Previous clinical studies have reported that gait retraining is an effective non-invasive intervention for patients with medial compartment knee osteoarthritis. These gait retraining programs often target a reduction in the knee adduction moment (KAM), which is a commonly used surrogate marker to estimate the loading in the medial compartment of the tibiofemoral joint. However, conventional evaluation of KAM requires complex and costly equipment for motion capture and force measurement. Gait retraining programs, therefore, are usually confined to a laboratory environment. In this study, machine learning techniques were applied to estimate KAM during walking with data collected from two low-cost wearable sensors. When compared to the traditional laboratory-based measurement, our mobile solution using artificial neural network (ANN) and XGBoost achieved an excellent agreement with R ² of 0.956 and 0.947 respectively. With the implementation of a real-time audio feedback system, the present algorithm may provide a viable solution for gait retraining outside laboratory. Clinical treatment strategies can be developed using the continuous feedback provided by our system.

中文翻译：

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膝关节骨关节炎患者步态再训练膝关节内收力矩的实时估计

先前的临床研究报告说，步态再训练对内侧间隔膝关节骨关节炎患者是一种有效的非侵入性干预措施。这些步态再训练程序通常以减少膝关节内收力矩（KAM）为目标，KAM是一种常用的替代指标，用于估计胫股关节内侧隔室的负荷。但是，传统的KAM评估需要复杂且昂贵的设备来进行运动捕捉和测力。因此，步态再训练程序通常仅限于实验室环境。在这项研究中，机器学习技术被用于估计步行过程中的KAM，并从两个低成本的可穿戴传感器收集数据。与传统的实验室测量相比， ^2个分别为0.956和0.947。通过实时音频反馈系统的实现，本算法可以为在实验室外进行步态再训练提供可行的解决方案。可以使用我们系统提供的持续反馈来制定临床治疗策略。