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Introduction: Several clinical studies have demonstrated that brain-computer interfaces (BCIs) controlled functional electrical stimulation (FES) facilitate neurological recovery in patients with stroke. This review aims to evaluate the effectiveness of BCI-FES training on upper limb functional recovery in stroke patients., Methods: PubMed, Embase, Cochrane Library, Science Direct and Web of Science were systematically searched from inception to October 2023. Randomized controlled trials (RCTs) employing BCI-FES training were included. The methodological quality of the RCTs was assessed using the PEDro scale. Meta-analysis was conducted using RevMan 5.4.1 and STATA 18., Results: The meta-analysis comprised 290 patients from 10 RCTs. Results showed a moderate effect size in upper limb function recovery through BCI-FES training (SMD = 0.50, 95% CI: 0.26-0.73, I 2 = 0%, p < 0.0001). Subgroup analysis revealed that BCI-FES training significantly enhanced upper limb motor function in BCI-FES vs. FES group (SMD = 0.37, 95% CI: 0.00-0.74, I 2 = 21%, p = 0.05), and the BCI-FES + CR vs. CR group (SMD = 0.61, 95% CI: 0.28-0.95, I 2 = 0%, p = 0.0003). Moreover, BCI-FES training demonstrated effectiveness in both subacute (SMD = 0.56, 95% CI: 0.25-0.87, I 2 = 0%, p = 0.0004) and chronic groups (SMD = 0.42, 95% CI: 0.05-0.78, I 2 = 45%, p = 0.02). Subgroup analysis showed that both adjusting (SMD = 0.55, 95% CI: 0.24-0.87, I 2 = 0%, p = 0.0006) and fixing (SMD = 0.43, 95% CI: 0.07-0.78, I 2 = 46%, p = 0.02). BCI thresholds before training significantly improved motor function in stroke patients. Both motor imagery (MI) (SMD = 0.41 95% CI: 0.12-0.71, I 2 = 13%, p = 0.006) and action observation (AO) (SMD = 0.73, 95% CI: 0.26-1.20, I 2 = 0%, p = 0.002) as mental tasks significantly improved upper limb function in stroke patients., Discussion: BCI-FES has significant immediate effects on upper limb function in subacute and chronic stroke patients, but evidence for its long-term impact remains limited. Using AO as the mental task may be a more effective BCI-FES training strategy., Systematic Review Registration: Identifier: CRD42023485744, https://www.crd.york.ac.uk/prospero/display&#95;record.php?ID=CRD42023485744., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Ren, Li, Gao, Pan, Wang, Yang, Duan, Guo and Zhang.)

Objective: The intelligent infusion management system is established through intelligent internet of things, by using the gravity sensor, acceleration sensor and other equipment., Methods: Establish an infusion management platform based on B/S architecture. Combined with the intelligent terminal and get the major values, including:the infusion speed, the remaining liquid and so on. Besides, it can integrate with the HIS, EMR and PDA., Results: It has solved the data collection of the whole infusion process, realized the real-time visualization during the infusion, and improved the quality of nursing management., Conclusions: In the paper, it has been used in wards more than 2 years, which has reduced the workload of the infusion and improved the satisfaction of patients. At the same time, it has provided the infusion big data collection and statistics for the research and auxiliary treatment.