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Patient care and monitoring of the admitted patients in big hospital wards is a serious, complex, continuous and tedious human intervention demanding task. Hospitalized patients experience bed and movement fall at the rate of 25 out of 1000 people in a year. This drastically increases hospitalization and treatment expenses. Though patient monitoring systems are proposed so far, most of them need human intervention. Completely automatic patient monitoring system is thus need of the hour. This paper proposes an automatic patient vital monitoring, fall prediction and fall detection system using IoT. This paper discusses the ideation, Design, development and testing of an IoT patient monitoring system. The proposed work mainly focuses on reducing the bed fall statistics using the proposed IoT monitored bed for patients with Medical Intranet of Things (MIoT) by issuing timely alarm to the Hospital Staff members in the ward (Nurse Station). The main objective of this work is to provide a complete automatic patient monitoring system with design consideration, feasibility study, usability of the system and user experience. The proposed Hospital bed is installed with motion detection, humidity, Health parameter and weight sensors which are installed in the bed or are wearable for detecting the patient movements and health parameters before the actual fall. This sensor will be connected to the hospital Intranet and the regular notification will be sent to duty nurse with periodic time interval. If a patient tries to exit the bed, the notification will be sent to the duty nurse for the necessary action. Additional bed wet sensors, emergency alarm system, body temperature sensors and other vital parameters will add very important patient monitoring functionalities to the system including behavioral and movement analysis. The entire ward data will be recorded and displayed in the nurse cabin for better monitoring, caring and analysis. The field testing was done using 50 critical patients over 220 patient days in three hospitals each and the resulting statistics is presented at the end for the system performance assessment. The system achieves around 90% improvements in the patient bed fall statistics. However, there are no exactly comparable published results of similar systems for quantitative benchmarking.

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Hospital Patient Monitoring and Fall Prediction System Using Medical Intranet of Things (IoT)

  • 会议论文

  • 发表日期：

    2025年

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  • M. Swarna,
  • M. Shanmugakumar,
  • S. Krishnaveni

摘要

Patient care and monitoring of the admitted patients in big hospital wards is a serious, complex, continuous and tedious human intervention demanding task. Hospitalized patients experience bed and movement fall at the rate of 25 out of 1000 people in a year. This drastically increases hospitalization and treatment expenses. Though patient monitoring systems are proposed so far, most of them need human intervention. Completely automatic patient monitoring system is thus need of the hour. This paper proposes an automatic patient vital monitoring, fall prediction and fall detection system using IoT. This paper discusses the ideation, Design, development and testing of an IoT patient monitoring system. The proposed work mainly focuses on reducing the bed fall statistics using the proposed IoT monitored bed for patients with Medical Intranet of Things (MIoT) by issuing timely alarm to the Hospital Staff members in the ward (Nurse Station). The main objective of this work is to provide a complete automatic patient monitoring system with design consideration, feasibility study, usability of the system and user experience. The proposed Hospital bed is installed with motion detection, humidity, Health parameter and weight sensors which are installed in the bed or are wearable for detecting the patient movements and health parameters before the actual fall. This sensor will be connected to the hospital Intranet and the regular notification will be sent to duty nurse with periodic time interval. If a patient tries to exit the bed, the notification will be sent to the duty nurse for the necessary action. Additional bed wet sensors, emergency alarm system, body temperature sensors and other vital parameters will add very important patient monitoring functionalities to the system including behavioral and movement analysis. The entire ward data will be recorded and displayed in the nurse cabin for better monitoring, caring and analysis. The field testing was done using 50 critical patients over 220 patient days in three hospitals each and the resulting statistics is presented at the end for the system performance assessment. The system achieves around 90% improvements in the patient bed fall statistics. However, there are no exactly comparable published results of similar systems for quantitative benchmarking.