Crouch Gait Recognition of Children with Cerebral Palsy Based on CNN-LSTM Hybrid Model

Abstract

Crouch gait is one of the key characteristics of children with cerebral palsy, and early detection of gait changes is crucial for subsequent exoskeleton-assisted therapy. This study uses the Vicon 3D motion capture system to collect experimental data on four gait phases of children with cerebral palsy and introduces a CNN-LSTM hybrid model. The model combines the spatial feature extraction strengths of CNN with the temporal sequence modeling capabilities of LSTM, enabling it to effectively identify the complex dynamic changes in gait specific to children with cerebral palsy. By integrating these two components, the model not only accurately extracts key gait features but also captures the temporal dependencies within the gait cycle, allowing for precise recognition of crouch gait. Experimental results demonstrate that the proposed model exhibits good robustness and achieves high accuracy in both overall gait recognition and distinguishing the four individual gait phases. It significantly outperforms traditional machine learning architectures.