Predicting Severe Knee Arthritis Based on Two Inertial Measurement Unit Sensors as a Dynamic Coordinate System Using Classical Machine Learning

The novel features based on the dynamic coordinate system, along with the success of the proposed KNN model, demonstrate the effectiveness of the proposed algorithm in diagnosing between signals received from healthy individuals and patients. The proposed algorithm outperforms existing methods in similar articles in sensitivity showing an improvement of 4% and at least. The main objective of this study is to investigate the feasibility of using a wearable device as an auxiliary tool in the diagnosis of arthritis. The reported results in this study are related to two groups of individuals with severe arthritis (grade 4), and there is a possibility of weaker results with the current method.