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Accessible Making for Assistive Technology

Accessible Making for Assistive Technology
辅助技术的无障碍制作
批准号:
RGPIN-2018-05817
负责人:
Baljko, Melanie
金额:
$1.68万
依托单位:
依托单位国家:
加拿大
项目类别:
Discovery Grants Program - Individual
财政年份:
2018
资助国家:
加拿大
项目状态:
已结题
起止时间:
2018-01-01 至 2019-12-31

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中文摘要
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英文摘要
The way mainstream technologies are designed often excludes disabled people from using them. This, in turn, necessitates the need for Assistive Technology (AT). AT, more generally, refers to items and techniques that enable a person with a disability to complete a task that they would otherwise be unable to do. ******Personal-scale fabrication, which is an alternative to mass production, uses 3D printers and other equipment, alongside digital electronics design and various other materials for the creation of different types of customizable objects. In the last decade, both personal-scale fabrication and Do-It-Yourself (DIY) approaches have been conceived of as an alternative — and a potentially superior — way to design ATs and to deploy ATs to users who would benefit from them. These ATs span a range of complexity, from static objects without moving parts (such as cup holders and door openers), all the way to digital interactive devices, such as Speech Generating Devices (SGDs), which can be used by individuals with little or no functional speech.******These Do-It-Yourself Assistive Technologies (DIY-ATs) are indeed promising; they offer the possibility to circumvent some of the biggest problems of ATs: abandonment (even though they are often expensive and/or take a long time to get); failure to meet user needs (despite careful assessment by clinicians); failure to meet needs of their users, and marginalization of their users (in their processes of design/acquisition). ******There are many challenges to be solved before DIY-AT will be used more widely. This research focuses on an important, if not the most important, challenge: DIY-AT, ostensibly intended to benefit individuals with disabilities, still marginalize those individuals, since the making itself is often not accessible. ******This project focuses on accessible making for a challenging class of DIY-ATs: those which are interactive digital devices. These devices use low-cost single-board computers (such as Arduino and Raspberry Pi), they make use of software modules and 3D-printed components, and they require the assembly of electronics******This project will answer questions about the pros and cons of new interfaces, techniques, and systems for designing and making DIY-ATs. To do this, the project will engage with community groups and, together, build learning modules for a set of new DIY-ATs. We are confident this can be done because we have done it twice already. The project will study and learn how people with disabilities are able to use them. A side-benefit is that the DIY-AT designs and learning modules will be generated by the project and they will be made freely available to anyone who wants to use them. The knowledge that is gained will be used in many ways: to improve the design of existing computer interfaces and systems, to improve STEM learning, and to improve the way people with disabilities get to determine the kinds of ATs that they use and customize.
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Accessible Making for Assistive Technology
  • 批准号:
    RGPIN-2018-05817
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.68万
  • 财政年份:
    2022
  • 负责人:
    Baljko, Melanie
  • 依托单位:
Accessible Making for Assistive Technology
  • 批准号:
    RGPIN-2018-05817
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.68万
  • 财政年份:
    2021
  • 负责人:
    Baljko, Melanie
  • 依托单位:
Accessible Making for Assistive Technology
  • 批准号:
    RGPIN-2018-05817
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.68万
  • 财政年份:
    2020
  • 负责人:
    Baljko, Melanie
  • 依托单位:
Accessible Making for Assistive Technology
  • 批准号:
    RGPIN-2018-05817
  • 项目类别:
    Discovery Grants Program - Individual
  • 资助金额:
    $1.68万
  • 财政年份:
    2019
  • 负责人:
    Baljko, Melanie
  • 依托单位:
国内基金
海外基金
Scalable Learning and Optimization: High-dimensional Models and Online Decision-Making Strategies for Big Data Analysis