I-Corps: End-User Trained Augmentative/Alternative Communication Tablet for Speech-Impaired Patients

这个I-Corps项目更广泛的影响/商业潜力源于利用人工智能（AI）强大的图像和模式识别能力来创建一种不需要用户适应新技术固有限制的通信辅助设备。相反，该设备适应用户，无论他们遇到什么限制，几乎立即吸收。因此，与引入不熟悉的技术相关联的重大障碍被移除，并且用户-无论口头交流的潜在身体损伤如何-都可以与他人、他们的环境（通过链接的物联网智能设备）以及设备或仪器接口进行交互。特别是对于那些有永久限制语言交流的条件的人来说，与他人互动的实用，适应性和可行的方法可以减少对照顾者的依赖，并使他们能够更充分地融入他们的环境。对引入新通信设备的实际障碍的更详细的理解也将转化为相关领域，包括表征医疗、危险处置和恶劣环境的情况。这个I-Corps项目建立在成功展示机器学习（“人工智能”）图像处理技术的基础上，为非语言通信创造了一种可行的替代方法。输入设备是传统的平板电脑或智能手机，并且典型的用户将与单个手指交互，尽管替代方案是可以想到的。该项目源于一项专注于开发下一代环保型地球元素丰富的半导体的研究，其中需要以自动化的方式处理大量视频数据。开发的相应工具的基础框架适用于这一新的但并非无关的目的，以便通信设备适应用户和用户的限制，而不是相反。我们已经确定，通过利用适用于基于AI的应用程序的当前一代硬件，基本上在真实的时间内处理用户定义的抽象输入是可行的。用户训练神经网络，随着使用而变得越来越准确。该项目将为目标市场确定有意义的成功指标，以便相应的最低可行产品可以与现有的和竞争的技术进行比较，以及确定技术吸收的主要障碍，可以发挥作用。该奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

The broader impact/commercial potential of this I-Corps project stems from exploiting the powerful image and pattern recognition abilities of artificial intelligence (AI) to create a communication assistance device that does not require the user to adapt to the inherent limitations of a new technology. Instead, the device adapts to the user, within whatever limitations they experience, for near-immediate uptake. Consequently, the significant barriers associated with introducing unfamiliar technology are removed, and the user - regardless of the underlying physical impairment to verbal communication - can interact with others, their environment (through linked internet-of-things enabled smart devices) and equipment or instrumentation interfaces. Particulary for those with a condition permanently limiting verbal communication, a practical, adaptive and viable approach to interacting with others can reduce the reliance on caregivers, and enable them to more fully integrate in their environment. A more detailed understanding of the practical obstacles to introducing new communication devices will translate to related fields as well, including situations characterizing medical, hazardous disposal, and harsh environments.This I-Corps project builds upon the successful demonstration of machine-learning ("artificial intelligence, or AI") image processing techniques to create a viable alternative method for nonverbal communication. The input device is a conventional tablet or smartphone, and the typical user will interact with a single finger, although alternatives are conceivable. The project originates from a study focused on development of next-generation, environmentally-friendly, earth-abundant element semiconductors, in which large amounts of video data needed to be processed in an automated fashion. The underlying framework for the corresponding tool that was developed was adapted to this new, but not unrelated, purpose, so that the communication device adapts to the user and the user's limitations, and not the other way around. We have established that is is feasible to process user-defined abstract input essentially in real time by capitalizing on current generation hardware adapted for AI-based applications. The user trains a neural network, which becomes increasingly more accurate with use. The project will identify meaningful success metrics for target markets so that the corresponding minimum viable product can be compared to existing and competing technologies, as well as identify the primary barriers to technology uptake that can come into play.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.