Vectorizing Human Gait: An Adaptive Kinematic Embedding for Monitoring and Intent Detection.

Lower-limb exoskeletons offer high-dose gait rehabilitation, but their effectiveness depends on accurate coordination between the user and robotic controller. We propose a real-time method to estimate and represent the user's intended motions from hip and knee joint angle feedback. A kinematic feature vector is obtained through a processing pipeline including gait phase estimation, standardization, and recursive fitting of a von Mises basis function model to the hip and knee flexion angles. We show that the basis functions coefficients form a rich feature vector for classifying and detecting changes in gait patterns, while also implicitly providing real-time maximum likelihood estimates of the joint kinematics over a gait cycle, independent of user's current gait phase within the cycle. The algorithm was evaluated on 16 participants across 16 treadmill and overground walking conditions, demonstrating accurate reconstructions of joint kinematics. We show that gait cycle kinematics are accurately modeled by the von Mises basis model, with coefficients of determination above ${0}.{90} \pm {0}.{03}$ . The feature vector varies meaningfully across different walking conditions, enabling efficient real-time detection of changes, such as asymmetric limping.