KR102565174B1 - Ward alarm and monitoring system for unexpected accident using heat sensor - Google Patents

Abstract

The present invention is configured to simultaneously determine the abnormal body temperature of a patient in each bedroom and whether or not a fall accident occurs using a heat-detection sensor, thereby not only improving the problem of invasion of personal privacy by not using a separate camera and thermal imaging camera. In addition, installation cost is reduced, low-power operation is possible, and after the local management server analyzes the heat-sensing information received from the data collection devices to recognize the object, the temperature values and location information of the recognized object are It is configured to determine the patient's abnormal body temperature and whether a fall accident has occurred, so that the calculation process is simple and the accuracy of object recognition can be increased. It transmits unexpected situation data including the contents of the emergency situation, ward and room identification numbers, and when the management program of the manager terminal receives the unexpected situation data from the local management server, by outputting warning alarm information to the outside, Remote monitoring is possible, improving work efficiency and maximizing the efficiency of the nursing management system. In the event of an unexpected situation, rapid follow-up is possible, which can increase the safety and reliability of medical services. It's about the system.

Description

Ward alarm and monitoring system for unexpected accident using heat sensor}

The present invention relates to an alarm and monitoring system for an emergency situation in a ward using a heat sensor, and more specifically, to detect and monitor the body temperature and fall of each patient in each ward using the heat sensor, and simultaneously detect and monitor an emergency situation. When a situation is detected, it is output to the outside through the output means, so that the patient's condition can be remotely monitored to improve management efficiency and convenience, as well as to reduce unnecessary manpower consumption, and to quickly respond to unexpected situations. It relates to a ward emergency alarm and monitoring system using a heat-sensing sensor that can increase service safety and reliability and can be installed and operated at low cost.

In general, a hospital is a place for providing lodging and treatment to patients for various treatment purposes, and each ward is equipped with one to a plurality of bedrooms.

Since these hospitals prioritize the safety of patients, they periodically examine and check the patient's condition, but various unexpected situations such as sudden abnormal patient conditions and falls in the bedroom are occurring invariably.

In particular, a fall is an accident in which a patient suddenly falls to a low position regardless of his or her intention. According to a domestic study, 70.8% of falls occur in hospitals, and most often occur between 22:00 and 06:00. reported

Accordingly, various studies are being conducted on a patient monitoring system for remotely monitoring a patient's condition such as a fall and at the same time outputting an unexpected situation to the outside so that a prompt response can be made.

1 is a block diagram showing a patient fall prevention monitoring device disclosed in Korean Patent Registration No. 10-1712191 (Title of Invention: Patient Fall Prevention Monitoring Device).

The patient fall prevention monitoring device (hereinafter referred to as the prior art) 100 of FIG. 1 uses a Raspberry Pi 111 based on an embedded board and an optical sensor 112 having a camera to The monitoring unit 101 photographs the uncomfortable patient's bed and processes the signal, and the data processed by the monitoring unit 101 is analyzed by the OpenCV program 122 to infer the movement of the patient and the server (123) and stored in the database 124, the image processing unit 102 corresponding to the client, and black pixels and white pixels according to the patient sleeping in the right posture, sleeping on the side, and sitting postures inferred by the image processing unit 102 The posture determination unit 103 that determines the patient's bed posture by calculating the number of pixels on the screen for each pixel, and the data processed by the image processing unit 102 are transferred to the management center 105 using TCP/IP communication or web sockets. When a picture processed by the embedded board 121 of the image processing unit 102 is transmitted as html, the video communication unit 104 transmits to , and the management center 105 receives, monitors, and manages the picture in the PC.

In addition, the difference image analysis performed by the OpenCV program 122 analyzes the difference images constructed by the contrast difference between the adjacent (i-1) th image and the i th image to infer the movement form of the moving object, and The method of inferring the movement type for is, if there is a background screen taken in advance and a screen to be detected, the image is converted to gray scale, and the converted gray scale screen is converted to black and white through binarization. A screen is created, morphology image processing is performed to reduce the noise of the binarized screen, and moving average image processing is performed so that even if the background image changes, a non-moving object can be applied to the background image.

Unlike the conventional contact method or count method, the prior art 100 configured as described above can significantly reduce malfunction or delay in information transmission because it rapidly and accurately monitors the patient's fall and departure through non-contact real-time image analysis. Since information on this uncomfortable inpatient can be monitored in real time by hospital personnel, it has the advantage of preventing accidents caused by patients falling or leaving the bed in advance and performing efficient nursing management tasks.

However, the prior art 100 analyzes the image obtained by the photographing of the optical sensor (camera) 112, in detail, generates a difference image, and then displays the black pixels and white pixels of the generated difference image on the screen. Since it is configured to determine the patient's bed posture by calculating the number of pixels, real-time image analysis for each of a plurality of bedrooms results in an excessively complex amount of computational processing and a problem in that computational processing time is consumed.

In addition, the prior art 100 has a disadvantage in that the installation cost is high because the expensive camera 112 must be installed at the top of each bedroom.

In addition, the prior art 100 can monitor and detect whether or not a fall has occurred, but has a structural limitation in that it cannot monitor and detect sudden situations such as abnormal body temperature.

In addition, the prior art 100 has a disadvantage in that the patient's personal privacy problem is caused because the patient's face and life are captured by the camera 112 as it is, regardless of the patient's intention.

In order to overcome this problem, Korean Patent Registration No. 10-2052883 (Title of Invention: Fall Prediction System Using a Thermal Imaging Camera and Fall Prediction Method Using a Thermal Imaging Camera) discloses a step of photographing a person with a thermal imaging camera. ; Analyzing each pixel data constituting the captured image, obtaining x-axis histogram and y-axis histogram data of the captured image based on the pixel data, and based on the x-axis histogram and y-axis histogram data , determining whether the person is in a stable state or a fall risk state, and operating a notification device based on the person's condition, thereby protecting the patient's privacy and predicting the patient's fall. A fall prediction method using a thermal imaging camera has been disclosed, but the installation cost is very high because an expensive and high-powered thermal imaging camera must be installed to photograph each bedroom. Since the thermal image obtained by the thermal image is analyzed in real time to determine the state of a person, the calculation processing amount is high, and when there are a large number of objects to be monitored, a problem occurs that the calculation processing speed is delayed.

The present invention is intended to solve this problem, and an object of the present invention is configured to simultaneously determine the abnormal body temperature and the occurrence of a fall accident of a patient in each bedroom using a heat-sensing sensor, thereby enabling a separate camera and a thermal imaging camera. It is intended to provide a ward emergency alarm and monitoring system that can not only improve personal privacy issues, but also reduce installation costs and enable low-power operation.

In addition, another problem of the present invention is that the local management server analyzes the heat-sensing information received from the data collection devices to recognize the object, and then uses the temperature values and location information of the recognized object to determine the patient's ideal body temperature and It is intended to provide a ward emergency situation warning and monitoring system that is configured to determine whether or not a fall accident has occurred, thereby increasing the accuracy of object recognition while simplifying calculation processing.

In addition, another problem of the present invention is that when the local management server determines that the patient's abnormal body temperature and a fall accident have occurred, it transmits the unexpected situation data including the contents of the unexpected situation and the identification number of the ward and room to the administrator's terminal. When the terminal's management program receives emergency situation data from the local management server, it outputs warning alarm information to the outside, enabling remote monitoring of the patient's condition, improving work efficiency and maximizing the efficiency of the nursing management system. It is intended to provide a ward emergency warning and monitoring system that can increase the safety and reliability of medical services by enabling rapid follow-up in the event of an unexpected situation.

The solution of the present invention for solving the above problems is installed on the upper part of each bedroom of at least one or more wards to measure the temperature of a preset detection area (S) - detection sensors; a data collection device installed in the ward to collect heat-sensing information, which is the temperature value of the sensing area S measured by the heat-sensing sensors of the ward; By analyzing the heat-sensing information transmitted from the data collection device, it is determined whether the patient has an abnormal body temperature and a fall accident in each bedroom. and a local management server for transmitting unexpected situation data including room identification information to the outside. a manager terminal installed with a management program for outputting the received unexpected situation data to the outside when receiving emergency situation data from the local management server; and a ward thermometer installed in the ward to measure the temperature of the ward and then transmit the ward temperature value to the data collection device at a predetermined period (T), wherein the heat-sensing sensors measure the temperature when the temperature is measured. The area (S) is subdivided into a plurality of cells, the temperature is measured, and heat-sensing information, which is the temperature value for each cell, is detected. When the local management server receives the heat-sensing information transmitted from the data collection device, the input After extracting the temperature value for each cell from the heat-sensing information, comparing the extracted temperature value for each cell with the preset body temperature range, recognizing consecutive cells included in the body temperature range as patient objects, and detecting cell information of the patient object patient object recognition unit; After extracting the temperature values for each cell of the patient object by utilizing the heat-sensing information transmitted from the data collection device and the cell information of the patient object input from the patient object recognizing unit, the temperature value of each cell of the extracted patient object is extracted. Calculate the patient's body temperature, which is the average value of the patient's body temperature, and compare the calculated patient's body temperature with the preset normal body temperature range. an abnormal body temperature determination unit for determining that an abnormal body temperature has occurred in the patient when the body temperature is out of the normal range; an unexpected situation data generation unit determining that an unexpected situation has occurred and generating unexpected situation data when the patient's body temperature is out of the normal body temperature range by the abnormal body temperature determining unit; a control unit for transmitting the emergency situation data generated by the emergency situation data generation unit to the manager terminals; a database unit storing a reference table, which is data in which optimal constants (TH) are matched for each range of temperature values of the ward; An optimal constant setting unit which is executed every predetermined period (T), wherein the optimal constant setting unit includes a ward temperature value input module that receives the temperature value of each ward of the heat-sensing information transmitted from the data collection device; a reference table extraction module for extracting a reference table stored in the database unit; After searching the reference table extracted by the reference table extraction module and extracting an optimal constant (TH) matching the ward temperature value input by the ward temperature value input module, the extracted optimal constant (TH) is stored in the database. When the patient object recognition unit receives heat-sensing information transmitted from the data collection device, after extracting the temperature value for each cell from the input heat-sensing information, the database unit After calculating the optimal temperature value for each cell by multiplying the optimal constant (TH) stored in the extracted temperature value for each cell, the calculated optimal temperature value for each cell is compared with the preset body temperature range to determine successive cells included in the body temperature range It is recognized as a patient object, and the optimal constant (TH) stored in the reference table has a smaller value as the temperature value of the corresponding ward increases and a higher value as the temperature value of the ward decreases. a heat-sensing information input module receiving the heat-sensing information transmitted from the data collection device; After extracting the temperature value for each cell from the input heat-sensing information, calculating the optimum temperature value for each cell by multiplying the optimal constant (TH) stored in the database unit with the extracted temperature value for each cell, and then calculating the optimal temperature for each cell an object recognition module that compares the value with a preset body temperature range, recognizes consecutive cells included in the body temperature range as patient objects, and detects cell information of the patient object; an object location information extraction/storage module for detecting cell location information of the patient object recognized by the object recognition module and then storing the detected cell location information of the patient object in the database unit; A facial region detection module comprising: an object location information input module for receiving cell-location information of a patient object detected by the object location information extraction/storage module; an accumulated data extraction module for extracting cell-location information of the corresponding patient object stored in the database unit, and extracting data from a current point in time to a preset previous time (t); a longitudinal direction determining module for determining a longitudinal direction of the patient by analyzing the cell-location information of the patient object input through the object location information input module; a candidate regions determining module for determining regions adjacent to both ends in the longitudinal direction of the patient object determined in the longitudinal direction determining module as candidate regions; a width calculation module for each candidate region that calculates the size (cell size) of the width of each candidate region determined by the candidate region determination module; The size of the width of each candidate region calculated by the width calculation module of each candidate region is compared with a second reference value, which is the maximum value of the size of the width for determining that the candidate region is a facial region, and 1) the candidate regions When one of the widths is less than the second reference value and the other is greater than or equal to the second reference value, a candidate region having a width less than the second reference value is determined as a facial region, and 2) the candidate regions a comparison and determination module for re-executing the heat-sensing information input module, the object recognition module, and the object location information extraction/storage module when the sizes of all widths are equal to or greater than a second reference value; The comparison and determination module is executed when the width of all of the candidate regions is less than the second reference value, and by using the data extracted by the cumulative data extraction module, in the direction of the previous point of view based on the current point of view, the cell of the patient object After analyzing the location information, detecting each candidate region, and detecting the width size of each candidate region, using the detected width values of each candidate region, the change rate of width size of each candidate region during the previous time ( %) for each candidate region tracking module based on cumulative data; A width variation comparison and determination module that compares the width change rate (%) of each candidate region calculated by each candidate region tracking module based on the cumulative data and determines that a candidate region having a low width size change rate (%) is a facial region. ; 1) a facial region determination module that finally determines the determined facial region as the facial region of the corresponding patient when the facial region is determined in the comparison and determination module or 2) the width variation comparison and determination module; Using the optimal temperature values of each cell detected by the patient object recognition unit, determining the patient's body temperature to determine the average value of the optimal temperature values of the cells corresponding to the facial region determined by the facial region determination module as the body temperature of the corresponding patient module, wherein the abnormal body temperature determining unit determines whether the patient has an abnormal body temperature by using the body temperature determined by the patient temperature determining module.

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In the present invention, the facial region detection module further includes a verification module executed after the longitudinal direction determination module, and the verification module includes the cell-location information of the patient object detected by the object location information extraction/storage module and , After calculating the length (L, cell-size) of the patient object using the longitudinal direction of the patient object determined by the longitudinal direction determination module, the calculated length (L) of the patient object 1) If the length (L) of the patient object is less than the reference value, it is determined that there was an error in determining the length direction of the patient, 2) The length of the patient object ( If L) is equal to or greater than the reference value, a first verification module for determining that there is no error in determining the longitudinal direction of the patient; Compare whether the longitudinal direction of the patient object determined by the longitudinal direction determination module has many Y-axis components or X-axis components, which are the longitudinal direction of the bed, 1) if the X-axis component is in the longitudinal direction of the patient object If it is large, it is determined that an error has occurred in determining the longitudinal direction, and 2) if it is determined that there are many Y-axis components in the longitudinal direction of the patient object, a second verification module for determining that there is no error in determining the longitudinal direction; 1) If it is determined that no error has occurred in both the first verification module and the second verification module, it is verified that there is no error in determining the longitudinal direction of the patient object in the longitudinal direction determination module, and 2) the first verification module and and an error determination module for finally determining that an error has occurred in determining the longitudinal direction of the patient object in the longitudinal direction determination module, when it is determined that an error has occurred in at least one of the second verification modules, and The determination module is executed when the verification module verifies that there is no error in determining the longitudinal direction of the patient object of the longitudinal direction determination module, and the heat-sensing information input module determines the length direction of the patient object of the longitudinal direction determination module in the verification module. It is desirable to re-execute when it is determined that an error has occurred in determining the longitudinal direction.

In addition, in the present invention, bedroom area information, which is location information of cells corresponding to bedrooms in the sensing area S of each of the heat-detecting sensors, is preset and stored in the database unit, and the local management server includes a fall determination unit. and a data input module for receiving cell information of the patient object from the patient object recognition unit; a bedroom area information extraction module for searching the database unit and extracting bedroom area information of a corresponding heat-sensing sensor stored in the database unit; a location comparison module for comparing cell locations of the patient object and bedroom cells by utilizing the patient object cell information input through the data input module and the bedroom area information extracted by the bedroom area information extraction module; and a fall determination module for determining that a fall accident has occurred in the patient when an elapsed time in which the cell position of the patient object deviates from the cell position in the bedroom in the position comparison module is greater than or equal to a threshold value, When it is determined by the decision module that a fall accident has occurred in the patient, it is preferable to generate unexpected situation data.

In addition, in the present invention, the management program provides a GUI displaying a ward display box corresponding to the ward and a bedroom display box corresponding to each of the bedrooms arranged in the ward, and the GUI is for patients who do not have an unexpected situation. The bedroom display boxes corresponding to the above are displayed in a first color, the bedroom display boxes corresponding to patients with abnormal body temperature are displayed in a second color, and the bedroom display boxes corresponding to patients with a fall accident are displayed in a third color. It is preferable to display in color.

According to the present invention having the above problems and solutions, it is configured to simultaneously determine the abnormal body temperature of a patient in each bedroom and whether or not a fall accident occurs using a heat-sensing sensor, so that a separate camera and thermal imaging camera are not used, and personal privacy Not only can the infringement problem be improved, but the installation cost is reduced and low-power operation is possible.

In addition, according to the present invention, after the local management server recognizes an object by analyzing the heat-sensing information received from the data collection devices, the patient's abnormal body temperature and fall accident occur using the temperature values and location information of the recognized object. By being configured to determine whether or not, the calculation process is simple and the accuracy of object recognition can be increased.

In addition, according to the present invention, when the local management server determines that the patient's abnormal body temperature and a fall accident have occurred, it transmits the unexpected situation data including the contents of the unexpected situation and the ward and room identification numbers to the administrator terminal, and the management program of the administrator terminal When emergency situation data is received from this local management server, it is possible to remotely monitor the patient's condition by outputting warning alarm information to the outside, thereby improving work efficiency and maximizing the efficiency of the nursing management system at the same time. In the event of an occurrence, prompt follow-up is possible, increasing the safety and reliability of medical services.

1 is a block diagram showing a patient fall prevention monitoring device disclosed in Korean Patent Registration No. 10-1712191 (Title of Invention: Patient Fall Prevention Monitoring Device).
2 is a block diagram showing a system for warning and monitoring an emergency situation in a ward, which is an embodiment of the present invention.
3 is an exemplary view of FIG. 2 .
FIG. 4 is a plan view illustrating the data collection device and heat-detecting sensors of FIG. 2 .
FIG. 5 is a side view illustrating the heat-sensing sensor of FIG. 4 .
6 is an exemplary view of a GUI displayed by the management program of FIG. 2 .
FIG. 7 is a block diagram illustrating a local management server of FIG. 2 .
8 is a block diagram illustrating a patient object recognition unit of FIG. 7 .
FIG. 9 is a block diagram illustrating a facial area detection module of FIG. 8 .
10 is a block diagram illustrating a verification module of FIG. 9 .
11 is a block diagram illustrating a fall determination unit of FIG. 7 .
12 is a block diagram illustrating an optimum constant setting unit of FIG. 7 .

Hereinafter, an embodiment of the present invention will be described with reference to the accompanying drawings.

FIG. 2 is a configuration diagram showing an emergency situation warning and monitoring system in a hospital ward according to an embodiment of the present invention, and FIG. 3 is an exemplary view of FIG. 2 .

A ward emergency alarm and monitoring system 1, which is an embodiment of the present invention, detects and monitors the body temperature and fall of each patient in each ward using a heat-sensing sensor 9, and at the same time detects an unexpected situation, By outputting this to the outside through the output means, the patient's condition can be remotely monitored to increase management efficiency and convenience, as well as to reduce unnecessary manpower consumption, and to respond quickly to unexpected situations, improving the safety and reliability of medical services. can be increased, and low-cost installation and operation are possible.

In addition, as shown in FIGS. 2 and 3, the ward emergency situation alarm and monitoring system 1 is installed on the ceiling directly above each of the bedrooms of each ward and measures the temperature of the detection area S directly below the heat- Sensors (9), and data collection devices (4) installed in each ward to collect the temperature values measured by the heat-detection sensors (9) of the corresponding ward, and from the data collection devices (4) A local management server (3) that analyzes the received collected information (temperature value information) to determine the patient's fever status and fall status in each bedroom, and manager terminals (5), which are terminals used or possessed by a manager (nurse) And, it is an application installed in the manager terminals (5), receives the fever status and fall status of each patient transmitted from the local management server (3), visualizes them, displays them on the monitor of the manager terminal (5), and displays them locally When the unexpected situation occurrence data is received from the management server 3, the management program 7 that displays warning alarm information through the manager terminal 5, the local management server 3, the data collection devices 4, and the manager It consists of a communication network 10 providing a data movement path between the terminals 5.

At this time, in the present invention, the data collection devices 4 simply transmit the heat-sensing information measured by the heat-detecting sensors 9 of the corresponding ward 200 to the local management server 3, and the local management server 3 ) analyzes the heat-sensing information received from the data collection devices 4 and performs calculation processing to determine the patient's abnormal body temperature and whether or not to fall. The arithmetic processing for determining can be configured to be performed directly in the data collection device 4.

The communication network 10 supports data communication between the local management server 3, data collection devices 4 and manager terminals 5, and in detail, a wired communication network, a wide area communication network (WAN), a mobile communication network, etc. may consist of

FIG. 4 is a plan view illustrating the data collection device and heat-detecting sensors of FIG. 2 , and FIG. 5 is an exemplary side view illustrating the heat-detecting sensor of FIG. 4 .

As shown in FIGS. 4 and 5, the heat-detection sensors 9 are installed on the ceiling 220 directly above each bedroom 210 disposed in the corresponding ward 200, and the lower detection area S Measure the surface temperature. That is, the heat-detection sensors 9 measure the surface temperature of the preset detection area S.

In addition, the heat-detecting sensors 9 can measure the sensing area S by subdividing it into a plurality of cells S'. At this time, the temperature value for each cell S' generated by the heat-detecting sensor 9 is referred to as heat-sensing information.

In addition, it is preset that the sensing area S is subdivided into a plurality of cells by the thermal sensors 9 .

In addition, the heat-sensing sensors 9 transmit the generated heat-sensing information to the data collection device 4 of the corresponding ward 200 in real time.

The ward thermometer 11 measures the temperature of the corresponding ward 200 and transmits the measured temperature value of the ward to the data collection device 4 of the corresponding ward 200 at predetermined intervals T.

The data collection device 4 is installed in each ward 200 to collect heat-sensing information from the heat-sensing sensors 9 of the corresponding ward 200 and at the same time, ward thermometers 11 at predetermined intervals T. ), collects ward temperature values, and transmits collected data consisting of the collected heat-sensing information or collected data matching the collected heat-sensing information and ward temperature values to the local management server 3.

At this time, the collected data generated by the data collection device 4 is composed of identification information of the corresponding ward, identification information of each heat-sensing sensor, heat-sensing information, and ward temperature value.

Manager terminals 5 are digital devices possessed by managers such as nurses or installed at positions such as desks where managers are placed, and in detail, may be composed of desktop-PCs, laptops, tablet PCs, smartphones, etc. .

In addition, a management program 7 is installed in the manager terminals 5 .

6 is an exemplary view of a GUI displayed by the management program of FIG. 2 .

The management program 7 is software, application, or application program installed in the manager terminal 5 and providing an emergency alarm and monitoring service in a ward in conjunction with the local management server 3.

In addition, the management program 7 visualizes the unexpected situation data received from the local management server 3 through prepared GUIs (Graphic User Interfaces) and displays them on the monitor of the manager terminal 5. At this time, the unexpected situation data consists of the contents of the unexpected situation (abnormal body temperature or fall), identification information of the ward where the unexpected situation occurred, and identification information of the bedroom where the unexpected situation occurred.

In addition, as shown in FIG. 6, the GUI 910 provided by the management program 7 is arranged in the ward display area 9111 displayed corresponding to the location of the actual ward 200 and the corresponding ward 200. State display units 911 in which bedroom display boxes 9112 corresponding to each of the bedrooms 210 are graphically displayed and displayed, and unexpected situation display boxes 912 displaying identification numbers of wards and bedrooms in which an unexpected situation has occurred made up of

In addition, the GUI 910 displays 1) bedroom display boxes 9112 in which abnormal body temperature and unexpected falls do not occur in the first color, and 2) true room display boxes 9112 in which abnormal body temperature occurs in a second color. 3) By displaying the bedroom indicator box (9112) where a fall accident occurred in a third color, the administrator can intuitively recognize whether an unexpected situation has occurred and the location of the ward and bedroom where the unexpected situation occurred quickly and accurately. This enables rapid response to emergencies.

FIG. 7 is a block diagram illustrating a local management server of FIG. 2 .

As shown in FIG. 7, the local management server 3 includes a control unit 31, a database unit 32, a data transmission/reception unit 33, a patient object recognition unit 34, and an abnormal body temperature determination unit 35. , a fall determination unit 36, an unexpected situation data generation unit 37, and an optimal constant setting unit 38.

The control unit 31 is the O.S (Operating System) of the local management server 3, and manages the control objects 32, 33, 34, 35, 36, 37, and 38. and control.

In addition, when the controller 31 receives collected data from the data collection devices 4 through the data transceiver 33, the controller 31 controls the data transceiver 33 to transmit the received collected data to the manager terminal 5. While controlling the data transmission/reception unit 33, the received collected data is stored in the database unit 32, heat-sensing information of the collected data is input to the patient object recognition unit 34, and the ward temperature value is included in the collected data. If is included, the ward temperature value is input to the optimal constant setting unit 38.

In addition, the control unit 31 generates unexpected situation data when it is determined that an abnormal body temperature has occurred in the patient by the abnormal body temperature determination unit 35 or when it is determined that a fall accident has occurred in the patient by the fall determination unit 36 Section 37 is executed.

In addition, when the unexpected situation data is generated by the unexpected situation data generator 37, the control unit 31 controls the data transmission/reception unit 33 to transmit the generated unexpected situation data to the manager terminals 5.

In the database unit 32, the location and identification information of each ward 200 and the location and identification information of the bedroom 210, which is the detection area S of each of the heat-detecting sensors 9 of each ward 200, are stored. It is set and saved.

In addition, in the database unit 32, the detection area S of each heat-detecting sensor 9 and the location information of the cells subdivided in the corresponding sensing area S are matched with the sensing area information and the bedroom in the sensing area S. Bedroom area information, which is location information of cells corresponding to 210, is preset and stored.

Also, collected data transmitted from the data collection devices 4 are stored in the database unit 32 .

In addition, the database unit 32 stores the unexpected situation data generated by the unexpected situation data generating unit 37 .

In addition, the optimal constant (TH, Threshold) set by the optimal constant setting unit 38 is stored in the database unit 32 .

In addition, the database unit 32 stores a reference table, which is data in which optimal constants (TH) are matched for each range of ward temperature values. At this time, the optimal constant (TH) for each range of ward temperature values in the reference table can be determined through a number of repeated experiments, and the optimal constant (TH) decreases as the ward temperature value increases and increases as the ward temperature value decreases. have

For example, when the ward temperature value is high, the temperature value of each cell detected by the thermal sensor 9 increases as a whole, making it difficult to accurately measure the patient's body temperature.

The data transmission/reception unit 33 transmits/receives data with the data collection devices 4 and the manager terminals 5.

The program management unit 34 manages and controls updates and GUI changes of the management program 7 .

8 is a block diagram illustrating a patient object recognition unit of FIG. 7 .

As shown in FIG. 8, the patient object recognition unit 34 includes a heat-sensing information input module 341, an object recognition module 343, an object location information extraction/storage module 345, a facial region detection module ( 347).

The heat-sensing information input module 341 receives the heat-sensing information transmitted from the data collection device 4 under the control of the control unit 31 .

The object recognition module 343 first extracts the temperature value for each cell from the input heat-sensing information, and then sets the optimum constant TH by the optimum constant setting unit 38 of FIG. 10 and stores it in the database unit 32. , Threshold) is multiplied by the temperature value for each cell to calculate the optimal temperature value for each cell.

In general, since the heat-sensing sensor 9 is installed on the ceiling of a ward and senses heat at a predetermined distance from an object, it is affected by the temperature of the ward. For example, when the temperature of the corresponding ward is low, the temperature value of the cells in the detection area S measured by the heat-detection sensor 9 is lowered as a whole, and when the temperature of the corresponding ward is high, heat-sensing The temperature values of the cells measured by the sensor 9 increase as a whole.

Accordingly, according to the present invention, the optimal constant setting unit 38 of FIG. 10, which will be described later, periodically sets an optimal constant (TH) capable of correcting the error of the temperature value for each cell in consideration of the temperature value of the ward, and the object recognition module ( 343) can increase the accuracy and reliability of object recognition by correcting the error of the temperature value of each cell due to the temperature of the ward by applying the set optimal constant (TH) when recognizing the patient object using the temperature value of each cell.

In addition, the object recognition module 343 compares the calculated optimal temperature value for each cell with a preset body temperature range, recognizes consecutive cells included in the body temperature range as a patient object, and detects cell information of the patient object.

The object location information extraction/storage module 345 detects the cell-location information of the patient object recognized by the object recognition module 343 and stores it in the database unit 32 .

FIG. 9 is a block diagram illustrating a facial area detection module of FIG. 8 .

As shown in FIG. 9, the facial region detection module 347 includes an object location information input module 3471, an accumulated data extraction module 3472, a longitudinal direction determination module 3473, a verification module 3474, and a candidate region. field determination module 3475, width calculation module 3476 of each candidate region, comparison and determination module 3477, each candidate region tracking module 3478 based on accumulated data, width variation comparison and determination module 3479, face It consists of an area determination module 3480 and a patient body temperature determination module 3481.

The object location information input module 3471 receives the cell-location information of the patient object detected by the object location information extraction/storage module 345.

The cumulative data extraction module 3472 extracts cell-location information of a corresponding patient object stored in the database unit 31 .

At this time, the accumulated data extraction module 3472 extracts data from the current point in time to a preset previous time (t).

The longitudinal direction determining module 3473 analyzes the cell-location information of the patient object input through the object location information input module 3471 to determine the longitudinal direction of the patient.

In general, since the length of a human body is significantly larger than the width, the longitudinal direction determination module 3473 can determine the width direction and length direction of the patient object by analyzing the cell-location information of the patient object.

10 is a block diagram illustrating a verification module of FIG. 9 .

The verification module 3474 of FIG. 10 verifies whether or not there is an error in the longitudinal direction of the patient determined by the longitudinal direction determination module 3473.

Also, as shown in FIG. 10 , the verification module 3474 includes a first verification module 34741 , a second verification module 34742 , and an error determination module 34743 .

The first verification module 34741 calculates the length (L, cell-size) of the patient object by utilizing the cell-location information of the patient object and the longitudinal direction of the patient object determined by the longitudinal direction determination module 3473. Afterwards, the calculated length (L) of the patient object is compared with a preset reference value.

At this time, the reference value is defined as the minimum value of the length of an object that can be determined that the recognized object is a body.

In addition, the first verification module 34741 determines that 1) if the length (L) of the patient object is less than the reference value, there is an error in determining the longitudinal direction of the patient, and 2) if the length (L) of the patient object is greater than the reference value, the patient It is judged that there was no error in determining the longitudinal direction of

When the longitudinal direction of the bed is assumed to be the Y-axis, the second verification module 34742 determines whether the longitudinal direction of the patient object determined by the longitudinal direction determination module 3473 has many Y-axis components or X-axis components (in the Y-axis). close or close to the X-axis).

In addition, the second verification module 34742 determines that 1) if there are many X-axis components in the longitudinal direction of the patient object, an error has occurred in determining the longitudinal direction, and 2) if there are many Y-axis components in the longitudinal direction of the patient object. If it is determined, it is determined that there is no error in the determination of the longitudinal direction.

The error occurrence determination module 34743 1) determines the longitudinal direction of the patient object in the longitudinal direction determining module 3473 when it is determined that no errors have occurred in both the first verification module 34741 and the second verification module 34742. 2) If it is determined that an error has occurred in at least one of the first verification module 34741 and the second verification module 34742, the longitudinal direction determination module 3473 determines the longitudinal direction of the patient object. It is finally determined that an error has occurred in

At this time, the control unit 30 executes the candidate region determination module 3475 when 1) it is finally verified in the verification module 3474 that no error has occurred, and 2) when it is finally determined that an error has occurred, The heat detection information input module 341, the object recognition module 343, and the object location information extraction/storage module 345 are re-executed.

The candidate region determination module 3475 is executed when the verification module 3474 verifies that there is no error in determining the longitudinal direction of the patient object by the longitudinal direction determination module 3473.

Also, the candidate regions determination module 3475 determines regions adjacent to both ends of the patient object in the longitudinal direction as candidate regions.

For example, since the regions adjacent to both ends of the human body in the height direction consist of the outside of the face and the legs, the candidate regions determined by the candidate regions determination module 3475 consist of the face region and the leg region.

The width calculation module 3477 of each candidate region calculates the size of the width (cell size) of each candidate region determined by the candidate region determination module 3475.

The comparison and determination module 3477 compares the size of the width of each candidate region calculated by the width calculation module 3477 of each candidate region with a preset second reference value. At this time, the second reference value is defined as the maximum value of the width at which the recognized candidate region can be determined as the facial region.

Further, the comparison and determination module 3477 may perform a candidate having a width less than the second reference value when the width of one of the candidate regions is less than the second reference value and the width of the other candidate region is greater than or equal to the second reference value. The area is determined as a facial area.

In addition, the comparison and determination module 3477 executes each candidate region tracking module 3478 based on accumulated data when the widths of all candidate regions are smaller than the second reference value.

In addition, the comparison and determination module 3477 extracts/stores the heat detection information input module 341, object recognition module 343, and object location information of FIG. Rerun module 345.

For example, when the patient is lying down, the patient's leg area is generally covered with a blanket, and accordingly, when the patient is in a state where the blanket is covered with both legs spread, the width of the leg area is the second Although detection is greater than the reference value, a human facial region has a characteristic of not being detected as exceeding a predetermined size. Therefore, in the present invention, the comparison and determination module 3477 is used to detect the facial region among candidate regions.

As another example, when the patient's legs are overlapped, since the width of the leg region is detected to be less than the second reference value, after the comparison and determination module 3477, each candidate region tracking module 3478 based on accumulated data is executed. .

Each candidate region tracking module 3478 based on accumulated data is executed when the width of all candidate regions in the comparison and determination module 3477 is less than the second reference value.

In addition, each candidate region tracking module 3478 based on accumulated data utilizes the data extracted by the cumulative data extraction module 3472 to analyze the cell-location information of the patient object in the direction of the previous point of view based on the current point of view. , After detecting each candidate region, the size of the width of each candidate region is detected.

In addition, the cumulative data-based tracking module 3478 of each candidate region utilizes the detected width values of each candidate region to calculate a width change rate (%) of each candidate region.

The width variation comparison and determination module 3479 compares the width variation rate (%) of each candidate area calculated by each candidate area tracking module based on the cumulative data, and selects a candidate area with a low width size variation rate (%) as a facial area. decide that

For example, the face region has a small change rate (%) of width size even when the posture is changed, but the leg region has a high change rate (%) compared to the face region even when the posture is not changed.

When the facial region is determined in 1) the comparison and determination module 3477 or 2) the width variation comparison and determination module 3479, the facial region determination module 3480 finally determines the determined facial region as the facial region of the corresponding patient. .

The patient body temperature determination module 3481 determines the average value of the optimum temperature values of the cells corresponding to the facial region determined by the facial region determination module 3480 as the patient's body temperature.

The patient object recognition unit 34 configured as described above inputs the patient's body temperature information recognized by the object recognition module 343 to the abnormal body temperature determination unit 35, and the cell information of the patient object to the fall determination unit ( 36).

The abnormal body temperature determining unit 35 compares the patient's body temperature detected by the patient's body temperature determination module 3481 with a predetermined normal body temperature range, and 1) determines that the patient's body temperature is normal when the patient's body temperature is within the normal body temperature range. 2) When the patient's body temperature is out of the normal body temperature range, it is determined that abnormal body temperature has occurred in the patient.

At this time, the control unit 31 executes the sudden situation data generation unit 37 when it is determined by the abnormal body temperature determination unit 35 that abnormal body temperature has occurred in the patient.

11 is a block diagram illustrating a fall determination unit of FIG. 7 .

As shown in FIG. 11, the fall determination unit 36 includes a data input module 361, a bedroom area information extraction module 362, a location comparison module 363, and a fall determination module 364.

The data input module 361 receives cell information of the patient object from the patient object recognition unit 34 and receives heat-sensing information of a corresponding heat-sensing sensor.

The bedroom area information extraction module 362 searches the database unit 32 and extracts the bedroom area information of the corresponding thermal sensor 9 stored in the database unit 32 .

At this time, the bedroom area information is location information of cells corresponding to the bedroom within the corresponding detection area S, and is preset and stored in the database unit 32.

The location comparison module 363 utilizes the patient object cell information input through the data input module 361 and the bedroom area information extracted by the bedroom area information extraction module 362 to determine the location of the cell of the patient object and the bedroom. Compare cell positions.

The fall determination module 364 determines that a fall accident has occurred in the patient when the elapsed time in which the cell position of the patient object deviates from the cell position of the bedroom in the position comparison module 363 is greater than or equal to a threshold value.

Returning to FIG. 7 and examining the unexpected situation data generator 37, the abnormal situation data generator 37 determines the abnormal body temperature of the patient by the abnormal body temperature determination unit 35 or falls It is executed when the patient's fall is determined by and generates unexpected situation data. At this time, the unexpected situation data consists of the contents of the unexpected situation (abnormal body temperature, fall accident), the identification number of the corresponding ward, and the identification number of the corresponding bedroom.

In addition, the unexpected situation data generated by the unexpected situation data generator 37 is transmitted to the manager terminals 5 through the data transmission/reception unit 33 under the control of the control unit 31, and management of the manager terminals 5 When the program 7 receives emergency situation data from the local management server 3, it outputs sound warning information to the outside so that the administrator can quickly deal with it.

12 is a block diagram illustrating an optimum constant setting unit of FIG. 7 .

The optimum constant setting unit 38 of FIG. 12 is executed according to the control of the control unit 31 at each preset period T.

As shown in FIG. 12, the optimal constant setting unit 38 includes a ward temperature value input module 381, a reference table extraction module 382, and an optimal constant determining module 383.

The ward temperature value input module 381 receives the temperature value of each ward of the heat-sensing information transmitted from the data collection device 4 .

The reference table extraction module 382 extracts the reference table stored in the database unit 32 .

The optimal constant setting module 383 searches the reference table extracted by the reference table extraction module 382, and selects optimal constants TH matched with each ward temperature value input by the ward temperature value input module 381. extract each.

In addition, the optimal constant setting module 383 stores the extracted optimal constants (TH) of each ward in the database unit 32 .

As described above, the ward emergency alarm and monitoring system 1, which is an embodiment of the present invention, is configured to simultaneously determine the abnormal body temperature and the occurrence of a fall accident of patients in each bedroom using the heat-detection sensor 9, so that a separate camera and thermal imaging cameras are not used, which not only improves privacy issues but also reduces installation costs and enables low-power operation.

In addition, in the ward emergency alarm and monitoring system 1 of the present invention, after the local management server 3 analyzes the heat-sensing information received from the data collection devices 4 to recognize an object, the temperature of the recognized object By using the values and location information, it is configured to determine the abnormal body temperature of the patient and whether or not a fall accident has occurred, thereby increasing the accuracy of object recognition while simplifying calculation processing.

In addition, in the ward emergency alarm and monitoring system 1 of the present invention, when the local management server 3 determines that a patient has an abnormal body temperature and a fall accident, the manager terminal 5 identifies the contents of the unexpected situation, the ward, and the guest room. Emergency situation data including a number is transmitted, and when the management program 7 of the manager terminal 5 receives the unexpected situation data from the local management server 3, warning alarm information is output to the outside so as to determine the patient's condition. Remote monitoring is possible to improve work efficiency and maximize the efficiency of the nursing management system, and in the event of an unexpected situation, prompt follow-up is possible, increasing the safety and reliability of medical services.

1: Ward emergency alarm and monitoring system
3: local management server 4: data collection device
5: manager terminal 7: management program
9: heat-detection sensor 10: communication network
11: ward thermometer 31: control unit
32: database unit 33: data transmission and reception unit
34: patient object recognition unit 35: abnormal body temperature determination unit
36: Fall determination unit 37: Unexpected situation data generation unit
38: optimal constant setting unit 341: heat-sensing information input module
343: object recognition module 361: data input module
362: Bedroom area information extraction module 363: Location comparison module
364: fall determination module 381: ward temperature value input module
382: Reference table extraction module 383: Optimal constant determination module

Claims (6)

Heat-detection sensors installed on the upper part of each bedroom of at least one or more wards to measure the temperature of a preset detection area (S);
a data collection device installed in the ward to collect heat-sensing information, which is the temperature value of the sensing area S measured by the heat-sensing sensors of the ward;
By analyzing the heat-sensing information transmitted from the data collection device, it is determined whether the patient has an abnormal body temperature and a fall accident in each bedroom. and a local management server for transmitting unexpected situation data including room identification information to the outside.
a manager terminal installed with a management program for outputting the received unexpected situation data to the outside when receiving emergency situation data from the local management server;
A ward thermometer installed in the ward to measure the temperature of the ward and then transmitting the ward temperature value to the data collection device at predetermined intervals (T);
When the heat-sensing sensors measure the temperature, the preset sensing area (S) is subdivided into a plurality of cells to measure the temperature and detect heat-sensing information, which is the temperature value for each cell,
The local management server
When the heat-sensing information transmitted from the data collection device is received, the temperature value for each cell is extracted from the input heat-sensing information, and then the extracted temperature value for each cell is compared with the preset body temperature range to be included in the body temperature range and continuously a patient object recognizing unit recognizing cells as patient objects and detecting cell information of the patient object;
After extracting the temperature values for each cell of the patient object by utilizing the heat-sensing information transmitted from the data collection device and the cell information of the patient object input from the patient object recognizing unit, the temperature value of each cell of the extracted patient object is extracted. Calculate the patient's body temperature, which is the average value of the patient's body temperature, and compare the calculated patient's body temperature with the preset normal body temperature range. an abnormal body temperature determination unit for determining that an abnormal body temperature has occurred in the patient when the body temperature is out of the normal range;
an unexpected situation data generation unit determining that an unexpected situation has occurred and generating unexpected situation data when the patient's body temperature is out of the normal body temperature range by the abnormal body temperature determining unit;
a control unit for transmitting the emergency situation data generated by the emergency situation data generation unit to the manager terminals;
a database unit storing a reference table, which is data in which optimal constants (TH) are matched for each range of temperature values of the ward;
It includes an optimal constant setting unit that is executed every predetermined period (T),
The optimal constant setting unit
a ward temperature value input module that receives the temperature value of each ward of the heat-sensing information transmitted from the data collection device;
a reference table extraction module for extracting a reference table stored in the database unit;
After searching the reference table extracted by the reference table extraction module and extracting an optimal constant (TH) matching the ward temperature value input by the ward temperature value input module, the extracted optimal constant (TH) is stored in the database. Including an optimal constant setting module stored in the unit,
The patient object recognition unit
When the heat-sensing information transmitted from the data collection device is received, the temperature value for each cell is extracted from the input heat-sensing information, and then the optimum constant (TH) stored in the database is multiplied by the extracted temperature value for each cell. After calculating the optimal temperature value for each cell, the calculated optimal temperature values for each cell are compared with the preset body temperature range, and consecutive cells included in the body temperature range are recognized as patient objects,
The optimal constant (TH) stored in the reference table has a smaller value as the temperature value of the corresponding ward increases, and a higher value as the temperature value of the ward decreases;
The patient object recognition unit
a heat-sensing information input module receiving the heat-sensing information transmitted from the data collection device;
After extracting the temperature value for each cell from the input heat-sensing information, calculating the optimum temperature value for each cell by multiplying the optimal constant (TH) stored in the database unit with the extracted temperature value for each cell, and then calculating the optimal temperature for each cell an object recognition module that compares the value with a preset body temperature range, recognizes consecutive cells included in the body temperature range as patient objects, and detects cell information of the patient object;
an object location information extraction/storage module for detecting cell location information of the patient object recognized by the object recognition module and then storing the detected cell location information of the patient object in the database unit;
Including a facial region detection module,
The facial region detection module
an object location information input module that receives cell-location information of the patient object detected by the object location information extraction/storage module;
an accumulated data extraction module for extracting cell-location information of the corresponding patient object stored in the database unit, and extracting data from a current point in time to a preset previous time (t);
a longitudinal direction determining module for determining a longitudinal direction of the patient by analyzing the cell-location information of the patient object input through the object location information input module;
a candidate regions determining module for determining regions adjacent to both ends in the longitudinal direction of the patient object determined in the longitudinal direction determining module as candidate regions;
a width calculation module for each candidate region that calculates the size (cell size) of the width of each candidate region determined by the candidate region determination module;
The size of the width of each candidate region calculated by the width calculation module of each candidate region is compared with a second reference value, which is the maximum value of the size of the width for determining that the candidate region is a facial region, and 1) the candidate regions When one of the widths is less than the second reference value and the other is greater than or equal to the second reference value, a candidate region having a width less than the second reference value is determined as a facial region, and 2) the candidate regions a comparison and determination module for re-executing the heat-sensing information input module, the object recognition module, and the object location information extraction/storage module when the sizes of all widths are equal to or greater than a second reference value;
The comparison and determination module is executed when the width of all of the candidate regions is less than the second reference value, and by using the data extracted by the cumulative data extraction module, in the direction of the previous point of view based on the current point of view, the cell of the patient object After analyzing the location information, detecting each candidate region, and detecting the width size of each candidate region, using the detected width values of each candidate region, the change rate of width size of each candidate region during the previous time ( %) for each candidate region tracking module based on cumulative data;
A width variation comparison and determination module that compares the width change rate (%) of each candidate region calculated by each candidate region tracking module based on the cumulative data and determines that a candidate region having a low width size change rate (%) is a facial region. ;
1) a facial region determination module that finally determines the determined facial region as the facial region of the corresponding patient when the facial region is determined in the comparison and determination module or 2) the width variation comparison and determination module;
Using the optimal temperature values of each cell detected by the patient object recognition unit, determining the patient's body temperature to determine the average value of the optimal temperature values of the cells corresponding to the facial region determined by the facial region determination module as the body temperature of the corresponding patient contains a module,
The abnormal body temperature determination unit
A ward emergency alarm and monitoring system, characterized in that for determining whether or not the abnormal body temperature is present using the body temperature determined by the patient body temperature determination module. delete delete The method of claim 1, wherein the facial region detection module
Further comprising a verification module executed after the longitudinal direction determination module,
The verification module
Using the cell-location information of the patient object detected by the object location information extraction/storage module and the longitudinal direction of the patient object determined by the longitudinal direction determination module, the length (L, cell-size) of the patient object is determined. After calculation, the calculated length (L) of the patient object is compared with the reference value defined as the minimum value of the length of the object that can determine that the recognized object is the body. 1) If the length (L) of the patient object is less than the reference value, , It is determined that there is an error in determining the longitudinal direction of the patient, and 2) a first verification module for determining that there is no error in determining the longitudinal direction of the patient if the length (L) of the patient object is greater than or equal to a reference value;
Compare whether the longitudinal direction of the patient object determined by the longitudinal direction determination module has many Y-axis components or X-axis components, which are the longitudinal direction of the bed, 1) if the X-axis component is in the longitudinal direction of the patient object If it is large, it is determined that an error has occurred in determining the longitudinal direction, and 2) if it is determined that there are many Y-axis components in the longitudinal direction of the patient object, a second verification module for determining that there is no error in determining the longitudinal direction;
1) If it is determined that no error has occurred in both the first verification module and the second verification module, it is verified that there is no error in determining the longitudinal direction of the patient object in the longitudinal direction determination module, and 2) the first verification module and When it is determined that an error has occurred in at least one of the second verification modules, an error determination module for finally determining that an error has occurred in determining the longitudinal direction of the patient object in the longitudinal direction determination module,
The candidate regions determining module
Executed when the verification module verifies that there is no error in determining the longitudinal direction of the patient object in the longitudinal direction determination module;
The heat-sensing information input module
Ward emergency situation warning and monitoring system, characterized in that the verification module is re-executed when it is determined that an error has occurred in determining the longitudinal direction of the patient object in the longitudinal direction determining module. The method of claim 4, wherein the database unit
Bedroom area information, which is location information of cells corresponding to a bedroom in the sensing area S of each of the heat-detecting sensors, is preset and stored,
The local management server further includes a fall determination unit,
The fall determination unit
a data input module receiving cell information of the patient object from the patient object recognition unit;
a bedroom area information extraction module for searching the database unit and extracting bedroom area information of a corresponding heat-sensing sensor stored in the database unit;
a location comparison module for comparing cell locations of the patient object and bedroom cells by utilizing the patient object cell information input through the data input module and the bedroom area information extracted by the bedroom area information extraction module;
a fall determination module for determining that a fall accident has occurred in the patient when an elapsed time in which the cell position of the patient object is deviated from the cell position of the bedroom in the position comparison module is greater than or equal to a threshold value;
The emergency situation warning and monitoring system of the ward, characterized in that, when it is determined by the fall determination module that a fall accident has occurred in the patient, the unexpected situation data generating unit generates unexpected situation data. The method of claim 5, wherein the management program
Provide a GUI displaying a ward display column corresponding to the ward and a bedroom display column corresponding to each of the bedrooms arranged in the ward;
The GUI is
The bedroom display boxes corresponding to patients without an unexpected situation are displayed in the first color, and the bedroom display boxes corresponding to patients with abnormal body temperature are displayed in the second color, and corresponding to patients with falls Ward emergency situation alarm and monitoring system, characterized in that the bedroom display column is displayed in a third color.