At present, the upper limb function of stroke patients is often assessed clinically using a scale method, but this method has problems such as time-consuming, poor consistency of assessment results, and high participation of rehabilitation physicians. To overcome the shortcomings of the scale method, intelligent upper limb function assessment systems combining sensors and machine learning algorithms have become one of the hot research topics in recent years. Firstly, the commonly used clinical upper limb functional assessment methods are analyzed and summarized. Then the researches on intelligent assessment systems in recent years are reviewed, focusing on the technologies used in the data acquisition and data processing parts of intelligent assessment systems and their advantages and disadvantages. Lastly, the current challenges and future development directions of intelligent assessment systems are discussed. This review is hoped to provide valuable reference information for researchers in related fields.
We aimed to establish an optical coherence tomography (OCT) system to measure the strain of blood vessels. A general OCT system was constructed firstly and its reliability was confirmed by comparing the OCT imaging of the porcine coronary and the corresponding histological slices. The strain of the porcine coronary was induced by static flow pressure and correlation algorithm was used to calculate the strain field of blood vessels within OCT images. The results suggest that bright-dark stratification of blood vessels displayed in OCT images is consistent with the intima and media layers of histological image. Furthermore, the strain of media layer is greater than that of the intima layer under the same static pressure. The optical coherence imaging system could not only measure the histological structure of the blood vessels, but also qualify the vessel strain under flow pressure.