Stroke patients under the background of the new crown epidemic need to be home-based care. However, traditional nursing methods cannot take care of the patients’ lives in all aspects. Based on this, based on machine learning algorithms, our work combines regression models and SVM to build a smart wearable device system and builds a system prediction module to predict patient care needs. The node is used to collect human body motion and physiological parameter information and transmit data wirelessly. The software is used to quickly process and analyze the various motion and physiological parameters of the patient and save the analysis and processing structure in the database. By comparing the results of nursing intervention experiments, we can see that the smart wearable device designed in this paper has a certain effect in stroke care.

新冠疫情背景下的脑卒中患者需要居家护理。然而，传统的护理方法无法全方位照顾患者的生活。基于此，我们的工作基于机器学习算法，结合回归模型和SVM构建智能可穿戴设备系统，并构建系统预测模块来预测患者护理需求。该节点用于采集人体运动和生理参数信息并无线传输数据。该软件用于快速处理和分析患者的各种运动和生理参数，并将分析处理结构保存在数据库中。通过对比护理干预实验结果可以看出，本文设计的智能可穿戴设备在脑卒中护理中具有一定的效果。