A gait recognition architecture for early screening in the assessment of Parkinson's patients

Parkinson's disease (PD), a common neurodegenerative disorder, is estimated to affect over two million people in China, with a trend towards younger demographics, posing a significant challenge to public health. Traditional diagnostic methods rely heavily on manual detection, which is inefficient and highly susceptible to subjective biases. Gait recognition, as a long-distance, non-contact, and efficient human identification technology, has garnered attention from researchers. However, existing gait recognition architectures have deficiencies in gait feature capture, sampling flexibility, generalization ability, and task adaptability. In response to these challenges, we propose an enhanced hybrid architecture based on GaitBase, integrating four specialized expert subnetworks and adaptive feature fusion mechanisms, significantly enhancing the accuracy and efficiency of gait recognition, particularly in the field of Parkinson's disease gait recognition. Our model breaks through the limitations of traditional fixed convolution kernels, enabling irregular sampling, enhancing feature capture capabilities, and improving the model's generalization performance. Extensive comparative experiments conducted on the CASIA-B and OU-MVLP datasets have validated the model's robustness and superiority across multiple viewing angles. The model demonstrates comparable performance to existing methods, while outperforming them in specific viewpoints. Additionally, transfer learning experiments conducted on our self-built Parkinson's disease patient gait dataset further confirmed the model's effectiveness in recognizing and classifying gait features of Parkinson's disease patients. We provide a new technological pathway for the intelligent detection of Parkinson's disease and offer new insights for the application of gait recognition technology in broader fields.