GAIT based human Parkinson’s disease detection using fused features with multi-kernel support vector machine

Gait analysis plays a pivotal role in the detection and monitoring of Parkinson’s disease (PD), offering insights into motor impairments characteristic of the condition. This study proposes a novel approach employing advanced feature extraction and machine learning algorithms to enhance the classification of PD depends on gait features. In this study, both local as well as global features are extracted from the gait images to facilitate a classification process. Local feature extraction is achieved using an optimized convolutional neural network (OCNN), while global feature extraction is done by the Swin Transformer architecture. To further improve feature extraction, an Adaptive Glowworm Swarm Optimization (AGSO) algorithm is applied to improve the performance of the CNN. The fused local and global features are then fed into a Multi-Kernel Support Vector Machine (MK-SVM) classifier, which serves as the core of the proposed model. The performance of the model is evaluated using the GAIT-IT datasets, assessing metrics such as accuracy, precision, recall, and F-measure. As a result of the proposed model, performance is superior to that of traditional methods in terms of accuracy and reliability, offering a promising approach for PD detection based on gait analysis.