Real-Time Neurological Disease Prediction with 3D Single Pose Estimation using MediaPipe
Keywords:
regression, neurological, MediaPipe, estimate, circumstances, generalizabilityAbstract
For the diagnosis and analysis of chronic diseases including Cerebellar Ataxia (CA), Spinocerebellar Ataxia (SCA), and Parkinson's disease, artificial intelligence (AI) is an emerging field. Many doctors, diagnosis teams, and medical professionals benefit from the identification and analysis of neurological illnesses made possible by AI technologies like machine learning and deep learning. Our previous research was completed with the gait value analysis for foot position. This paper aims to conduct the research in real time prediction of neurological disease. For enhancing disease detection model performance and generalizability, diverse datasets from various populations and circumstances must be gathered and annotated. In this research, we applied MediaPipe for real-time disease recognition in videos. We gathered the gait values from the real time videos. The collected gait values are experimented with the target gait values of normal persons. It shows the difference of the gait analysis which helps to predict the disease. The single pose estimation is applied for the activities of human to analyse the gait values. It estimate the gait values in different ankles and compare with the target values. Our research exhibits the predicted result in the real time video of human actions. The experimental result provides the person is affected with neurological disease or a normal person with single pose estimation. Logistic regression algorithm is used to predict the accuracy of the disease. We got 98.53% accuracy for logistic regression algorithm.
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