ABSTRACT Gait analysis offers significant potential for personalised health assessment and enhances the understanding of musculoskeletal dynamics in biomechanical studies. To meet the growing need for intelligent gait analysis, a digital twin (DT)‐driven gait analysis framework integrating wearable inertial sensors, multi‐task deep learning, and OpenSim‐based musculoskeletal biomechanical simulation was investigated and achieved closed‐loop analysis by collecting, processing, and mapping gait data. The DT system architecture followed a layered design methodology, combining interaction between physical, data, DT and service layers to create a comprehensive framework. The inertial measurement unit (IMU) sensors captured the changes in limb angles, acceleration and angular velocity. Data were stored in data layer. A multi‐task deep learning model with three convolutional layers was designed to combine with the Zero Velocity Update (ZUPT) algorithm with a short‐distance step length calculation strategy to suppress integral drift, enabling accurate annotation of key gait events, such as heel strike, toe‐off and related parameters. Gait parameters were extracted through the closed‐loop adaptive model. A step length estimation accuracy of mean error ( μ ) −0.023 m with standard deviation ( σ ) 0.025 m and excellent temporal parameter regression performance were achieved. To reveal the biomechanical mechanism of abnormal gaits, the lower limb joints were simulated through model scaling, inverse kinematic analysis and inverse dynamic calculation based on OpenSim platform. The virtual mapping model in data‐driven digital layer synchronised with the real motion and iteratively optimised. Gait classification was achieved by DT based convolutional neural network (CNN) with an overall accuracy of nearly 98%. Experimental validation confirmed that the system can conduct analysis and visualisation of the posture and mechanical data of the lower limbs. The DT‐driven gait analysis system showed significant potential for providing performance optimisation guidance feedback to assist precision gait analysis.
Ye et al. (Thu,) studied this question.