Digital twin-driven framework for improving self-management of ergonomic risks

Purpose The physically-demanding and repetitive nature of construction work often exposes workers to work-related musculoskeletal injuries. Real-time information about the ergonomic consequences of workers' postures can enhance their ability to control or self-manage their exposures. This study proposes a digital twin framework to improve self-management ergonomic exposures through bi-directional mapping between workers' postures and their corresponding virtual replica. Design/methodology/approach The viability of the proposed approach was demonstrated by implementing the digital twin framework on a simulated floor-framing task. The proposed framework uses wearable sensors to track the kinematics of workers' body segments and communicates the ergonomic risks via an augmented virtual replica within the worker's field of view. Sequence-to-sequence long short-term memory (LSTM) network is employed to adapt the virtual feedback to workers' performance. Findings Results show promise for reducing ergonomic risks of the construction workforce through improved awareness. The experimental study demonstrates feasibility of the proposed approach for reducing overexertion of the trunk. Performance of the LSTM network improved when trained with augmented data but at a high computational cost. Research limitations/implications Suggested actionable feedback is currently based on actual work postures. The study is experimental and will need to be scaled up prior to field deployment. Originality/value This study reveals the potentials of digital twins for personalized posture training and sets precedence for further investigations into opportunities offered by digital twins for improving health and wellbeing of the construction workforce.