Abstract
Construction workers executing manual-intensive tasks are susceptible to musculoskeletal disorders (MSDs) due to overexposure to awkward postures. Automated posture recognition and assessment based on wearable sensor output can help reduce MSDs risks through early risk-factor detection. However, extant studies mainly focus on optimizing recognition models. There is a lack of studies exploring the design of a wearable sensing system that assesses the MSDs risks based on detected postures and then provides feedback for injury prevention. This study aims at investigating the design of an effective wearable MSDs prevention system. This study first proposes the design of a wearable inertial measurement unit (IMU) sensing system, then develops the prototype for end-user evaluation. Construction workers and managers evaluated a proposed system by interacting with wearable sensors and user interfaces (UIs), followed by an evaluation survey. The results suggest that wearable sensing is a promising approach for collecting motion data with low discomfort; posture-based MSDs risk assessment has a high potential in improving workers’ safety awareness; and mobile- and cloud-based UIs can deliver the risk assessment information to end-users with ease. This research contributes to the design, development, and validation of wearable sensing-based injury prevention systems, which may be adapted to other labor-intensive occupations.
Highlights
The research presented in this paper is part of a larger project directed at developing a data-driven approach for mitigating the risk of musculoskeletal disorders (MSDs) among construction workers, such as chronic backache and over-exertion
This study proposes a wearable sensing system that integrates inertial measurement unit (IMU) sensors for motion sensing, a deep neural network (DNN) model for posture recognition, posture-based ergonomics assessment models for MSD risk assessment, and user interfaces (UIs) for risk assessment feedback
The results show that (i) the proposed wearable sensors (WSs) system is a promising approach for collecting data from construction workers because it is not perceived to cause discomfort; (ii) the resulting posture-based MSD risk assessment information has a high potential for improving the workers’ safety awareness; and (iii) the developed mobile and cloud-based UI can readily deliver actionable MSD risk assessment information to the targeted end users
Summary
The research presented in this paper is part of a larger project directed at developing a data-driven approach for mitigating the risk of musculoskeletal disorders (MSDs) among construction workers, such as chronic backache and over-exertion. Labor-intensive construction tasks tend to expose workers to using awkward working postures, such as working overhead, kneeling, and back bending. Overexposure to awkward postures can lead to MSDs. Construction-related MSDs account for 30% of workplace injuries in the. Proactive MSDs prevention is predicated on effective monitoring of workers’ physical status. Conventional observation-based risk-monitoring strategies are impractical on construction sites. Safety inspectors can be overwhelmed by jobsite complexity, such as rapidly changing working conditions and concurrent appearance of various construction tasks [3]. There has been growing interest in the use of automated motion-sensing approaches for construction risk assessment
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