AI-Driven Biomimetic Framework for Rapid Human Gait, Posture Assessment, and Adaptive Exoskeleton Configuration

Abstract

Human gait and posture assessment playsa fundamental role in rehabilitation engineering, orthopedic diagnosis, neurological disorder evaluation, sports biomechanics, and intelligent exoskeleton development. Conventional clinical assessment methods frequently depend on laboratory-based motion capture systems, wearable sensors, and expert interpretation, making large-scale deployment expensive and time-consuming. Recent developments in artificial intelligence (AI), deep learning, and computer vision have enabled markerless motion analysis using standard cameras while significantly improving computational efficiency and diagnostic accuracy. Simultaneously, biomimetic engineering has introduced biologically inspired mechanical structures capable of adapting exoskeleton behavior according to natural human movement patterns. Integrating these technologies provides an opportunity to establish intelligent diagnostic systems that rapidly analyze human locomotion while supporting adaptive exoskeleton configuration. This study proposes an AI-driven biomimetic framework that combines deep learning, computer vision, and biomechanical modeling for rapid gait and posture assessment together with adaptive exoskeleton parameter optimization. The framework employs multi-stage image acquisition, pose estimation, gait feature extraction, posture evaluation, biomechanical interpretation, AI-based decision support, and biomimetic adaptation for personalized exoskeleton configuration. TensorFlow and PyTorch provide scalable deep learning environments for model development, while convolutional neural networks and computer vision techniques enable efficient feature extraction from real-time video streams. Biomimetic principles ensure that robotic assistance follows physiological joint behavior rather than rigid predefined movement trajectories.