SpeechMatch—A novel digital approach to supporting communication for neurodiverse groups

IF 2.8 Q3 ENGINEERING, BIOMEDICAL
Sarah Lennard, Samuel J. Tromans, Robert Taub, Sarah Mitchell, Rohit Shankar
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引用次数: 0

Abstract

Communication can be a challenge for a significant minority of the population. Those with intellectual disability, autism, or Stroke survivors can encounter significant problems and stigma in their communication abilities leading to worse health and social outcomes. SpeechMatch (https://www.speechmatch.com/) is a digital App which is a pragmatic mobile language training platform that teaches individuals to “match” critical components of conversation and looks to provides subjects with immediate visual feedback to shape identification and expression of emotion in speech. While it has been used in autistic people there has been no systematic exploration of its strengths and weaknesses. Further, it's potential to afford improvements in communication to other vulnerable groups such as intellectual disability or Stroke survivors has not been explored. This study looked to understand acceptability from people with intellectual disability and/or autism and those recovering from a stroke on the utility and scope of SpeechMatch using co-production techniques using experts by experience and a mixed methods evaluation. Results across four domains suggest high acceptability levels but highlighting needs for platform capabilities improvement and better user engagement. The study outlines a vital and essential aspect for improving SpeechMatch. It gives a template for evidenced based quality improvement of similar devices.

Abstract Image

一种新颖的数字方法来支持神经多样性群体的交流。
对于相当一部分人来说,沟通可能是一个挑战。那些智力残疾、自闭症或中风幸存者可能会在沟通能力方面遇到重大问题和耻辱,从而导致更糟糕的健康和社会结果。SpeechMatch (https://www.speechmatch.com/)是一个数字应用程序,它是一个实用的移动语言培训平台,教个人“匹配”对话的关键组成部分,并希望为主题提供即时的视觉反馈,以塑造语音中的情感识别和表达。虽然它已经用于自闭症患者,但还没有对其优缺点进行系统的探索。此外,它是否有潜力改善与其他弱势群体的沟通,如智力残疾或中风幸存者,还没有得到探索。本研究旨在了解智力残疾和/或自闭症患者以及中风恢复期患者对语音匹配的实用性和范围的接受程度,使用经验专家和混合方法评估的联合制作技术。四个领域的结果表明可接受程度较高,但突出了平台功能改进和更好的用户参与的需求。该研究概述了改善SpeechMatch的一个至关重要的方面。为同类器械的循证质量改进提供了模板。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Healthcare Technology Letters
Healthcare Technology Letters Health Professions-Health Information Management
CiteScore
6.10
自引率
4.80%
发文量
12
审稿时长
22 weeks
期刊介绍: Healthcare Technology Letters aims to bring together an audience of biomedical and electrical engineers, physical and computer scientists, and mathematicians to enable the exchange of the latest ideas and advances through rapid online publication of original healthcare technology research. Major themes of the journal include (but are not limited to): Major technological/methodological areas: Biomedical signal processing Biomedical imaging and image processing Bioinstrumentation (sensors, wearable technologies, etc) Biomedical informatics Major application areas: Cardiovascular and respiratory systems engineering Neural engineering, neuromuscular systems Rehabilitation engineering Bio-robotics, surgical planning and biomechanics Therapeutic and diagnostic systems, devices and technologies Clinical engineering Healthcare information systems, telemedicine, mHealth.
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