University of Oulu

Mäkela S-M, Lämsä A, Keränen JS, Liikka J, Ronkainen J, Peltola J, Häikiö J, Järvinen S, Bordallo López M. Introducing VTT-ConIot: A Realistic Dataset for Activity Recognition of Construction Workers Using IMU Devices. Sustainability. 2022; 14(1):220.

Introducing VTT-ConIot : a realistic dataset for activity recognition of construction workers using IMU devices

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Author: Mäkelä, Satu-Marja1; Lämsä, Arttu1; Keränen, Janne S.1;
Organizations: 1VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
2Center for Machine Vision and Signal Analysis, University of Oulu, 90017 Oulu, Finland
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 4.1 MB)
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Language: English
Published: Multidisciplinary Digital Publishing Institute, 2022
Publish Date: 2022-05-23


Sustainable work aims at improving working conditions to allow workers to effectively extend their working life. In this context, occupational safety and well-being are major concerns, especially in labor-intensive fields, such as construction-related work. Internet of Things and wearable sensors provide for unobtrusive technology that could enhance safety using human activity recognition techniques, and has the potential of improving work conditions and health. However, the research community lacks commonly used standard datasets that provide for realistic and variating activities from multiple users. In this article, our contributions are threefold. First, we present VTT-ConIoT, a new publicly available dataset for the evaluation of HAR from inertial sensors in professional construction settings. The dataset, which contains data from 13 users and 16 different activities, is collected from three different wearable sensor locations. Second, we provide a benchmark baseline for human activity recognition that shows a classification accuracy of up to 89% for a six class setup and up to 78% for a sixteen class more granular one. Finally, we show an analysis of the representativity and usefulness of the dataset by comparing it with data collected in a pilot study made in a real construction environment with real workers.

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Series: Sustainability
ISSN: 2071-1050
ISSN-E: 2071-1050
ISSN-L: 2071-1050
Volume: 14
Issue: 1
Article number: 220
DOI: 10.3390/su14010220
Type of Publication: A1 Journal article – refereed
Field of Science: 113 Computer and information sciences
Funding: This research was funded by Business Finland, under grant number 5432/31/2018 ConIoT project.
Copyright information: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (