基于计算机视觉与人工智能的智能监护系统

      Intelligent Monitoring System Based on Computer Vision and Artificial Intelligence

      • 摘要: 为保障在院患者护理质量,应对眼科医院术后患者复杂多变的情况进行更加全面、准确、实时的监测,该文设计了基于计算机视觉及人工智能(artificial intelligence, AI)的智能监护系统,用于实时监测患者的健康状况。其主要应用场景包括医疗监护、康复治疗和住院护理等,涵盖智能采集设备、智能摄像头、连续生理数据分析算法与人工智能算法及软件。由于眼科医院术后患者的情况复杂多变,对患者进行全面、准确、实时的监测很必要,因此需要探索一种低生理负担和心理压力的监测技术。智能监护系统可以连续采集患者的生理参数指标,在对监测数据进行智能算法分析后,将其传输至医生工作站或护士站,为患者监护、病情评估、风险预警等提供新的工具与手段。同时,应用计算机视觉和人工智能技术,还可以分析面部表情、人体姿态等数据,识别患者的情绪状态和卧床姿态,及时发现异常情况并采取相应的措施。这有助于改善医护人员的日常工作,提升病区中单间病房的护理安全,且可用于危重患者、老年患者的护理,并提高护理效率和质量。

         

        Abstract: To ensure the quality of care for inpatients in ophthalmic hospitals, address the complex and variable conditions of postoperative patients, and conduct more comprehensive, accurate and real-time monitoring of patients, an intelligent monitoring system based on computer vision and artificial intelligence has been designed. This system is employed for real-time monitoring of patient health conditions and intelligent care, with primary applications in medical monitoring, rehabilitation therapy, and inpatient care. It comprises intelligent data acquisition devices, smart cameras, continuous physiological data analysis algorithms, AI algorithms, and software. Given the complex and variable conditions of postoperative patients in ophthalmic hospitals, a comprehensive, accurate, and real-time monitoring of patients is required. Therefore, it is necessary to explore a monitoring technology that imposes low physiological and psychological burdens. The intelligent monitoring system can continuously collect patients' physiological parameter indicators and transmit the monitoring data to doctors' workstations or nurse stations after analysis using intelligent algorithms, providing new tools for patient monitoring, disease assessment, risk warning, and more. Furthermore, through the application of computer vision and artificial intelligence technologies, the system can analyze facial expressions, body postures, and other data to identify patients' emotional states and bedridden postures, enabling the timely detection of abnormal situations and implementation corresponding measures. This helps improving the daily work of medical staff, enhance the nursing safety in single-patient rooms in wards, and potentially find applications in the care of critically ill patients and elderly patients, thereby improving nursing efficiency and quality.

         

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