Affiliation:
1. University of Helsinki, Finland
2. University of Extremadura, Spain
3. University of Tartu, Tartu, Estonia
4. Häme University of Applied Sciences, Finland
5. University of Applied Sciences and Arts of Southern Switzerland, Sveitsi, Switzerland
6. University of Helsinki, Finland and the Hong Kong University of Science and Technology, Hong Kong
Abstract
Today, an increasing number of systems produce, process, and store personal and intimate data. Such data has plenty of potential for entirely new types of software applications, as well as for improving old applications, particularly in the domain of smart healthcare. However, utilizing this data, especially when it is continuously generated by sensors and other devices, with the current approaches is complex—data is often using proprietary formats and storage, and mixing and matching data of different origin is not easy. Furthermore, many of the systems are such that they should stimulate interactions with humans, which further complicates the systems. In this article, we introduce the Human Data Model—a new tool and a programming model for programmers and end users with scripting skills that help combine data from various sources, perform computations, and develop and schedule computer-human interactions. Written in JavaScript, the software implementing the model can be run on almost any computer either inside the browser or using Node.js. Its source code can be freely downloaded from GitHub, and the implementation can be used with the existing IoT platforms. As a whole, the work is inspired by several interviews with professionals, and an online survey among healthcare and education professionals, where the results show that the interviewed subjects almost entirely lack ideas on how to benefit the ever-increasing amount of data measured of the humans. We believe that this is because of the missing support for programming models for accessing and handling the data, which can be satisfied with the Human Data Model.
Publisher
Association for Computing Machinery (ACM)
Reference52 articles.
1. Smart health: A context-aware health paradigm within smart cities
2. Beyond Weiser: From Ubiquitous to Collective Computing
3. Data driven education in personal learning environments—What about learning beyond the institution;Conde Miguel;International Journal of Learning Analytics and Artificial Intelligence for Education,2019
4. Personalized education to increase interest;Reber Rolf;Current Directions in Psychological Science,2018
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1. HIPPO;Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies;2022-12-21