Field Of Assistive Technology Research Articles

BUILDING DOCUMENT READING ASSISTANT FOR THE VISUALLY IMPAIRED

This study introduces a solution that applies document analysis and recognition technologies to enhance document accessibility for individuals with visual impairments. The objective is to develop an algorithm capable of accurately analyzing the content of document components and converting them into voice format. Leveraging the pre-trained YOLOv8 model for document analysis and optical character recognition technology, the image annotation model uses the AIAnytime API and Pix2Tex technology to extract LaTeX code from images, facilitating the conversion of mathematical formulas into spoken words. The research results demonstrate significant progress in effectively supporting document reading, making a meaningful contribution to the field of assistive technology for the visually impaired.

Sign Language Detection Using Machine Learning

This paper investigates the application of machine learning for sign language detection. The objective is to develop a model that translates sign language into spoken language, bridging the communication gap between deaf and hearing individuals. You Only Look Once (YOLO), a deep learning object detection algorithm, is employed to train a model on a dataset of labeled sign language images derived from video data. The system achieves real-time sign detection in videos. However, challenges include the scarcity of large, labeled datasets and the inherent ambiguity of certain signs, which can lead to reduced detection accuracy. This research contributes to the field of Assistive Technologies (AT) by promoting accessibility and social inclusion for the deaf community.

Robotic technology in Autism intervention: Design and application of the CARS cat-like robot

This study introduces a novel robotic system designed to aid individuals with Autism Spectrum Disorder (ASD) in enhancing their social communication and interaction skills. Incorporating advanced artificial intelligence, sensors, and image recognition technologies, the robot simulates human social interactions and provides tailored feedback, aiming to improve the quality of life for individuals with ASD. Through a detailed examination of hardware design, software functionalities, and data processing mechanisms, this paper evaluates the system's practical applications and identifies ongoing challenges and potential improvements. The findings suggest that while the robotic system significantly enhances social interaction and learning for autistic individuals, challenges such as technological adaptability, cost, and physical design require further innovation. This research contributes to the field of assistive technologies by offering insights into how robotic systems can be optimized to meet the unique needs of the autism community, thus paving the way for more effective therapeutic tools.

Development of a Sign Language recognizer using LSTM

Sign language recognition plays a crucial role in bridging communication gaps between the deaf community and the hearing population. This paper presents the development of a sign language recognizer using Long Short-Term Memory (LSTM) networks, a type of recurrent neural network well-suited for sequence prediction tasks. The proposed model aims to accurately interpret hand gestures into text, enhancing accessibility and communication for individuals with hearing impairments. Our approach utilizes a custom-built dataset of sign language gestures, which undergoes preprocessing to extract relevant features such as hand position, orientation, and movement. The LSTM-based architecture is designed to capture the temporal dynamics of these gestures, enabling the recognition of complex sign language patterns. We train the model using a supervised learning approach. Our method demonstrates superior accuracy in recognizing sign language gestures compared to traditional machine learning methods and other deep learning architectures. This advancement contributes significantly to the field of assistive technologies. Future work will explore the integration of additional modalities, such as facial expressions and body movements, to further enhance the system's performance and usability.

DEVELOPMENT OF SMART TOILET SYSTEM FOR OLD AGE AND SPECIALLY ABLED PERSON

This research paper presents the development of a novel smart toilet system designed to the unique needs of elderly individuals and persons with disabilities. The primary objective of the project is to monitor the health conditions of users while inside the toilet, this system incorporates advanced sensors, user-friendly interfaces, and automation to provide a seamless and dignified toileting experience for individuals with diverse mobility challenges. Integral to the system is a comprehensive health monitoring component. Utilizing sensors, the system measures the user's body temperature and heartbeat. This real-time health data is crucial for timely detection of potential health issues, promoting preventive healthcare and immediate response if needed. This research contributes to the evolving field of assistive technologies, specifically addressing the needs of aging populations and individuals with disabilities. The integration of mechanical design, health monitoring, and cloud-based connectivity exemplifies a holistic approach to smart healthcare solutions, fostering independence and improved quality of life for users. Key Words: Smart and healthy living, Emergency assistance, Care, Health.

SOLAR BASED WHEEL CHAIR FOR DISABLED

The Solar Based Wheelchair for Disabled (SBWCD) is a groundbreaking project aimed at enhancing mobility and independence for individuals with disabilities by harnessing the power of solar energy. Traditional wheelchairs often rely on manual propulsion or battery-powered motors, which can be limiting in terms of range, accessibility to charging stations, and environmental impact. In response to these challenges, the SBWCD project introduces a novel approach by integrating solar panels into the design of wheelchairs to harness solar energy and convert it into electrical power for propulsion. This innovative solution offers numerous benefits, including extended range, reduced reliance on grid electricity, and decreased carbon footprint. The SBWCD features a lightweight and durable frame equipped with high-efficiency solar panels mounted on the backrest and armrests, maximizing exposure to sunlight while maintaining user comfort and ergonomics. The solar panels are connected to an onboard battery system, which stores excess energy generated during daylight hours for use during periods of low sunlight or at night. This hybrid power system ensures continuous operation of the wheelchair regardless of environmental conditions, providing users with greater autonomy and freedom of movement. Moreover, the SBWCD is designed with user-centric features and accessibility in mind. Adjustable seating, armrests, and footrests accommodate users of varying sizes and preferences, while ergonomic controls and intuitive interfaces ensure ease of operation for individuals with limited mobility or dexterity. Additionally, safety features such as anti-tip mechanisms, seat belts, and obstacle detection systems enhance user confidence and mitigate risks associated with wheelchair use in diverse environments. In summary, the Solar Based Wheelchair for Disabled (SBWCD) project represents a significant advancement in assistive technology, offering a sustainable and user-friendly solution for individuals with disabilities. By harnessing the abundant and renewable energy of the sun, SBWCD empowers users to navigate their surroundings with greater independence, efficiency, and environmental consciousness. As the demand for accessible and eco-friendly mobility solutions continues to grow, the SBWCD project serves as a beacon of innovation and inclusivity in the field of assistive technology.

Empowering the Blind: An AI Driven Indoor Assistance for Visually Impaired

Abstract: This review paper presents the design and development of a comprehensive mobile application aimed at enhancing the daily lives of visually impaired individuals. The proposed mobile app offers real-time assistance for various visual recognition tasks, including object detection, distance estimation, currency recognition, barcode detection, color recognition, and emotion analysis. The primary objective of this application is to provide visually impaired users with a powerful tool to navigate their surroundings, identify objects and currency, scan barcodes, discern colors, and even gauge the emotions of those they interact with. The app integrates pre-trained deep learning models for object detection and facial emotion analysis, computer vision techniques for distance estimation, and optical character recognition for currency recognition. User feedback and engagement are central to the ongoing improvement of the application, ensuring that it remains a valuable resource for the community it serves. Ethical considerations and privacy concerns are also addressed, with a focus on data security and user privacy. By presenting the development and functionalities of this app, this review paper not only contributes to the field of assistive technology but also underscores the importance of harnessing cutting-edge technology to improve the quality of life for visually impaired individuals.

Artificial Intelligence-Powered Mobile Application to Help Visually Impaired People

This paper introduces an innovative mobile application powered by Artificial Intelligence (AI) technology, designed to significantly improve the daily lives of visually impaired individuals. The application incorporates advanced features such as object recognition, color recognition, offline functionality, currency notes recognition, barcode reading, text reader. These features collectively empower users by providing real-time information about their surroundings, facilitating safe and independent, enabling secure financial transactions, and enhancing overall accessibility. The integration of AI algorithms ensures accurate and efficient performance, making the application a valuable tool for improving the quality of life and promoting inclusivity for visually impaired individuals. This research contributes to the field of assistive technology by showcasing the potential of AI- driven solutions in addressing accessibility challenges and fostering independence among diverse user groups.

Enhancing Digital Accessibility and Learning Outcomes: Key2enable's Assistive Technology for Children with Disabilities

This paper showcases how Key2enable's assistive technology works as a holistic solution comprising hardware and software that can enable people with severe disabilities to communicate, learn, engage, and autonomously use technology. This is an exploratory study and will develop a pathway to new developments in the field of assistive technology and portray how motivation plays a key role. The sample consists of 10 children with motor impairments and on the neurodivergence spectrum. There is insufficient research on this relatively small population and this study aims to address how using the right technology for cognitive stimulation can significantly accelerate progress and bring equal opportunities to all in this constantly changing digital world.

Contemporary and Future Development of 3D Printing Technology in the Field of Assistive Technology, Orthotics and Prosthetics.

3D printing is considered as a helpful technology that facilitates innovative assistive technology, orthotics, and prosthetics development. This technology could likely contribute to positive treatment outcomes. It could also mitigate the challenges encountered when using the traditional methods. Our team's research in the application of 3D printing in prosthetics, orthotics and biomedical technology has shown beneficial results in its use. This article gives a general description on application of CAD/CAM, digitalization and 3d printing in this industry followed by short description of two spinal-related projects conducted in our research team. Technological and clinical challenges on utilization of this technology have been listed. Finally, this manuscript provides recommendation for broader applications and developments of the aforementioned technology through interdisciplinary practices. A glimpse into the future of 3D printing in the healthcare industry shows that this industry is poised to continue having a significant impact in this sector. It should be emphasized that assistive technology, orthotics, and prosthetics require a human touch and connection, and no digital tool or technology can replace such requirements. Indeed, multi-disciplinary collaboration is the key to the success of applications of 3D printing.

The International Functional Electrical Stimulation Society (IFESS): Highlights from the IFESS conference at Rehabweek 2023.

The annual conference of the International Functional Electrical Stimulation Society (IFESS) was held in conjunction with the 7th RehabWeek Congress, from September 24 to 28, 2023 at the Resorts World Convention Centre on Sentosa Island, in Singapore. The Congress was a joint meeting of the International Consortium on Rehabilitation Technology (ICRT) together with 10 other societies in the field of assistive technology and rehabilitation engineering. The conference features comprehensive blend of technical and clinical context of FES, a sustained value the society has offered over many years. The cross- and inter- disciplinary approach of medicine, engineering, and science practiced in the FES community had enabled vibrant interaction, creation, and development of impactful and novel contributions to the field of FES, translating FES directly into highly relevant and sustainable solutions for the users.

Accessible Prosthetic Arms: Victoria Hand Project and The Impact of 3D Printing.

Victoria Hand Project (VHP) is a Canadian charity with a mission to provide 3D printed prosthetic arms to people in-need across the world, by partnering with prosthetic care providers. This article explores the journey of VHP, sharing insights, lessons learned, ongoing directions, and the impact of 3D printing on prosthetic care for people with upper-limb amputation. Benefits such as affordability and customization are explored, as well as the challenges encountered, including quality control and the steep learning curve associated with working in the digital 3D space. Through this article, the potential of 3D printing to continue to transform the field of assistive technology and prosthetic and orthotic applications is underscored, especially when used for collaborative, humanitarian initiatives.

Gesture-to-Text: A Real-Time Indian Sign Language Translator with Pose Estimation and LSTMs

In recent years, there have been notable advancements in technology, deep learning, and pose detection. One significant application of these advancements pertains to the real-time detection of sign language from video sources. The motivation behind this research stems from the pressing societal need to enhance the quality of life for individuals with speech impairments. Given the current prominence of online meetings, exacerbated by the COVID-19 pandemic, there is a growing need for systems that can provide individuals with speech impairments greater independence in communication, eliminating the requirement for a human translator. This research proposal advocates for a solution that leverages PoseNet algorithms for the extraction of key pose points, which are subsequently employed within LSTM models for the predictive modeling of sign language gestures. This research paper aims to make several notable contributions to the field of assistive technology and human-computer interaction. The achieved accuracy stands at an impressive 98%, underscoring the robustness and precision of our proposed system.

Adapting Assistive Technology to Diverse Learning Needs in Inclusive Education

The goal of inclusive education is to give everyone, including those with disabilities, equal access to learning opportunities. Customizing assistive technology is essential to promoting diversity in learning environments. With a focus on how assistive technology can improve accessibility, rendezvous, and learning outcomes, this abstract examines the ever-changing field of assistive technology in the context of varied learning needs. This paper explores the opportunities and difficulties of incorporating assistive technology into inclusive education, providing practical tips for implementation and customization. This abstract illuminated the transformative potential of adaptive technology to empower students with varying abilities and contribute to a more inclusive and supportive learning environment by looking at case studies and emerging trends.

Compressive sensing applied to SSVEP-based brain–computer interface in the cloud for online control of a virtual wheelchair

People with severe motor disabilities have great challenges in achieving independence, particularly driving or controlling manually robotic wheelchairs. Existing assistive technologies for robotic wheelchair control often fail to address user needs adequately, becoming necessary an appropriate evaluation of usability and user’s satisfaction for these systems. In fact, user experience and satisfaction are crucial in determining effectiveness, usability and acceptance of these technologies. The aim is to enhance the quality of life of individuals with severe motor disabilities, increasing their independence to perform some daily activities, such as locomotion. This study proposes a cloud Steady-State Visual Evoked Potential (SSVEP)-based Brain–Computer Interface (BCI) system to evaluate its performance through the user experience in commanding a virtual electric-powered wheelchair in a training simulator. Moreover, this study compares traditional wheelchair command modes, such as input interfaces using JoyStick (JS) and Eye Tracker (ET), through standardized questionnaires to measure motivation, ease of use, workload, discomfort, sense of presence and other relevant parameters. The analysis of user experience reveals high levels of motivation, enjoyment, and usability when using the cloud SSVEP-based BCI proposed here. Although our BCI demands higher mental work and discomforts than both ET and JS, its potential for improving the user’s performance is demonstrated here. The findings of this research provide valuable insights for researchers and practitioners in the field of assistive technologies, enabling the development of more personalized systems based on the user’s experience. The main contributions of this study lie in a cloud BCI approach and a comprehensive evaluation methodology to evaluate the user’s experience.

Recommendations for promoting user agency in the design of speech neuroprostheses.

Brain-computer interfaces (BCI) that directly decode speech from brain activity aim to restore communication in people with paralysis who cannot speak. Despite recent advances, neural inference of speech remains imperfect, limiting the ability for speech BCIs to enable experiences such as fluent conversation that promote agency - that is, the ability for users to author and transmit messages enacting their intentions. Here, we make recommendations for promoting agency based on existing and emerging strategies in neural engineering. The focus is on achieving fast, accurate, and reliable performance while ensuring volitional control over when a decoder is engaged, what exactly is decoded, and how messages are expressed. Additionally, alongside neuroscientific progress within controlled experimental settings, we argue that a parallel line of research must consider how to translate experimental successes into real-world environments. While such research will ultimately require input from prospective users, here we identify and describe design choices inspired by human-factors work conducted in existing fields of assistive technology, which address practical issues likely to emerge in future real-world speech BCI applications.

Assessing the implementation of user-centred design standards on assistive technology for persons with visual impairments: a systematic review.

https://www.crd.york.ac.uk/PROSPERO/display_record.php?RecordID=307466 PROSPERO (CRD42022307466).

Toward a lightweight ASR solution for atypical speech on the edge

In this article, to the purpose of simplifying challenges in designing automatic speech recognition (ASR) systems working on disordered speech, we focus on an isolated word recognition solution based on a convolutional neural network architecture to predict the presence of precise voice commands within atypical utterances. Italian speech recognizers have trained from scratch on custom datasets by following a speaker dependent approach, then their performances (in terms of word error rate) have been investigated with the collaboration of 16 Italian speakers with motor and speech impairments. Our ASR system works on Mel-frequency Cepstral Coefficients features extraction method, and thanks to its inner structure, the trained ASR model can be deployed on edge computing nodes where the local inference task requires limited computational resources. We acknowledge that it may have significant implications in the field of assistive technology. Then, in the end of the paper, we present a prototype of an edge computing device aimed at supporting the voice interaction between its user with speech disorders and his/her surrounding smart environment through a custom interaction with virtual assistants’ services.

Designing Assistive Devices to Support Learning for Students with Hearing Impairments

Sign language recognition has become a crucial area of research, especially in the field of assistive technology for hearing- and speech-impaired individuals. This paper presents a new project aimed at developing a device that translates American Sign Language (ASL) into spoken language using the Internet of Things (IoT) and sensors. The system design covers 30 basic gestures and focuses on making communication easier for hearing-impaired students in technical programs. The project addresses the challenges and limitations of sign language recognition, and aims to reduce the communication gap between hearing-impaired and normal individuals. The implementation of this project is expected to result in a more accessible and inclusive society for all.

How the CYBATHLON Competition Has Advanced Assistive Technologies

Approximately 1.1. billion people worldwide live with some form of disability, and assistive technology has the potential to increase their overall quality of life. However, the end users’ perspective and needs are often not sufficiently considered during the development of this technology, leading to frustration and nonuse of existing devices. Since its first competition in 2016, CYBATHLON has aimed to drive innovation in the field of assistive technology by motivating teams to involve end users more actively in the development process and to tailor novel devices to their actual daily-life needs. Competition tasks therefore represent unsolved daily-life challenges for people with disabilities and serve the purpose of benchmarking the latest developments from research laboratories and companies from around the world. This review describes each of the competition disciplines, their contributions to assistive technology, and remaining challenges in the user-centered development of this technology.