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The early warning research on nursing care of stroke patients with intelligent wearable devices under COVID-19.
Li, Fengxia; Tao, Zhimin; Li, Ruiling; Qu, Zhi.
Afiliação
  • Li F; Huaihe Hospital of Henan University, College of Nursing and Health, Henan University, Kaifeng, 475001 China.
  • Tao Z; College of Nursing and Health, Henan University, Kaifeng, 475001 China.
  • Li R; College of Nursing and Health, Henan University, Kaifeng, 475001 China.
  • Qu Z; College of Nursing and Health, Henan University, Kaifeng, 475001 China.
Pers Ubiquitous Comput ; 27(3): 767-779, 2023.
Article em En | MEDLINE | ID: mdl-33526997
ABSTRACT
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.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Pers Ubiquitous Comput Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Pers Ubiquitous Comput Ano de publicação: 2023 Tipo de documento: Article