School of Microelectronics, South China University of Technology, Guangzhou, China.
The First Affiliated Hospital of Nanchang University, Nanchang, China.
JMIR Aging. 2024 Nov 25;7:e58261. doi: 10.2196/58261.
Patients with knee osteoarthritis (KOA) often present lower extremity motor dysfunction. However, traditional radiography is a static assessment and cannot achieve long-term dynamic functional monitoring. Plantar pressure signals have demonstrated potential applications in the diagnosis and rehabilitation monitoring of KOA.
Through wearable gait analysis technology, we aim to obtain abundant gait information based on machine learning techniques to develop a simple, rapid, effective, and patient-friendly functional assessment model for the KOA rehabilitation process to provide long-term remote monitoring, which is conducive to reducing the burden of social health care system.
This cross-sectional study enrolled patients diagnosed with KOA who were able to walk independently for 2 minutes. Participants were given clinically recommended functional tests, including the 40-m fast-paced walk test (40mFPWT) and timed up-and-go test (TUGT). We used a smart shoe system to gather gait pressure data from patients with KOA. The multidimensional gait features extracted from the data and physical characteristics were used to establish the KOA functional feature database for the plantar pressure measurement system. 40mFPWT and TUGT regression prediction models were trained using a series of mature machine learning algorithms. Furthermore, model stacking and average ensemble learning methods were adopted to further improve the generalization performance of the model. Mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE) were used as regression performance metrics to evaluate the results of different models.
A total of 92 patients with KOA were included, exhibiting varying degrees of severity as evaluated by the Kellgren and Lawrence classification. A total of 380 gait features and 4 physical characteristics were extracted to form the feature database. Effective stepwise feature selection determined optimal feature subsets of 11 variables for the 40mFPWT and 10 variables for the TUGT. Among all models, the weighted average ensemble model using 4 tree-based models had the best generalization performance in the test set, with an MAE of 2.686 seconds, MAPE of 9.602%, and RMSE of 3.316 seconds for the prediction of the 40mFPWT and an MAE of 1.280 seconds, MAPE of 12.389%, and RMSE of 1.905 seconds for the prediction of the TUGT.
This wearable plantar pressure feature technique can objectively quantify indicators that reflect functional status and is promising as a new tool for long-term remote functional monitoring of patients with KOA. Future work is needed to further explore and investigate the relationship between gait characteristics and functional status with more functional tests and in larger sample cohorts.
膝骨关节炎（KOA）患者常表现出下肢运动功能障碍。然而，传统影像学检查是一种静态评估，无法实现长期动态功能监测。足底压力信号在 KOA 的诊断和康复监测中具有潜在的应用价值。
通过可穿戴步态分析技术，我们旨在基于机器学习技术获取丰富的步态信息，开发一种简单、快速、有效且患者友好的 KOA 康复过程功能评估模型，以提供长期远程监测，有利于减轻社会医疗保健系统的负担。
本横断面研究纳入了能够独立行走 2 分钟的 KOA 患者。参与者接受了临床推荐的功能测试，包括 40m 快步走测试（40mFPWT）和计时起立行走测试（TUGT）。我们使用智能鞋系统从 KOA 患者收集步态压力数据。从数据中提取多维步态特征和身体特征，建立足底压力测量系统的 KOA 功能特征数据库。使用一系列成熟的机器学习算法训练 40mFPWT 和 TUGT 回归预测模型。此外，采用模型堆叠和平均集成学习方法进一步提高模型的泛化性能。平均绝对误差（MAE）、平均绝对百分比误差（MAPE）和均方根误差（RMSE）被用作回归性能指标来评估不同模型的结果。
共纳入 92 例 KOA 患者，根据 Kellgren 和 Lawrence 分级评估为不同严重程度。共提取 380 个步态特征和 4 个身体特征，形成特征数据库。有效的逐步特征选择确定了 40mFPWT 的 11 个变量和 TUGT 的 10 个变量的最佳特征子集。在所有模型中，使用 4 个基于树的模型的加权平均集成模型在测试集中具有最佳的泛化性能，对于 40mFPWT 的预测，MAE 为 2.686 秒，MAPE 为 9.602%，RMSE 为 3.316 秒，对于 TUGT 的预测，MAE 为 1.280 秒，MAPE 为 12.389%，RMSE 为 1.905 秒。
这种可穿戴足底压力特征技术可以客观地量化反映功能状态的指标，有望成为 KOA 患者长期远程功能监测的新工具。未来需要进一步探索和研究更多功能测试和更大样本队列中步态特征与功能状态之间的关系。
