Li Sujiao, Wu Kun, Meng Qiaoling, Yu Hongliu
Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China.
Shanghai Engineering Research Center of Assistive Devices, Shanghai 200093, P. R. China.
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2022 Jun 25;39(3):620-626. doi: 10.7507/1001-5515.202112046.
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.
目前,临床上常采用量表法对脑卒中患者的上肢功能进行评估,但该方法存在耗时、评估结果一致性差以及康复医师参与度高等问题。为克服量表法的缺点,结合传感器和机器学习算法的智能上肢功能评估系统已成为近年来的热门研究课题之一。首先,对常用的临床上肢功能评估方法进行了分析和总结。然后综述了近年来智能评估系统的研究情况,重点介绍了智能评估系统数据采集和数据处理部分所采用的技术及其优缺点。最后,讨论了智能评估系统当前面临的挑战和未来的发展方向。希望这篇综述能为相关领域的研究人员提供有价值的参考信息。