Xiang Liangliang, Gu Yaodong, Deng Kaili, Gao Zixiang, Shim Vickie, Wang Alan, Fernandez Justin
Faculty of Sports Science, Ningbo University, Ningbo, China.
Auckland Bioengineering Institute, University of Auckland, Auckland, New Zealand.
NPJ Digit Med. 2025 May 13;8(1):276. doi: 10.1038/s41746-025-01677-0.
Running biomechanics studies the mechanical forces experienced during running to improve performance and prevent injuries. This study presents the development of a digital twin for predicting bone stress in runners. The digital twin leverages a domain adaptation-based Long Short-Term Memory (LSTM) algorithm, informed by wearable sensor data, to dynamically simulate the structural behavior of foot bones under running conditions. Data from fifty participants, categorized as rearfoot and non-rearfoot strikers, were used to create personalized 3D foot models and finite element simulations. Two nine-axis inertial sensors captured three-axis acceleration data during running. The LSTM neural network with domain adaptation proved optimal for predicting bone stress in key foot bones-specifically the metatarsals, calcaneus, and talus-during the mid-stance and push-off phases (RMSE < 8.35 MPa). This non-invasive, cost-effective approach represents a significant advancement for precision health, contributing to the understanding and prevention of running-related fracture injuries.
跑步生物力学研究跑步过程中所经历的机械力，以提高运动表现并预防损伤。本研究展示了一种用于预测跑步者骨骼应力的数字孪生模型的开发。该数字孪生模型利用基于域适应的长短期记忆（LSTM）算法，并结合可穿戴传感器数据，来动态模拟跑步条件下足部骨骼的结构行为。来自50名参与者的数据，分为后足着地者和非后足着地者，用于创建个性化的3D足部模型和有限元模拟。两个九轴惯性传感器在跑步过程中捕获三轴加速度数据。经证明，具有域适应能力的LSTM神经网络在预测关键足部骨骼（特别是跖骨、跟骨和距骨）在支撑中期和蹬离期的骨骼应力方面表现最佳（均方根误差<8.35兆帕）。这种非侵入性、经济高效的方法代表了精准健康领域的一项重大进展，有助于理解和预防与跑步相关的骨折损伤。
