KR20210025177A - System for predicting falldown using dosage drug and the line of follow for patient - Google Patents

Abstract

The present invention provides a falling prediction system using a dosage drug and a moving line. The falling prediction system comprises: a terminal configured to be detachably attached to clothes of a falling prediction patient and storing identification information of the falling prediction patient; a plurality of gateways installed in a plurality of locations and transmitting the identification information of the falling prediction patient and their own location information stored therein from the terminal, respectively; a database storing dosage drug information of the falling prediction patient, dosage drug information of a plurality of test subjects, and falling information in accordance with a moving line; and a falling management server extracting the dosage drug information of a patient from the database based on the identification information of the falling prediction patient received from the plurality of gateways, extracting the moving line based on the received location information of the falling prediction patient, and extracting the dosage drug information and the falling information of the moving line of the plurality of test subjects matching the extracted dosage drug information and moving line from the database to predict a fall probability of the falling prediction patient.

Description

Fall prediction system using the patient's medication and movement line {SYSTEM FOR PREDICTING FALLDOWN USING DOSAGE DRUG AND THE LINE OF FOLLOW FOR PATIENT}

The present invention relates to a fall prediction system, and in particular, to a fall prediction system using drugs and movements of a patient to predict a fall possibility in advance using a drug taken by the patient and a movement line according to movement.

As the society enters an aging society, dementia patients and elderly patients are gradually increasing. Falls are considered one of the biggest accidents for these patients, and in fact, falls cause serious problems.

Recently, facilities that care for elderly patients have increased, and caregivers are taking care of patients, but as the number of patients increases, one-on-one care is difficult with a small number of caregivers, so it is not enough to prevent falls.

In particular, in the case of patients with dizziness due to drug use, severe injuries can occur only by a fall, so it is essential to prevent this or take quick measures after the death.

Conventionally, a number of fall detection technologies have been disclosed. However, in the prior art, most of the techniques for detecting that a fall has occurred after a fall has occurred, and a technique capable of predicting and preventing a fall in advance has hardly been suggested.

On the other hand, technologies for predicting falls using a plurality of sensors are also disclosed.

For example, Korean Patent Publication No. 10-1645617 discloses a fall prevention device that detects acceleration, angular velocity, distance to obstacles, foot height, and pressure by attaching various sensors to a patient's body to predict a patient's fall probability. .

However, this fall prevention device requires several sensors, which increases the cost, and because several sensors must be attached to the body, it is cumbersome and inconvenient to move.

In addition, as another example, in Korean Patent Publication No. 10-1883008, a fall sensing terminal worn by a user detects acceleration data according to the user's movement or movement, and generates acceleration characteristic values or event-related data therefrom. A fall prediction or detection device configured to calculate a fall risk rate by periodically receiving event-related data from a fall sensing terminal is disclosed.

However, in the apparatus of the prior literature, the fall sensing terminal has a problem in that the design is complicated and the cost is increased because the three-axis acceleration sensor, the acceleration feature value extraction unit, and the event classification unit must be provided.

On the other hand, in the prior art, including the prior literature, a technique for detecting a fall according to a drug taken by a patient and a movement line in which the patient moves is not disclosed.

In particular, in the case of confused or dizzy patients after taking the drug, the possibility of a fall is high depending on the drug and the movement, and therefore, there is a need to develop a technology for predicting a fall in advance.

Republic of Korea Patent Publication No. 10-1645617 Korean Registered Patent Publication No. 10-1883008

An object of the present invention is to provide a fall prediction system capable of predicting a patient's fall probability in advance by using information of a drug to be taken by a patient and a movement path according to the movement of the patient.

An object of the present invention is to provide a fall prediction system for predicting a patient's fall in advance by collecting location information from a terminal attached to a patient's clothes and analyzing a movement line according to movement from the collected location information.

An object of the present invention is to provide a fall prediction system that enables active patient management by calculating a patient's fall probability prediction result and transmitting a notification when a patient moves using the calculated fall probability prediction result.

A fall prediction system using drugs and movement lines of a patient according to an embodiment of the present invention comprises: a terminal configured to be detachable from clothes of a patient predicting a fall and storing identification information (ID) of the patient predicting a fall; A plurality of gateways installed at a plurality of locations and each transmitting identification information of the patient for predicting falls and their location information stored therein from the terminal; A database for storing the drug information of the patient predicting the fall, the drug information of the patient to be tested, and the fall information according to the movement path; Extracts the patient's medication information from the database based on the identification information of the fall prediction patient received from the plurality of gateways, extracts the movement line based on the received location information of the fall prediction patient, and the extracted And a fall management server for predicting a fall probability of the fall predicted patient by extracting from the database the fall information of the drug and the movement line of the plurality of subjects to be tested matching the drug information and the movement line.

In the present invention, the terminal, an ID storage unit for storing the identification information of the patient predicting the fall; And a transmitter for transmitting the identification information to the outside.

In the present invention, the gateway includes: a sensing unit that detects the terminal when the terminal approaches within a predetermined distance; A location information storage unit that stores its own location information; A control unit for controlling to transmit location information stored in the location information storage unit when the terminal is detected by the detection unit; And a transmission unit for transmitting the location information by the control unit.

In the present invention, the database comprises: a drug-drug information storage unit for storing drug-drug information for each identification information of a plurality of patients; It includes a fall information storage unit for storing the drug information to be administered for each patient and the fall information according to the movement line of the plurality of test subjects.

In the present invention, the fall management server, the communication unit for receiving the location information and identification information from the transmission unit; A movement line extracting unit for extracting the movement line of the patient based on the received location information; The plurality of subjects to be experimented with extracting drug information of the patient corresponding to the received identification information from the drug information storage unit, and mapping the extracted drug information and the extracted movement line from the fall information storage unit An information extracting unit for extracting information about each drug to be taken and fall information of the movement line; It includes a fall prediction unit for predicting the likelihood of a fall of the patient based on the extracted fall information.

In the present invention, the movement line extracting unit extracts the location coordinates from the received location information and arranges the extracted location coordinates in chronological order to form a movement line.

In the present invention, the position coordinates are two-dimensional or three-dimensional coordinates.

In the present invention, the two-dimensional or three-dimensional coordinates may be displayed in a two-dimensional or three-dimensional coordinate system, respectively.

In the present invention, the information extracting unit extracts the drug drug information identical to the extracted drug drug information from the drug drug information storage unit, and the plurality of experiments in which the similarity to the movement line extracted from the movement line extraction unit is greater than or equal to a preset reference value. The motion of the subject patient is extracted from the fall information extracting unit, and the extracted fall information of the plurality of subject patients is extracted.

In the present invention, the information extracting unit is determined that the similarity is greater than or equal to the reference value if the position coordinates of the movement lines of the plurality of subjects to be experimented are within a set distance between the position coordinates and the distance of the movement lines extracted from the movement line extraction unit. Judge.

In the present invention, the fall management server may visually display a result of predicting the fall possibility. At this time, in the predicted result of the fall possibility, the location information at which the fall of the patient is predicted is also displayed.

A fall prediction system using a patient's medication and movement according to an embodiment of the present invention has the following effects.

The present invention predicts a patient's fall probability in advance by utilizing the patient's medication and movement, so that the fall probability can be accurately predicted with a simple configuration and treatment.

In the present invention, the patient does not feel uncomfortable because the patient can automatically measure the location by simply attaching a small terminal to the clothes, and because the movement line according to the movement is analyzed from the collected location information of the patient, it is possible to determine the possibility of a fall of the patient. Accuracy can be improved.

The present invention has the effect that it is possible to manage the patient by calculating the patient's fall possibility information and transmitting a notification when the patient moves by using the calculated fall possibility information.

According to the present invention, since drug use is considered when determining a patient's likelihood of falling, the accuracy of predicting falls for patients with a high likelihood of falls may be improved.

1 is a block diagram of a fall prediction system using drugs and movements of a patient according to an embodiment of the present invention.
2 is a diagram illustrating an example in which a terminal provided in the fall prediction system is attached to a patient's clothes.
3 is a block diagram of a terminal included in the fall prediction system.
4 is a configuration diagram of a gateway of the fall prediction system.
5 is a configuration diagram of a database in the fall prediction system.
6 is a configuration diagram of a management server provided in the fall prediction system.
7 is an exemplary view of a moving line extracted from a moving line extracting unit according to an embodiment of the present invention.
8 is a view for explaining a similarity according to an embodiment of the present invention.
9 is an exemplary diagram illustrating a fall probability prediction result in the management server.

Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, and only these embodiments make the disclosure of the present invention complete, and are common knowledge in the technical field to which the present invention pertains. It is provided to completely inform the scope of the invention to those who have, and the invention is only defined by the scope of the claims. The same reference numerals refer to the same elements throughout the specification.

Hereinafter, the present invention will be described in detail with reference to the accompanying drawings.

FIG. 1 is a configuration diagram of a fall prediction system using drugs and movement lines of a patient according to an embodiment of the present invention, and FIG. 2 is a diagram illustrating an example of attaching a terminal of the fall prediction system to a patient's clothes.

Referring to FIG. 1, a fall prediction system 1 (hereinafter, a fall prediction system) using a patient's medications and movement lines according to an embodiment of the present invention includes a terminal 100, a gateway 200, and a fall management server 300. And a database 400.

The terminal 100 is a small device that can be attached to the patient 10 who wants to predict the likelihood of a fall, as illustrated in FIG. 2. As the patient 10 moves, it moves together, and can communicate with the gateway 200 to be described later.

The terminal 100 may include a tag capable of performing short-range communication. In this embodiment, the terminal 100 may be, for example, a beacon tag, an NFC tag, an RFID tag, or the like.

Velcro tape may be attached to one side of the terminal 100 and a portion of the clothes, respectively, in order to easily and conveniently attach to the clothes of the patient 10. Accordingly, it may be easy to attach and detach the terminal 100 to clothes.

The terminal 100 may store patient identification information (ID) therein. Such identification information may be assigned to each patient 10 to which the terminal 100 is attached. Accordingly, in this embodiment, the identification information may be information identifying the terminal 100 or information identifying the patient 10 to which the terminal 100 is attached.

When attaching the terminal 100 to another patient, it is preferable to allocate new identification information corresponding to the patient 10 again.

Accordingly, if necessary, the administrator can know which patient 10 is attaching which terminal 100 by checking the identification information.

The gateway 200 may perform wireless communication with the terminal 100 and may detect the terminal 100 when the terminal 100 approaches within a set distance from the gateway 200.

In addition, the gateway 200 may store its own location information therein. That is, such location information may include a location coordinate in which the gateway 200 is installed. The location information may include 2D or 3D location coordinates.

On the other hand, when the gateway 200 detects the terminal 100, it may receive the identification information of the patient stored in the terminal 100 from the terminal 100.

The gateway 200 may transmit the received patient's identification information and its own location information to the fall management server 300 to be described later.

The database 300 may store medication information for each identification information of a plurality of patients who want to predict the likelihood of a fall. The drug-drug information is information related to the drug being taken for each patient, and may be a drug that may affect a fall.

In addition, the database 300 may store in advance information about falling drugs according to the drug and movement of a plurality of patients to be experimented.

Hereinafter, in order to distinguish the subject of the information stored in the database 300 from the patient to be tested and the patient 10 who wants to predict the likelihood of a fall, the former is referred to as the test subject, and the latter is referred to as the fall predicted patient 10. .

Specifically, after taking the drug for a large number of test subjects in advance, the movement of movement is identified, and information on whether a fall has occurred in the movement is recorded and stored in advance. The more patients in the experiment, the better.

The fall management server 300 may extract drug information and movement lines of the corresponding fall prediction patient 10 based on the location information and identification information of the patient 10 received from the gateway 200.

In addition, the fall management server 300 extracts the drug information of the fall prediction patient 10 extracted as described above, and the fall information of the drug and movement of a plurality of test subjects matching the movement line from the database 300. The fall prediction can predict the likelihood of a fall of the patient 10.

To this end, the fall management server 300 may search the database 300 for drug information on the patient 10 based on the identification information of the patient 10 received from the gateway 200.

In addition, the fall management server 300 can extract the movement of the fall prediction patient 10 based on the location information of the patient 10 received from the gateway 200, and take the extracted fall prediction patient 10 The possibility of a fall of the patient 10 may be predicted by searching the database 300 for the drug information and the falling information of a plurality of subjects to be tested and movement paths matching the drug information and the movement path.

On the other hand, even if it is the same or similar, if you take certain drugs, the likelihood of a fall is high and if you do not take it, the likelihood of a fall may be low.

Of course, the reverse is also possible. In other words, even with the same or similar movement, if you take other specific drugs, the likelihood of a fall is low, and if you do not take it, the likelihood of a fall may be high.

Accordingly, in the present invention, in order to consider the effect of the fall prediction patient 10 on the fall according to the drug and the movement line taken by the fall prediction patient 10, the fall probability is predicted according to the drug information and the movement line of the fall prediction patient 10.

To this end, in the present invention, drug information and fall information according to the movement path for each patient to be tested are stored in advance in the database 300, and the drug information and the movement path of the fall predicted patient 10 are then mapped to the test target patient. It is possible to predict the likelihood of a fall of the corresponding fall prediction patient 10 by extracting the drug information of the drug and the fall information of the movement line from the database 300.

3 is a block diagram of a terminal according to an embodiment of the present invention.

Referring to FIG. 3, a terminal 100 according to an embodiment of the present invention may include an ID storage unit 110 and a transmission unit 120.

The ID storage unit 110 may store identification information (ID) of the patient 10 to which the terminal 100 is attached. The ID storage unit 110 may store identification information (ID) of the patient 10 as well as unique identification information of the terminal 100 attached to the patient 10. In this case, the patient identification information (ID) and the identification degree of the terminal 100 may be mapped to each other and stored.

The transmission unit 120 may transmit identification information (ID) of the patient 10 stored in the ID storage unit 110 to the gateway 200.

Here, the identification information (ID) of the patient 10 may preferably be transmitted using a wireless communication method.

4 is a configuration diagram of a gateway of the fall prediction system.

Referring to FIG. 4, a gateway 200 according to an embodiment of the present invention may include a sensing unit 210, a location information storage unit 220, a control unit 230, and a transmission unit 240.

The sensing unit 210 may detect whether the terminal 100 is present. That is, when the terminal 100 approaches within a set distance range from the gateway 200, the terminal 100 may be detected.

When the terminal 100 is implemented as a beacon, for example, it transmits a signal at a certain period, and when the terminal 100 moves to the gateway 200 within a set distance, the detection unit 210 detects the signal transmitted from the terminal 100 and detects the terminal. (100) can be detected.

The location information storage unit 220 may store location information of the gateway 200 itself. Such location information may include two-dimensional or three-dimensional location coordinates.

Also, the location information may be information on a location where the gateway 200 is installed.

The controller 230 may control the overall operation of the gateway 200. In particular, when the terminal 100 is detected by the detection unit 210, the control unit 230 may transmit the location information stored in the location information storage unit 220 to the fall management server 300 through the transmission unit 240.

In this case, the identification information (ID) of the patient 10 received from the terminal 100 may be transmitted together with the location information.

The transmission unit 240 may process communication between the gateway 200 and the fall management server 300. As described above, the transmission unit 240 may transmit location information and identification information to the fall management server 300 under the control of the controller 230.

5 is a configuration diagram of a database in the fall prediction system.

Referring to FIG. 5, the database 400 according to an embodiment of the present invention may include a drug drug information storage unit 410 and a fall information storage unit 420.

The drug-drug information storage unit 410 may store drug-drug information for each identification information of a plurality of fall predicted patients.

Such drug-drug information may include information on drugs being taken for each of the plurality of fall predicted patients 10.

For example, for each patient 10, various types of information about the type of drug, a dose, and a dose time, etc., as to which drug is being taken, may be stored in the drug drug information storage unit 410. In addition, the drug-taking information storage unit 410 may include medical information such as a disease name, symptoms, and medical conditions, which are causes of drug use.

Therefore, in the present invention, it is possible to predict the likelihood of a fall in consideration of not only drug information, but also treatment information.

The fall information storage unit 420 may store drug information and fall information according to movement for a plurality of test subjects in advance.

That is, the fall information storage unit 420 is to determine the movement line according to the movement after taking the drug for a large number of experimental subjects in advance, and store in advance information on whether a fall occurred in the movement line.

For example, it stores information about which drug was taken for each of the plurality of test subjects and then moved to which movement line, and where the fall occurred in the movement line.

The drug information stored in the drug information storage unit 410 and the fall information stored in the information storage unit 420 distinguishes the patient 10 for predicting a fall, and predicts whether there is a possibility of a fall for each patient 10. It can be a reference.

6 is a block diagram of a fall management server provided in the fall prediction system.

6, the fall management server 300 of the fall prediction system according to an embodiment of the present invention includes a communication unit 310, a movement line extraction unit 320, an information extraction unit 330, and a fall prediction unit 340. It can be configured to include.

The communication unit 310 may receive identification information and location information of the fall prediction patient 10 from the transmission unit 240 of the gateway 200.

The communication unit 310 and the transmission unit 240 may transmit and receive information in a wired or wireless communication method. For example, communication protocols such as LTE, WiFi, Bluetooth, BLE, and 5G can be used.

The location information received through the communication unit 310 may include 2D or 3D location coordinates.

In addition, the location information may include a time corresponding to the location coordinates. That is, time information when the terminal 100 is detected by the gateway 200 may be included together. Therefore, by using this time information, it is possible to check at what point the terminal 100 has approached within the set distance range of the gateway 200.

The movement line extraction unit 320 may extract the movement line of the patient 10 for predicting a fall based on the location information received through the communication unit 310. Specifically, the movement line extraction unit 320 may extract the movement line by extracting the location coordinates from the received location information as described above, and then arranging the extracted location coordinates in the measured time order. Such a movement line may be visually displayed on a display unit (not shown).

7 is an exemplary view of a moving line extracted from a moving line extracting unit according to an embodiment of the present invention.

Referring to FIG. 7, by arranging the position coordinates of the patient 10 for predicting a fall in the order of time measured, a movement line according to the movement of the patient 10 may be formed. At this time, the movement line may include a location coordinate and time information at which the location coordinate is measured.

Therefore, if you randomly select a point on the moving line, you can check the location coordinates and time information of that point. The location coordinates may be two-dimensional or three-dimensional coordinates. The drawings exemplarily show two-dimensional coordinates as an example.

In the case of a specific movement line, the fall occurrence and the location coordinates of the fall occurrence may be set at one location of the movement line.

Again, referring to FIG. 6, the information extracting unit 330 may extract information about the drug dose corresponding to the identification information of the patient 10 from the drug information storage unit 410.

In addition, the information extracting unit 330 stores drug information of the fall predicted patient 10, and information about the drug to be taken for each patient and the fall information of the movement line mapped to the extracted movement line from the fall information storage unit 420. Can be extracted.

Here, the fall information stored in the fall information storage unit 420 is, as described above, information on whether a fall occurred in the movement line after taking the drug for a number of experimental subjects, and It can be information stored by pre-recording the location coordinates and time information where the fall occurred.

The fall prediction unit 340 may predict a fall probability of the fall prediction patient 10 based on the fall information extracted by the information extraction unit 330.

In detail, the fall information extracted by the information extracting unit 330 already stores drug information and learning information on whether a fall has occurred according to the movement of a number of experimental subjects in advance. The fall information can be extracted based on the medication information and the learning information of the experimenter patient with high similarity to the movement line.

On the other hand, even if many patients in the experiment took the same drug and moved in the same movement, a fall may or may not occur.

Therefore, the likelihood of a fall, that is, the probability of a fall, is calculated and applied to the predicted patient 10 according to whether a fall has occurred with respect to a specific drug and movement line, so that the likelihood of a fall, that is, the probability of a fall, can be predicted. It can be.

Therefore, the information extraction unit 330 can extract the same drug information as the extracted drug information from the drug information storage unit 410, and the similarity to the movement line extracted by the movement line extraction unit 320 Fall information corresponding to the movement lines of a plurality of test subjects that are equal to or greater than the set reference value may be extracted from the fall information extracting unit 320.

On the other hand, the information extraction unit 330 indicates that the similarity is greater than or equal to the reference value if the location coordinates of the movement lines extracted from the movement line extraction unit 320 and the location coordinates of the movement lines of the plurality of subjects to be experimented are within a set distance. I can judge.

8 is a view for explaining a similarity according to an embodiment of the present invention.

Referring to FIG. 8, the information extraction unit 330 of the present invention uses the fall information storage unit 420 to determine the drugs and movements of the patient to be tested with high similarity to the drugs and movements of the fall prediction patient 10. It is possible to extract, and extract fall information of the subject patient whose similarity is equal to or higher than the set reference value.

In this case, the degree of similarity may be a criterion for determining whether the movement line of the fall prediction patient 10 and the movement line of the subject patient previously stored in the database 300 are similar.

As shown in the example of FIG. 8, the information extracting unit 330 starts from the same starting point O when comparing two moving lines L1 and L2, respectively, and the positions of P1 and P1', the positions of P2 and P2', respectively. For each location of ., Pn, Pn', a distance d between each other can be calculated.

Of course, in order to extract the movement path of the subject patient having a high similarity with that of the fall predicting patient 10, the movement path of the fall predicting patient 10 may be compared with that of many test subjects.

In addition, when the calculated distance d is within a set distance range, it may be determined that the two moving lines L1 and L2 are similar. More precisely, if the ratio of each calculated distance d within the set distance range is greater than or equal to the set ratio, it may be determined that the degree of similarity is high.

For example, assuming that the location coordinates are extracted for each of 5 locations from two traffic lines and the distance difference at each location is 1m, 1m, 2m, 3m, 1m, respectively, and assuming that the preset distance range is 3m, all 5 distance differences are within 3m. Therefore, it can be judged that the two traffic lines are similar.

In addition, as another example, assuming that the preset distance range is 2m, all four distance differences are within the range of 2m and one is within the range of 3m, so they are out of the range. At this time, since all four of the five are within the range of 2m, the ratio is 80%.

Accordingly, if this ratio is greater than or equal to a preset ratio, it may be determined that the two moving lines are similar. In the above example, if the set ratio is 75%, 80% is equal to or greater than the set ratio, so it can be determined that the two moving lines are similar.

In another example, if the ratio of the distance difference between the two movement lines in each of the total position coordinates within the set distance range is less than or equal to the set ratio, it is determined that the similarity is not high and the movement lines of other patients can be compared again.

As described above, the information extracting unit 330 may compare the movement lines of a plurality of patients stored in the fall information storage unit 420 to determine the degree of similarity, and then extract fall information of a patient with a high degree of similarity.

9 is an exemplary diagram showing a result of predicting a fall possibility in the management server.

Referring to FIG. 9, the fall prediction unit 340 may predict a fall probability for each fall prediction patient 10 based on drug use information and movement lines.

As shown in the example of FIG. 9, the predicted result of the fall probability may include information on a drug to be administered for each patient, a movement line having a high degree of similarity, a fall probability, and information such as a fall prediction location.

The extracted prediction result may be visually displayed on a display unit (not shown). This makes it possible to predict the likelihood of a fall for each patient in advance.

The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments, but may be manufactured in various different forms, and those having ordinary knowledge in the technical field to which the present invention pertains. It will be understood that the present invention can be implemented in other specific forms without changing the technical spirit or essential features of the present invention. Therefore, it should be understood that the embodiments described above are illustrative and non-limiting in all respects.

The fall problem is a problem that can occur not only to patients with disabilities, but also to the general public. In particular, even the general public may be more likely to fall due to taking certain medications or having mental problems. Accordingly, the development of a technology for detecting or predicting a fall is continuing.

In the present specification, as an example, a technique for predicting the likelihood of a fall occurring while a patient is moving is described, but the present invention is of course in a situation in which a fall may occur not only for the patient but also for the general public according to the drug to be taken and the movement line. It should be noted that it is applicable.

10: patient 100: terminal
110: ID storage unit 120: transmission unit
200: gateway 210: detection unit
220: location information storage unit 230: control unit
240: transmission unit 300: fall management server
310: communication unit 320: copper line extraction unit
330: information extraction unit 340: fall prediction unit
400: database 410: medication information storage unit
420: Fall information storage unit

Claims (12)

A terminal configured to be detachable from the clothes of the fall prediction patient and storing the identification information (ID) of the fall prediction patient;
A plurality of gateways installed in a plurality of locations, receiving identification information of the patient predicting fall from the terminal, and transmitting the identification information of the patient, which is stored therein, together with their location information;
A database for storing the drug information of the patient predicting the fall, and storing the drug information of the plurality of test subjects and the fall information according to the movement path;
Extracts the drug information of the fall predicted patient from the database based on the identification information of the fall predicted patient received from the plurality of gateways, extracts the movement line based on the received location information of the fall predicted patient, and the extraction Fall prediction system comprising a fall management server for predicting the fall probability of the fall predicted patient by extracting from the database the fall information of the drug and the movement line of the plurality of test subjects matching the drug information and the movement line. The method of claim 1, wherein the terminal,
An ID storage unit for storing identification information of the patient predicting the fall;
Fall prediction system comprising a transmitter for transmitting the identification information to the outside. The method of claim 2, wherein the gateway,
A sensing unit that detects the terminal when the terminal approaches within a certain distance;
A location information storage unit that stores its own location information;
A control unit for controlling to transmit location information stored in the location information storage unit when the terminal is detected by the detection unit;
Fall prediction system comprising a transmission unit for transmitting the location information by the control unit. The method of claim 3, wherein the database,
A dose drug information storage unit for storing drug drug information of the plurality of test subjects;
Fall prediction system comprising a fall information storage unit for storing the drug information and the fall information according to the movement of the plurality of test subjects. The method of claim 4, wherein the fall management server,
A communication unit for receiving location information and identification information of the fall predicted patient from the transmission unit;
A movement line extracting unit for extracting the movement line of the patient predicting the fall based on the received location information;
Using the drug information storage unit to extract the drug information of the fall prediction patient corresponding to the received identification information, and using the fall information storage unit, the extracted drug information and the plurality of the extracted movement lines are mapped. An information extracting unit for extracting drug information of a patient to be tested and information about a fall of a movement line;
Fall prediction system comprising a fall predictor for predicting a fall probability of the fall predicted patient based on the extracted fall information. The method of claim 5, wherein the copper line extracting unit,
A fall prediction system that extracts location coordinates from the received location information and arranges the extracted location coordinates in chronological order to form a moving line. The method of claim 6,
The position coordinate is a fall prediction system, characterized in that the two-dimensional or three-dimensional coordinates. The method of claim 7,
A fall prediction system that displays the two-dimensional or three-dimensional coordinates in a two-dimensional or three-dimensional coordinate system, respectively. The method of claim 6, wherein the information extracting unit,
Extracting the drug-drug information identical to the extracted drug-drug information from the drug-drug information storage unit, and extracting the fall information from the movement lines of the plurality of test subjects whose similarity to the movement line extracted from the movement line extraction unit is equal to or greater than a preset reference value. A fall prediction system that is extracted from the unit and extracts fall information of the extracted plurality of test subjects. The method of claim 9, wherein the information extracting unit,
A fall prediction system that determines that the similarity is greater than or equal to the reference value if the location coordinates of the motion lines extracted from the motion line extracting unit are within a set distance and the location coordinates of the motion lines of the plurality of subjects to be tested are greater than or equal to a set ratio. The method of claim 1, wherein the fall management server,
A fall prediction system that visually displays the result of predicting the fall probability. The method of claim 11,
A fall prediction system in which a result of predicting the likelihood of a fall is displayed with location information at which the fall of the fall prediction patient is predicted.

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