To develop and validate a neural network to estimate hip contact forces (HCF), and lower body kinematics and kinetics during walking in individuals with hip osteoarthritis (OA) using synthesised anatomical key points and electromyography. To assess the capability of the neural network to detect directional changes in HCF resulting from prescribed gait modifications. A calibrated electromyography-informed neuromusculoskeletal model was used to compute lower body joint angles, moments, and HCF for 17 participants with mild-to-moderate hip OA. Anatomical key points (e.g., joint centres) were synthesised from marker trajectories and augmented with bias and noise expected from computer vision-based pose estimation systems. Temporal convolutional and long short-term memory neural networks (NN) were trained using leave-one-subject-out validation to predict neuromusculoskeletal modelling outputs from the synthesised key points and measured electromyography data from 5 hip-spanning muscles. HCF was predicted with an average error of 13.4 ± 7.1% of peak force. Joint angles and moments were predicted with an average root-mean-square-error of 5.3 degrees and 0.10 Nm/kg, respectively. The NN could detect changes in peak HCF that occur due to gait modifications with good agreement with neuromusculoskeletal modelling (r = 0.72) and a minimum detectable change of 9.5%. The developed neural network predicted HCF and lower body joint angles and moments in individuals with hip OA using noisy synthesised key point locations with acceptable errors. Changes in HCF magnitude due to gait modifications were predicted with high accuracy. These findings have important implications for implementation of load-modification based gait retraining interventions for people with hip OA in a natural environment (i.e., home, clinic).

中文翻译：

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可以通过神经网络仅使用合成关键点和肌电图来预测髋部骨关节炎患者的髋部接触力


开发和验证神经网络，使用合成的解剖关键点和肌电图来估计髋部骨关节炎 (OA) 患者行走期间的髋部接触力 (HCF) 以及下半身运动学和动力学。评估神经网络检测因规定步态修改而导致的 HCF 方向变化的能力。使用经过校准的肌电图信息神经肌肉骨骼模型来计算 17 名患有轻度至中度髋关节 OA 的参与者的下半身关节角度、力矩和 HCF。解剖关键点（例如关节中心）是根据标记轨迹合成的，并通过基于计算机视觉的姿势估计系统预期的偏差和噪声进行增强。使用留一受试者验证来训练时间卷积和长短期记忆神经网络 (NN)，以根据合成的关键点和测量的 5 个跨髋肌肉的肌电图数据来预测神经肌肉骨骼建模输出。 HCF 的预测平均误差为峰值力的 13.4 ± 7.1%。预测关节角度和力矩的平均均方根误差分别为 5.3 度和 0.10 Nm/kg。神经网络可以检测由于步态改变而发生的峰值 HCF 变化，与神经肌肉骨骼模型非常一致 (r = 0.72)，最小可检测变化为 9.5%。开发的神经网络使用噪声合成的关键点位置来预测髋关节 OA 患者的 HCF 和下半身关节角度和力矩，误差可接受。由于步态改变而导致的 HCF 幅度的变化可以高精度预测。 这些发现对于在自然环境（即家庭、诊所）中对髋关节骨关节炎患者实施基于负荷修正的步态再训练干预措施具有重要意义。