobody unsatisfied

The use of extended reality and machine learning to improve healthcare and promote greenhealth

Com a Quarta Revolução Industrial, a propagação da Internet das Coisas, o avanço nas áreas de Inteligência Artificial e de Machine Learning até à migração para a Computação em Nuvem, o termo "Ambientes Inteligentes" cada vez mais deixa de ser uma idealização para se tornar realidade. Da mesma forma as tecnologias de Realidade Extendida também elas têm aumentado a sua presença no mundo tecnológico após um "período de hibernação", desde a popularização do conceito de Metaverse assim como a entrada das grandes empresas informáticas como a Apple e a Google num mercado onde a Realidade Virtual, Realidade Aumentada e Realidade Mista eram dominadas por empresas com menos experiência no desenvolvimento de sistemas (e.g. Meta), reconhecimento a nível mundial (e.g. HTC Vive), ou suporte financeiro e confiança do mercado. Esta tese tem como foco o estudo do potencial uso das tecnologias de Realidade Estendida de forma a promover Saúde Verde assim como seu uso em Hospitais Inteligentes, uma das variantes de Ambientes Inteligentes, incorporando Machine Learning e Computer Vision, como ferramenta de suporte e de melhoria de cuidados de saúde, tanto do ponto de vista do profissional de saúde como do paciente, através duma revisão literarária e análise da atualidade. Resultando na elaboração de um modelo conceptual com a sugestão de tecnologias a poderem ser usadas para alcançar esse cenário selecionadas pelo seu potencial, sendo posteriormente descrito o desenvolvimento de protótipos de partes do modelo conceptual para Óculos de Realidade Extendida como validação de conceito.With the Fourth Industrial Revolution, the spread of the Internet of Things, the advance in the areas of Artificial Intelligence and Machine Learning until the migration to Cloud Computing, the term "Intelligent Environments" increasingly ceases to be an idealization to become reality. Likewise, Extended Reality technologies have also increased their presence in the technological world after a "hibernation period", since the popularization of the Metaverse concept, as well as the entry of large computer companies such as Apple and Google into a market where Virtual Reality, Augmented Reality and Mixed Reality were dominated by companies with less experience in system development (e.g. Meta), worldwide recognition (e.g. HTC Vive) or financial support and trust in the market. This thesis focuses on the study of the potential use of Extended Reality technologies in order to promote GreenHealth as well as their use in Smart Hospitals, one of the variants of Smart Environments, incorporating Machine Learning and Computer Vision, as a tool to support and improve healthcare, both from the point of view of the health professional and the patient, through a literature review and analysis of the current situation. Resulting in the elaboration of a conceptual model with the suggestion of technologies that can be used to achieve this scenario selected for their potential, and then the development of prototypes of parts of the conceptual model for Extended Reality Headsets as concept validation

New Updates in E-Learning

This book presents state-of-the-art educational technologies and teaching methodologies and discusses future educational philosophies in support of the global academic society. New Updates in E-Learning is a collection of chapters addressing important issues related to effective utilization of the Internet and Cloud Computing, virtual robotics, and real-life application of hybrid educational environments to enhance student learning regardless of geographical location or other constraints. Over ten chapters, the book discusses the current and future evolution of educational technologies and methodologies and the best academic practices in support of providing high-quality education at all academic levels

The Eye: A Light Weight Mobile Application for Visually Challenged People Using Improved YOLOv5l Algorithm

The eye is an essential sensory organ that allows us to perceive our surroundings at a glance. Losing this sense can result in numerous challenges in daily life. However, society is designed for the majority, which can create even more difficulties for visually impaired individuals. Therefore, empowering them and promoting self-reliance are crucial. To address this need, we propose a new Android application called “The Eye” that utilizes Machine Learning (ML)-based object detection techniques to recognize objects in real-time using a smartphone camera or a camera attached to a stick. The article proposed an improved YOLOv5l algorithm to improve object detection in visual applications. YOLOv5l has a larger model size and captures more complex features and details, leading to enhanced object detection accuracy compared to smaller variants like YOLOv5s and YOLOv5m. The primary enhancement in the improved YOLOv5l algorithm is integrating L1 and L2 regularization techniques. These techniques prevent overfitting and improve generalization by adding a regularization term to the loss function during training. Our approach combines image processing and text-to-speech conversion modules to produce reliable results. The Android text-to-speech module is then used to convert the object recognition results into an audio output. According to the experimental results, the improved YOLOv5l has higher detection accuracy than the original YOLOv5 and can detect small, multiple, and overlapped targets with higher accuracy. This study contributes to the advancement of technology to help visually impaired individuals become more self-sufficient and confident. Doi: 10.28991/ESJ-2023-07-05-011 Full Text: PD

Affective Computing for Emotion Detection using Vision and Wearable Sensors

The research explores the opportunities, challenges, limitations, and presents advancements in computing that relates to, arises from, or deliberately influences emotions (Picard, 1997). The field is referred to as Affective Computing (AC) and is expected to play a major role in the engineering and development of computationally and cognitively intelligent systems, processors and applications in the future. Today the field of AC is bolstered by the emergence of multiple sources of affective data and is fuelled on by developments under various Internet of Things (IoTs) projects and the fusion potential of multiple sensory affective data streams. The core focus of this thesis involves investigation into whether the sensitivity and specificity (predictive performance) of AC, based on the fusion of multi-sensor data streams, is fit for purpose? Can such AC powered technologies and techniques truly deliver increasingly accurate emotion predictions of subjects in the real world? The thesis begins by presenting a number of research justifications and AC research questions that are used to formulate the original thesis hypothesis and thesis objectives. As part of the research conducted, a detailed state of the art investigations explored many aspects of AC from both a scientific and technological perspective. The complexity of AC as a multi-sensor, multi-modality, data fusion problem unfolded during the state of the art research and this ultimately led to novel thinking and origination in the form of the creation of an AC conceptualised architecture that will act as a practical and theoretical foundation for the engineering of future AC platforms and solutions. The AC conceptual architecture developed as a result of this research, was applied to the engineering of a series of software artifacts that were combined to create a prototypical AC multi-sensor platform known as the Emotion Fusion Server (EFS) to be used in the thesis hypothesis AC experimentation phases of the research. The thesis research used the EFS platform to conduct a detailed series of AC experiments to investigate if the fusion of multiple sensory sources of affective data from sensory devices can significantly increase the accuracy of emotion prediction by computationally intelligent means. The research involved conducting numerous controlled experiments along with the statistical analysis of the performance of sensors for the purposes of AC, the findings of which serve to assess the feasibility of AC in various domains and points to future directions for the AC field. The AC experiments data investigations conducted in relation to the thesis hypothesis used applied statistical methods and techniques, and the results, analytics and evaluations are presented throughout the two thesis research volumes. The thesis concludes by providing a detailed set of formal findings, conclusions and decisions in relation to the overarching research hypothesis on the sensitivity and specificity of the fusion of vision and wearables sensor modalities and offers foresights and guidance into the many problems, challenges and projections for the AC field into the future

Using Information Communications Technologies to Implement Universal Design for Learning

The purpose of this paper is to assist Ministries of Education, their donors and partners, Disabled Persons Organizations (DPOs), and the practitioner community funded by and working with USAID to select, pilot, and (as appropriate) scale up ICT4E solutions to facilitate the implementation of Universal Design for Learning (UDL), with a particular emphasis on supporting students with disabilities to acquire literacy and numeracy skills. The paper focuses primarily on how technology can support foundational skills acquisition for students with disabilities, while also explaining when, why, and how technologies that assist students with disabilities can, in some applications, have positive impacts on all students’ basic skills development. In 2018, USAID released the Toolkit for Universal Design for Learning to Help All Children Read, section 3.1 of which provides basic information on the role of technologies to support UDL principles and classroom learning. This paper expands upon that work and offers more extensive advice on using ICT4E1 to advance equitable access to high quality learning. Like the UDL toolkit, the audience for this guide is mainly Ministries of Education and development agencies working in the area of education, but this resource can also be helpful for DPOs and non-governmental organizations (NGOs) wishing to pilot or spearhead ICT initiatives. Content for this paper was informed by expert interviews and reviews of field reports during 2018. These included programs associated with United Nations, Zero Project, World Innovation Summit, UNESCO Mobile Learning Awards, and USAID’s All Children Reading: A Grand Challenge for Development. Relevant case studies of select education programs integrating technology to improve learning outcomes for students with disabilities were summarized for this document

AI-Generated Content (AIGC): A Survey

To address the challenges of digital intelligence in the digital economy, artificial intelligence-generated content (AIGC) has emerged. AIGC uses artificial intelligence to assist or replace manual content generation by generating content based on user-inputted keywords or requirements. The development of large model algorithms has significantly strengthened the capabilities of AIGC, which makes AIGC products a promising generative tool and adds convenience to our lives. As an upstream technology, AIGC has unlimited potential to support different downstream applications. It is important to analyze AIGC's current capabilities and shortcomings to understand how it can be best utilized in future applications. Therefore, this paper provides an extensive overview of AIGC, covering its definition, essential conditions, cutting-edge capabilities, and advanced features. Moreover, it discusses the benefits of large-scale pre-trained models and the industrial chain of AIGC. Furthermore, the article explores the distinctions between auxiliary generation and automatic generation within AIGC, providing examples of text generation. The paper also examines the potential integration of AIGC with the Metaverse. Lastly, the article highlights existing issues and suggests some future directions for application.Comment: Preprint. 14 figures, 4 table