JMIR Publications

ABSTRACT

Background:

Patients with knee osteoarthritis (KOA) often present lower extremity motor dysfunction. However, traditional radiography is a static assessment, cannot achieve long-term dynamic functional monitoring. Plantar pressure signals have demonstrated potential applications in the diagnosis and rehabilitation monitoring of KOA.

Objective:

Through the wearable gait analysis technology to obtain abundant gait information, based on machine learning method to develop a simple, rapid, effective and patient-friendly functional assessment method for the KOA rehabilitation process to provide long-term remote monitoring, is conducive to reducing the burden of social health care system.

Methods:

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 KOA patients. The multi-dimensional gait features extracted from the data and physical characteristics 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 utilized as regression performance metrics to evaluate the results of different models.

Results:

A total of 92 KOA patients 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 40mFPWT and 10 variables for TUGT. Among all models, weighted average ensemble model using four tree-based models has the best generalization performance in test set, with MAE of 2.686 seconds, MAPE of 9.602%, and RMSE of 3.316 seconds for the prediction of 40mFPWT, and for TUGT with MAE of 1.280 seconds, MAPE of 12.389%, and RMSE of 1.905 seconds.

Conclusions:

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 KOA patients. 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.