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辅助沟通技术(AAC)的进展:对言语残疾者配置的综述。

Augmentative and Alternative Communication (AAC) Advances: A Review of Configurations for Individuals with a Speech Disability.

机构信息

Wolfson School of Mechanical, Electrical, and Manufacturing Engineering, Loughborough University, Loughborough LE11 3TU, UK.

出版信息

Sensors (Basel). 2019 Apr 22;19(8):1911. doi: 10.3390/s19081911.

Abstract

High-tech augmentative and alternative communication (AAC) methods are on a constant rise; however, the interaction between the user and the assistive technology is still challenged for an optimal user experience centered around the desired activity. This review presents a range of signal sensing and acquisition methods utilized in conjunction with the existing high-tech AAC platforms for individuals with a speech disability, including imaging methods, touch-enabled systems, mechanical and electro-mechanical access, breath-activated methods, and brain-computer interfaces (BCI). The listed AAC sensing modalities are compared in terms of ease of access, affordability, complexity, portability, and typical conversational speeds. A revelation of the associated AAC signal processing, encoding, and retrieval highlights the roles of machine learning (ML) and deep learning (DL) in the development of intelligent AAC solutions. The demands and the affordability of most systems hinder the scale of usage of high-tech AAC. Further research is indeed needed for the development of intelligent AAC applications reducing the associated costs and enhancing the portability of the solutions for a real user's environment. The consolidation of natural language processing with current solutions also needs to be further explored for the amelioration of the conversational speeds. The recommendations for prospective advances in coming high-tech AAC are addressed in terms of developments to support mobile health communicative applications.

摘要

高科技辅助和替代性沟通(AAC)方法不断涌现;然而,为了实现以期望活动为中心的最佳用户体验,用户与辅助技术之间的交互仍然具有挑战性。本综述介绍了一系列用于言语障碍者的信号传感和采集方法,这些方法与现有的高科技 AAC 平台结合使用,包括成像方法、触摸启用系统、机械和机电访问、呼吸激活方法和脑机接口(BCI)。列出的 AAC 传感方式在易用性、可负担性、复杂性、便携性和典型会话速度方面进行了比较。对相关 AAC 信号处理、编码和检索的揭示突出了机器学习(ML)和深度学习(DL)在智能 AAC 解决方案开发中的作用。大多数系统的需求和可负担性阻碍了高科技 AAC 的使用规模。确实需要进一步研究开发智能 AAC 应用程序,以降低相关成本并提高解决方案在真实用户环境中的便携性。还需要进一步探索将自然语言处理与现有解决方案相结合,以提高会话速度。从支持移动健康交流应用程序的角度出发,提出了对未来高科技 AAC 发展的建议。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/109a/6515262/4d86a5f574f9/sensors-19-01911-g001.jpg

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