research-article

Human Data Model: Improving Programmability of Health and Well-Being Data for Enhanced Perception and Interaction

Authors:

Niko Mäkitalo,

Daniel Flores-Martin,

Eemil Lagerspetz,

Francois Christophe,

Petri Ihantola,

Masiar Babazadeh,

Juan Manuel Murillo,

Tommi MikkonenAuthors Info & Claims

ACM Transactions on Computing for Healthcare , Volume 1, Issue 4

Article No.: 26, Pages 1 - 39

https://doi.org/10.1145/3402524

Published: 30 September 2020 Publication History

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.

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Cited By

Kotilainen PMäkitalo NSystä KMehraj AWaseem MMikkonen TMurillo J(2025)Allocating distributed AI/ML applications to cloud–edge continuum based on privacy, regulatory, and ethical constraintsJournal of Systems and Software10.1016/j.jss.2025.112333222(112333)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2025.112333
Yin ZLiyanage MOttun APaul SZuniga ANurmi PFlores H(2023)HIPPOProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35703446:4(1-30)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3570344

Index Terms

Human Data Model: Improving Programmability of Health and Well-Being Data for Enhanced Perception and Interaction

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Published In

cover image ACM Transactions on Computing for Healthcare

ACM Transactions on Computing for Healthcare Volume 1, Issue 4

Special Issue on Wearable Technologies for Smart Health: Part 1

October 2020

184 pages

EISSN:2637-8051

DOI:10.1145/3427421

Editors:
Insup Lee
University of Pennsylvania, USA
,
John A. Stankovic
University of Virginia, USA

Issue’s Table of Contents

Copyright © 2020 ACM.

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Publication History

Published: 30 September 2020

Accepted: 01 May 2020

Revised: 01 May 2020

Received: 01 August 2019

Published in HEALTH Volume 1, Issue 4

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European Regional Development Fund
Department of Economy and Infrastructure of the Government of Extremadura
Interreg V-A España-Portugal (POCTEP) 2014-2020 program
Academy of Finland
Spanish Ministry of Science and Innovation
4IE+ project

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Cited By

Kotilainen PMäkitalo NSystä KMehraj AWaseem MMikkonen TMurillo J(2025)Allocating distributed AI/ML applications to cloud–edge continuum based on privacy, regulatory, and ethical constraintsJournal of Systems and Software10.1016/j.jss.2025.112333222(112333)Online publication date: Apr-2025
https://doi.org/10.1016/j.jss.2025.112333
Yin ZLiyanage MOttun APaul SZuniga ANurmi PFlores H(2023)HIPPOProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/35703446:4(1-30)Online publication date: 11-Jan-2023
https://dl.acm.org/doi/10.1145/3570344

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