Introducing VTT-ConIot: A Realistic Dataset for Activity Recognition of Construction Workers Using IMU Devices

Satu-Marja Mäkela, Arttu Lämsä, Janne S. Keränen, Jussi Liikka, Jussi Ronkainen, Johannes Peltola, Juha Häikiö, Sari Järvinen and Miguel Bordallo López
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Satu-Marja Mäkela: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Arttu Lämsä: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Janne S. Keränen: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Jussi Liikka: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Jussi Ronkainen: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Johannes Peltola: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Juha Häikiö: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Sari Järvinen: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland
Miguel Bordallo López: VTT Technical Research Centre of Finland Ltd., 90150 Oulu, Finland

Sustainability, 2021, vol. 14, issue 1, 1-20

Abstract: 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.

Keywords: IoT; human activity recognition; construction; IMU (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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