Human gait and posture analysis have become fundamental components of modern rehabilitation engineering, assistive robotics, and intelligent healthcare systems. Conventional assessment methods frequently rely on laboratory-based motion capture systems, which are expensive, time-consuming, and difficult to deploy in routine clinical practice. Recent advances in artificial intelligence (AI), computer vision, and deep learning have enabled the development of portable diagnostic platforms capable of performing real-time biomechanical assessment using low-cost imaging devices. Simultaneously, biomimetic engineering has emerged as an effective strategy for designing exoskeletons that replicate natural human movement while improving rehabilitation outcomes. This paper proposes an Intelligent Biomimetic System for Rapid Gait Assessment and AI-Based Exoskeleton Configuration, integrating AI-driven pose estimation, gait feature extraction, biomechanical modeling, and adaptive exoskeleton parameter optimization within a unified framework. The proposed methodology combines convolutional neural networks, human pose estimation, and biomimetic principles to generate clinically meaningful gait metrics while automatically recommending individualized exoskeleton configurations. TensorFlow-based deep learning architectures enable efficient model training and real-time inference, whereas modern pose estimation techniques improve movement tracking under practical operating conditions (Abadi et al., 2016; Palermo et al., 2021). The study synthesizes existing AI frameworks, evaluates their applicability to rehabilitation robotics, and identifies methodological improvements for intelligent assistive systems. The proposed framework demonstrates how AI-driven biomechanical assessment can enhance diagnostic efficiency, reduce configuration time, and support personalized rehabilitation. The paper contributes a comprehensive methodology suitable for next-generation rehabilitation platforms that combine computer vision, intelligent analytics, and biomimetic engineering.  

Biomimetic Systems, Artificial Intelligence, Human Gait Analysis, Posture Assessment

Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., :::, & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. In 12th USENIX Symposium on Operating Systems Design and Implementation, 265–283.

Candel, A., & LeDell, E. (2016). Deep learning with H2O. USA: H2O.ai.

Chen, T., Li, M., Li, Y., Lin, M., Wang, N., Wang, M., :::, & Zhang, Z. (2015). MXNet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv Preprint:1512.01274.

Falch, L., & Lohan, K. S. (2024). Webcam-based gaze estimation for computer screen interaction. Frontiers in Robotics and AI, 11, 1369566.

Jia, Y., Shelhamer, E., Donahue, J., Karayev, S., Long, J., Girshick, R., :::, & Darrell, T. (2014). Caffe: Convolutional architecture for fast feature embedding. In Proceedings of the 22nd ACM International Conference on Multimedia, 675–678.

Ma, Y., Yu, D., Wu, T., & Wang, H. (2019). Paddle Paddle: An open-source deep learning platform from industrial practice. Frontiers of Data & Computing, 1(1), 105–115.

Nirupama, P., Lokesh, G., Veerana gaiah, C., & Bhanu Prakash, P. (2021). Automatic motorcyclist helmet rule violation detection using TensorFlow and Keras in OpenCV. International Journal of Advanced Research in Engineering and Technology, 12(4), 65–74.

Palermo, M., Moccia, S., Migliorelli, L., Frontoni, E., & Santos, C. P. (2021). Real-time human pose estimation on a smart walker using convolutional neural networks. Expert Systems with Applications, 184, 115498.

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., :::, & Chintala, S. (2019). PyTorch: An imperative style, high-performance deep learning library. In 33rd Conference on Neural Information Processing Systems, 1–12.

K. Ramamurthy, R. K. Konduru and N. Amanmadov, "Evo Graph Coder: An Evolutionary Graph-Reasoning Framework for Self-Adaptive Software Engineering," in IEEE Access, vol. 14, pp. 63063-63076, 2026, doi: 10.1109/ACCESS.2026.3686019.

Geo Philip, Paulson, Integrated Intelligent Building Energy Management: A Multi-Layer Framework for Renewable Energy, HVAC Optimization, and Smart Electrical Network Coordination. Available at SSRN: https://ssrn.com/abstract=6993209 or http://dx.doi.org/10.2139/ssrn.6993209