detalle del documento
IDENTIFICACIÓN
oai:arXiv.org:2404.01654
Tema
Computer Science - Computer Vision... Computer Science - Artificial Inte... Electrical Engineering and Systems... Electrical Engineering and Systems...Autor
Xiang, Xiang Zhang, Zihan Ma, Jing Deng, YaoCategoría
Computer Science
Año
2024
fecha de cotización
10/4/2024
Palabras clave
parkinson engineering ai disease scienceResumen
Parkinson's Disease (PD) is the second most common neurodegenerative disorder.
The existing assessment method for PD is usually the Movement Disorder Society - Unified Parkinson's Disease Rating Scale (MDS-UPDRS) to assess the severity of various types of motor symptoms and disease progression.
However, manual assessment suffers from high subjectivity, lack of consistency, and high cost and low efficiency of manual communication.
We want to use a computer vision based solution to capture human pose images based on a camera, reconstruct and perform motion analysis using algorithms, and extract the features of the amount of motion through feature engineering.
The proposed approach can be deployed on different smartphones, and the video recording and artificial intelligence analysis can be done quickly and easily through our APP.
;Comment: Technical report for AI WALKUP, an APP winning 3rd Prize of 2022 HUST GS AI Innovation and Design Competition
Xiang, Xiang,Zhang, Zihan,Ma, Jing,Deng, Yao, 2024, AI WALKUP: A Computer-Vision Approach to Quantifying MDS-UPDRS in Parkinson's Disease
Clinical Relevance of Plaque Distribution for Basilar Artery Stenosis
study endovascular imaging wall basilar complications plaque postoperative artery plaques stenosis
