Malamas Nikolaos, Panayiotou Konstantinos, Karabatea Apostolia, Tsardoulias Emmanouil, Symeonidis Andreas L
Faculty of Engineering, Aristotle University of Thessaloniki, 541 24 Thessaloniki, Greece.
Gnomon Informatics S.A., 570 01 Thessaloniki, Greece.
Sensors (Basel). 2024 Aug 11;24(16):5199. doi: 10.3390/s24165199.
Today, Smart Assistants (SAs) are supported by significantly improved Natural Language Processing (NLP) and Natural Language Understanding (NLU) engines as well as AI-enabled decision support, enabling efficient information communication, easy appliance/device control, and seamless access to entertainment services, among others. In fact, an increasing number of modern households are being equipped with SAs, which promise to enhance user experience in the context of smart environments through verbal interaction. Currently, the market in SAs is dominated by products manufactured by technology giants that provide well designed off-the-shelf solutions. However, their simple setup and ease of use come with trade-offs, as these SAs abide by proprietary and/or closed-source architectures and offer limited functionality. Their enforced vendor lock-in does not provide (power) users with the ability to build custom conversational applications through their SAs. On the other hand, employing an open-source approach for building and deploying an SA (which comes with a significant overhead) necessitates expertise in multiple domains and fluency in the multimodal technologies used to build the envisioned applications. In this context, this paper proposes a methodology for developing and deploying conversational applications on the edge on top of an open-source software and hardware infrastructure via a multilayer architecture that simplifies low-level complexity and reduces learning overhead. The proposed approach facilitates the rapid development of applications by third-party developers, thereby enabling the establishment of a marketplace of customized applications aimed at the smart assisted living domain, among others. The supporting framework supports application developers, device owners, and ecosystem administrators in building, testing, uploading, and deploying applications, remotely controlling devices, and monitoring device performance. A demonstration of this methodology is presented and discussed focusing on health and assisted living applications for the elderly.
如今，智能助手（SAs）得到了显著改进的自然语言处理（NLP）和自然语言理解（NLU）引擎以及人工智能驱动的决策支持的支持，实现了高效的信息通信、便捷的电器/设备控制以及无缝访问娱乐服务等功能。事实上，越来越多的现代家庭配备了智能助手，它们有望通过语音交互在智能环境中提升用户体验。目前，智能助手市场由科技巨头生产的产品主导，这些产品提供设计良好的现成解决方案。然而，它们简单的设置和易用性也存在权衡，因为这些智能助手遵循专有和/或封闭源架构，功能有限。它们强制的供应商锁定使（普通）用户无法通过智能助手构建自定义对话应用程序。另一方面，采用开源方法构建和部署智能助手（这需要大量开销）需要在多个领域具备专业知识，并且要精通用于构建预期应用程序的多模态技术。在此背景下，本文提出了一种方法，通过多层架构在开源软件和硬件基础设施之上在边缘开发和部署对话应用程序，该架构简化了底层复杂性并减少了学习开销。所提出的方法便于第三方开发者快速开发应用程序，从而能够建立一个针对智能辅助生活领域等的定制应用程序市场。支持框架为应用程序开发者、设备所有者和生态系统管理员提供支持，帮助他们构建、测试、上传和部署应用程序，远程控制设备以及监控设备性能。本文给出并讨论了该方法的一个演示，重点关注针对老年人的健康和辅助生活应用。
