An upper-limb-movement classification system of cerebral palsy children based on arm motion detection.

The researches about upper limb palsy patients are the minority areas among the researches about cerebral palsy (CP) patients. This paper presents an upper-limb-movement classification system of cerebral palsy children based on their arm motion information to judge their impairment degree. The system contains three parts: image capture, image segmentation, and information classification processing. Momentum analysis parameters and coordination neural network are used to conduct the data classification. The experimental results are shown that the proposed system has the higher accurate rate of tracking compared with Normalized Cross Correlation and Otsu methods, and the patients are divided into the slight impairment grade or the serious impairment grade.