Department of Digital Systems, University of Piraeus, Piraeus, Greece.
Greek Research and Technology Network (GRNET), Athens, Greece.
Adv Exp Med Biol. 2020;1194:181-191. doi: 10.1007/978-3-030-32622-7_16.
The exponential growth of the number and variety of IoT devices and applications for personal use, as well as the improvement of their quality and performance, facilitates the realization of intelligent eHealth concepts. Nowadays, it is easier than ever for individuals to monitor themselves, quantify, and log their everyday activities in order to gain insights about their body's performance and receive recommendations and incentives to improve it. Of course, in order for such systems to live up to the promise, given the treasure trove of data that is collected, machine learning techniques need to be integrated in the processing and analysis of the data. This systematic and automated quantification, logging, and analysis of personal data, using IoT and AI technologies, have given birth to the phenomenon of Quantified-Self. This work proposes a prototype decentralized Quantified-Self application, built on top of a dedicated IoT gateway that aggregates and analyzes data from multiple sources, such as biosignal sensors and wearables, and performs analytics on it.
物联网设备和个人应用的数量和种类呈指数级增长，其质量和性能也得到了提高，这使得智能电子健康概念得以实现。如今，个人比以往任何时候都更容易监控自己，量化和记录他们的日常活动，从而深入了解身体的表现，并获得改善身体的建议和激励。当然，为了使这些系统能够兑现承诺，考虑到收集到的大量数据，需要将机器学习技术集成到数据的处理和分析中。这种使用物联网和人工智能技术对个人数据进行系统和自动化量化、记录和分析的方法，催生了量化自我的现象。这项工作提出了一个基于专用物联网网关的去中心化量化自我应用原型，该网关可以聚合和分析来自多个源的数据，例如生物信号传感器和可穿戴设备，并对其进行分析。
