Three-dimensional Knee Joint Load Estimation System During Walking Using IMU Sensors for the Prevention of Knee Osteoarthritis: A Fundamental Study

Knee osteoarthritis (KOA) is a serious disease affecting one-third of the world's population, and mechanical loading on the knee joint has been identified as a progressive factor. However, conventional knee contact force measurements are limited to laboratory environments, making daily preventive monitoring difficult. This fundamental study aims to establish the optimal deep learning approach for three-dimensional knee contact force estimation using wearable IMU sensors and to validate the technical feasibility for practical KOA prevention systems. Methodologically, four deep learning models were systematically compared: 1D-CNN, LSTM, RNN, and Transformer. These models were trained to map 45-channel IMU time-series data comprising three-axis acceleration, angular velocity, and magnetic field from five anatomical locations to six-dimensional knee contact forces through 7-fold cross-validation. The 1D-CNN achieved the optimal maximum error of 24.01 %, while LSTM demonstrated the most stable average error of 13.93 %. These results showed superior performance compared to existing IMU-based methods, realizing three-dimensional knee joint force estimation within the practical accuracy range of wearable healthcare devices. This fundamental study successfully established the technical feasibility of translating laboratory-based knee joint load assessment to daily environments and presented clear guidelines for developing non-invasive KOA prevention systems.