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 work load 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 experiment s 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 disorders (WMSDs).