Knee Osteoarthritis Classification System Examination on Wearable Daily-use IMU Layout
Chengshuo Xia, Tsubasa Maruyama, Haruki Toda, Mitsunori Tada, Koji Fujita, Yuta Sugiura
Reference / 引用はこちら
Chengshuo Xia, Tsubasa Maruyama, Haruki Toda, Mitsunori Tada, Koji Fujita, Yuta Sugiura, Knee Osteoarthritis Classification System Examination on Wearable Daily-use IMU Layout, The 2022 International Symposium on Wearable Computers (ISWC ’22), ACM.
The diagnostic approach for knee osteoarthritis that draws on kinematic characteristics provides a solution other than imaging medicine. However, gait-based kinematic analysis still requires a motion capture suit as a prerequisite to ensure a reliable calculation, which limits the daily screening at the end user. To further reduce the cost, we investigated a wearable inertial measurement unit (IMU)-based knee osteoarthritis classification system based on daily-use wearing IMU layout and machine learning approaches. The acceleration and angular velocity signal output from the IMU were used as the input data; the different features from the time and frequency domains were examined with different handcrafted feature classifiers, as well as the deep learning method. From the results, using three IMUs could reach a 0.82 area under the curve value, with a sensitivity of 86% and a specificity of 78%. The results showed that using daily IMU devices to establish a diagnostic system with an on-body sensor layout is feasible.