Global joint information extraction convolution neural network for Parkinson's disease diagnosis

Parkinson's Disease (PD) is a prevalent neurodegenerative condition, ranking second in incidence only to Alzheimer's disease. Presently, the diagnosis of PD using sensors and computer vision can recognize characteristics like speech, respiration, and gait in patients. However, these methods typically require the use of complex hardware systems or extensive data analysis when collecting or processing motion data from various joints of the patients. In this research, an efficient diagnostic system for PD is introduced, which utilizes common smartphones to capture videos of 42 healthy volunteers and 30 Parkinson's patients walking under different conditions. The system utilizes human pose estimation technology to extract three-dimensional joint coordinates, simplifying the data collection process. Afterward, joint coordinates are transformed into two-dimensional images using color mapping techniques, thus converting the diagnosis into an image recognition challenge. In contrast to video processing, this approach reduces 6.51 million parameters (22%) and reduces testing time by 62.92%. Finally, by combining ResNet50 and random forest algorithms, decision accuracy is improved using two-dimensional images. Experimental evidence substantiates the system's capability to efficiently identify Parkinsonian gait. The recognition rate for the leg joint group is 88.89%, and it would increase to 91.67% when factoring in the four-limb joint group. The system provides the potential for preliminary at-home diagnosis, holding significant clinical application potential, and diagnostic value for other movement-related disorders.