A Personalized Gait Parameter Prediction-Based Speed-Adaptive Control Method for Hybrid Active-Passive Intelligent Prosthetic Knee

To address the limitations of current prosthetic knees that lack personalized adaptability to users' gait characteristics and walking speeds, this study proposes a personalized gait parameter prediction-based speed-adaptive control method for a hybrid active-passive intelligent prosthetic knee (HAPK). The proposed system integrates a perceptron-based model to predict individualized gait parameters by mapping anthropometric data and walking speed to key points of the knee trajectory. A fuzzy logic-based damping control for the swing phase and a position-torque control for the stance extension phase are developed to achieve real-time adaptation to different walking speeds and user-specific biomechanics. The hybrid actuation system combines hydraulic damping and motor torque assistance to ensure both compliance and power delivery across gait phases. Experimental results from variable-speed walking tests demonstrate that the proposed control method improves gait symmetry indices-reducing stance and swing asymmetries by approximately 30-38%-and achieves smoother, more natural gait transitions compared to traditional fixed-gait control strategies. These findings validate the effectiveness of the proposed approach in achieving continuous, personalized, and speed-consistent gait control for intelligent prosthetic knees.