Classification of patients with patellofemoral pain syndrome during running based on hip joint kinematic feature using machine learning method

As a prevalent knee joint disorder, Patellofemoral Pain Syndrome (PFPS) is identified by musculoskeletal issues, often involving symptoms such as pain experienced around or posterior to the patella. Hip joint kinematics may play an essential role in PFPS. Although this condition has been studied from various perspectives, there is no definitive standard clinical method for diagnosis and functional classification. This study aimed to identify the most significant hip joint kinematic features for classifying PFPS patients during running using machine learning (ML) methods. Seven females with unilateral PFPS were paired with controls considering factors such as age, weight, height, and duration of physical activity. In total, 560 running cycles were captured utilizing a motion analysis system based on an inertial measurement unit (IMU). Hip joint kinematic variables, including three-dimensional angles, velocity, and acceleration, were measured. Nineteen features were used as inputs for ML algorithms. Four models—SVM, KNN, ANN, and RF—achieved 99% accuracy in classifying healthy and PFPS patients. Maximum hip adduction emerged as the most significant kinematic feature, and the SVM model performed best for PFPS classification. In conclusion, this study demonstrates that combining IMU sensors with machine learning techniques provides an accurate approach for diagnosing PFPS during running in non-laboratory and clinical environments. Moreover, frontal plane hip joint kinematics appears to be a critical factor in identifying this condition.