Yantao Yu

Estimating Construction Workers' Physical Workload by Fusing Computer Vision and Smart Insole Technologies

Construction workers are commonly subjected to ergonomic risks due to awkward postures and/or excessive manual material handling. Accurate ergonomic assessment will facilitate ergonomic risk identification and the subsequent mitigation. Traditional assessment methods such as visual observation and on-body sensors rely on subjective judgement and are intrusive in nature. To cope up with the limitations of the existing technologies, a computer vision and smart insole-based joint-level ergonomic workload calculation methodology is proposed for construction workers. Accordingly, this method could provide an objective and detailed ergonomic assessment for various construction tasks. Firstly, construction workers’ skeleton data is extracted using a smartphone camera with an advanced deep learning algorithm. Secondly, smart insoles are used to quantify the plantar pressures while the worker performs a construction activity. Finally, the gathered data is fed to an inverse dynamic model in order to calculate the joint torques and workloads. The aforementioned approach was tested with experiments comprising simulations of material handling, plastering and rebar. The results reveal that the developed methodology has the potential to provide detailed and accurate ergonomic assessment. Overall, this research contributes to the knowledge of occupational safety and health in construction management by providing a novel approach to assess the risk factors of work-related musculoskeletal

Quantifying the physical intensity of construction workers, a mechanical energy approach

Construction workers typically undertake highly demanding physical tasks involving various types of stresses from awkward postures, using excessive force, highly repetitive actions, and excessive energy expenditure, which increases the likelihood of unsafe actions, productivity loss, and human errors. Biomechanical models have been developed to estimate joint loadings, which can help avoid strenuous physical exertion, potentially enhancing construction workforce productivity, safety, and well-being. However, the models used are mainly in 2D, or to predict static strength ignored their velocity and acceleration or using marker-based method for dynamic motion data collection. To address this issue, this paper proposes a novel framework for investigating the mechanical energy expenditure (MEE) of workers using a 3D biomechanical model based on computer vision-based techniques. Human 3D Pose Estimation algorithm based on 2D videos is applied to approximate the coordinates of human joints for working postures, and smart insoles are used to collect foot pressures and plantar accelerations, as input data for the biomechanical analyses. The results show a detailed MEE rate for the whole body, at which joints the maximum and minimum values were obtained to avoid excessive physical exertion. The proposed method can approximate the total daily MEE of construction tasks by summing the assumed cost of individual tasks (such as walking, lifting, and stooping), providing suggestions for the design of a daily workload that workers can sustain without developing cumulative fatigue.