Occupational safety and health is traditionally a challenging area in the labor-intensive construction industry as accidents at work and non-ergonomic work conditions lead to absences and premature retirement of construction workers. Recently, the rise of the Internet of Things (IoT) and its accompanying technologies (e.g. wearable technologies) has enhanced interest in the occupational safety and health of construction work. The level of technology acceptance among construction workers is a crucial element in the adoption of these technologies. The main objectives of this study are to enhance understanding about construction workers’ attitudes towards IoT-based data-intensive work safety and wellbeing solutions and to identify factors that can promote technology adoption. Data for the study was collected through an online survey of 4385 construction workers. Based on the survey data it seems that construction workers would accept the sharing and utilizing data collected from them in the worksite environment if it could help identify employee personal health risks or promote personal and colleagues' occupational safety. Respondents were most concerned about privacy and security regarding wearables in the workplace. It can be concluded that user acceptance and trust building are key components in the adoption of IoT-based occupational safety and health solutions. Future studies should investigate methods for actively involving construction workers in the design and development process of IoT-based work safety solutions and examine technological solutions that promote trust building among construction workers.

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.

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.