Assistive technologies for severe and profound hearing loss: beyond hearing aids and implants

Assistive technologies offer capabilities that were previously inaccessible to individuals with severe and profound hearing loss who have no or limited access to hearing aids and implants. This literature review aims to explore existing assistive technologies and identify what still needs to be done. It is found that there is a lack of focus on the overall objectives of assistive technologies. In addition, several other issues are identified i.e. only a very small number of assistive technologies developed within a research context have led to commercial devices, there is a predisposition to use the latest expensive technologies and a tendency to avoid designing products universally. Finally, the further development of plug-ins that translate the text content of a website to various sign languages is needed to make information on the internet more accessible

Designing a Sensor-Based Wearable Computing System for Custom Hand Gesture Recognition Using Machine Learning

This thesis investigates how assistive technology can be made to facilitate communication for people that are unable to or have difficulty communicating via vocal speech, and how this technology can be made more universal and compatible with the many different types of sign language that they use. Through this research, a fully customisable and stand-alone wearable device was developed, that employs machine learning techniques to recognise individual hand gestures and translate them into text, images and speech. The device can recognise and translate custom hand gestures by training a personal classifier for each user, relying on a small training sample size, that works online on an embedded system or mobile device, with a classification accuracy rate of up to 99%. This was achieved through a series of iterative case studies, with user testing carried out by real users in their every day environments and in public spaces

A Cross-Lingual Mobile Medical Communication System Prototype for Foreigners and Subjects with Speech, Hearing, and Mental Disabilities Based on Pictograms

People with speech, hearing, or mental impairment require special communication assistance, especially for medical purposes. Automatic solutions for speech recognition and voice synthesis from text are poor fits for communication in the medical domain because they are dependent on error-prone statistical models. Systems dependent on manual text input are insufficient. Recently introduced systems for automatic sign language recognition are dependent on statistical models as well as on image and gesture quality. Such systems remain in early development and are based mostly on minimal hand gestures unsuitable for medical purposes. Furthermore, solutions that rely on the Internet cannot be used after disasters that require humanitarian aid. We propose a high-speed, intuitive, Internet-free, voice-free, and text-free tool suited for emergency medical communication. Our solution is a pictogram-based application that provides easy communication for individuals who have speech or hearing impairment or mental health issues that impair communication, as well as foreigners who do not speak the local language. It provides support and clarification in communication by using intuitive icons and interactive symbols that are easy to use on a mobile device. Such pictogram-based communication can be quite effective and ultimately make people’s lives happier, easier, and safer

Gesture Based Home Automation for the Physically Disabled

Paralysis and motor-impairments can greatly reduce the autonomy and quality of life of a patient while presenting a major recurring cost in home-healthcare. Augmented with a non-invasive wearable sensor system and home-automation equipment, the patient can regain a level of autonomy at a fraction of the cost of home nurses. A system which utilizes sensor fusion, low-power digital components, and smartphone cellular capabilities can extend the usefulness of such a system to allow greater adaptivity for patients with various needs. This thesis develops such a system as a Bluetooth enabled glove device which communicates with a remote web server to control smart-devices within the home. The power consumption of the system is considered as a major component to allow the system to operate while requiring little maintenance, allowing for greater patient autonomy. The system is evaluated in terms of power consumption and accuracy to prove its viability as a home accessibility tool

RoboTalk - Prototyping a Humanoid Robot as Speech-to-Sign Language Translator

Information science mostly focused on sign language recognition. The current study instead examines whether humanoid robots might be fruitful avatars for sign language translation. After a review of research into sign language technologies, a survey of 50 deaf participants regarding their preferences for potential reveals that humanoid robots represent a promising option. The authors also 3D-printed two arms of a humanoid robot, InMoov, with special joints for the index finger and thumb that would provide it with additional degrees of freedom to express sign language. They programmed the robotic arms with German sign language and integrated it with a voice recognition system. Thus this study provides insights into human–robot interactions in the context of sign language translation; it also contributes ideas for enhanced inclusion of deaf people into society

Automatic recognition of Arabic alphabets sign language using deep learning

Technological advancements are helping people with special needs overcome many communications’ obstacles. Deep learning and computer vision models are innovative leaps nowadays in facilitating unprecedented tasks in human interactions. The Arabic language is always a rich research area. In this paper, different deep learning models were applied to test the accuracy and efficiency obtained in automatic Arabic sign language recognition. In this paper, we provide a novel framework for the automatic detection of Arabic sign language, based on transfer learning applied on popular deep learning models for image processing. Specifically, by training AlexNet, VGGNet and GoogleNet/Inception models, along with testing the efficiency of shallow learning approaches based on support vector machine (SVM) and nearest neighbors algorithms as baselines. As a result, we propose a novel approach for the automatic recognition of Arabic alphabets in sign language based on VGGNet architecture which outperformed the other trained models. The proposed model is set to present promising results in recognizing Arabic sign language with an accuracy score of 97%. The suggested models are tested against a recent fully-labeled dataset of Arabic sign language images. The dataset contains 54,049 images, which is considered the first large and comprehensive real dataset of Arabic sign language to the furthest we know

Overcoming barriers and increasing independence: service robots for elderly and disabled people

This paper discusses the potential for service robots to overcome barriers and increase independence of elderly and disabled people. It includes a brief overview of the existing uses of service robots by disabled and elderly people and advances in technology which will make new uses possible and provides suggestions for some of these new applications. The paper also considers the design and other conditions to be met for user acceptance. It also discusses the complementarity of assistive service robots and personal assistance and considers the types of applications and users for which service robots are and are not suitable

Preliminary design issues for inertial rings in Ambient Assisted Living applications

A wearable 9dof inertial system able to measure hand posture and movement is presented. The design issues for the deployment of measurement instrumentation based on no-invasive ring-shaped inertial units and of a wireless sensor network by them composed are described. Compromises between the physical and functional proprieties of a wearable device and the requirements for the hardware development are discussed with attention to an handsome design concept aesthetically effective. Techniques of power saving based on an optimized firmware programming are mentioned to realize a performing battery powered system featured by an exhaustive operation time. The printed circuit board (PCB) design rules, the choice of the components and materials, the fusion of inertial data with optical sensors outcomes are also discussed. Previous experience in the field of wearable systems are mentioned in the presentation of the results that emphasize the functional and application potential of a 9dof inertial system integrated in a ring-shaped device. � 2015 IEEE

Future bathroom: A study of user-centred design principles affecting usability, safety and satisfaction in bathrooms for people living with disabilities

Research and development work relating to assistive technology 2010-11 (Department of Health) Presented to Parliament pursuant to Section 22 of the Chronically Sick and Disabled Persons Act 197