IEEE Trans Neural Syst Rehabil Eng. 2020 Apr;28(4):888-894. doi: 10.1109/TNSRE.2020.2978537. Epub 2020 Mar 5.
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 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.
先前的临床研究报告称，步态再训练是治疗膝关节内侧间室骨关节炎患者的一种有效非侵入性干预措施。这些步态再训练方案通常以减少膝内收力矩（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间室的负荷。然而，传统的 KAM 评估需要复杂且昂贵的运动捕捉和力测量设备。因此，步态再训练方案通常仅限于实验室环境。在这项研究中，我们应用机器学习技术，使用从两个低成本可穿戴传感器收集的数据来估计行走时的 KAM。与传统的基于实验室的测量方法相比，我们使用人工神经网络（ANN）和 XGBoost 的移动解决方案分别实现了 R 值为 0.956 和 0.947 的极好一致性。通过实施实时音频反馈系统，本算法可能为实验室外的步态再训练提供一种可行的解决方案。可以使用我们系统提供的连续反馈来制定临床治疗策略。
