Work

Machine-Learning-Based Fatigue Trend Analysis on IMU Wearable Sensor Data from Construction Site Workers

Year: 2025

Type: article

Abstract: Physical fatigue is a major cause of work-related accidents and musculoskeletal injuries in the construction industry, and additional means are needed for their identification and management to prevent long-term consequences. Based on recent scientific literature, fatigue can be detected with wearable inertial measurement units (IMUs). However, IMUs for detecting fatigue have been so far tested mainly in the laboratory; therefore, a research gap exists in application of IMU sensors for detecting fatigue in real-life work settings. The aim of this paper is to bring the fatigue trend detection with IMUs closer to real-life context by using wearable IMU sensor data from an actual construction site measuring actual workers with simulated work tasks. The paper also presents advancements in fatigue trend detection with frequency domain investigations to gain access to more detailed fatigue relevant features. Machine-learning methods are used to predict fatigue trends based on IMU data, resulting in fatigue trend detection accuracy that advances the state of the art. More knowledge is also unearthed about relevant sensor locations and features.

Source: Sensors

DOI: 10.3390/s25247455

Authors: Janne S. Keränen, Jamil Ahmad, Sergio Leggieri, Satu-Marja Mäkelä, Darwin G. Caldwell, Christian Di Natali, Atte Kinnula, Pekka Siirtola

Institutions: VTT Technical Research Centre of Finland, Italian Institute of Technology, University of Genoa, University of Oulu

Language: English

Topic: Context-Aware Activity Recognition Systems

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDGs: Decent work and economic growth, Good health and well-being, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137