Trajectory generation for lower extremity exoskeleton robot in level walking rehabilitation using trajectory morphing method

Lower extremity exoskeletons play a crucial role in rehabilitation by assisting individuals with walking impairments in restoring natural gait patterns. However, conventional trajectory generation methods rely on predefined gait patterns that do not adapt to individual variations, limiting rehabilitation effectiveness. This research introduces a trajectory morphing method to dynamically generate rehabilitation trajectories, enabling more personalized and adaptive gait restoration for individuals with mobility impairments, including stroke, spinal cord injury, and trauma-induced gait abnormalities. The objectives of this study are to design and develop a lower limb exoskeleton model that facilitates the transition from abnormal to normal gait, investigate trajectory morphing as an adaptive rehabilitation strategy, and validate the effectiveness of the control system in restoring natural walking. A lower limb exoskeleton model, integrated with a control system, was developed to adjust abnormal gait trajectories, bringing them closer to standard gait patterns using kinematic and gait analysis, ensuring trajectory adaptation to individual needs. Simulations were conducted using gait data from individuals with impairments, including cases such as Masasrol (fall trauma affecting the lumbar region), Mazar (spinal cord injury from a neurodegenerative disease), Rahiman (stroke-induced gait impairment), and Aman (accident-related trauma). The methodology focused on analyzing joint kinematics, while quantitative metrics such as RMSE (Root Mean Square Error) and DTW (Dynamic Time Warping) were used to measure trajectory deviation from normal reference gait patterns. The results indicate that the proposed trajectory morphing method significantly improves gait restoration, with RMSE analysis showing a reduction in trajectory error, suggesting improved trajectory accuracy, while DTW scores demonstrated greater similarity to normal gait patterns, confirming the system’s ability to adapt to individual gait deviations. The generated trajectories successfully reduced trajectory deviations, leading to smoother gait transitions and enhanced rehabilitation outcomes. This study contributes to the advancement of exoskeleton-assisted rehabilitation by introducing a patient-specific trajectory generation method that enhances adaptability and clinical effectiveness. The findings provide a foundation for future developments in intelligent exoskeleton control, enabling real-time trajectory adjustments for optimized rehabilitation efficiency.

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