Automatic path guidance system and method for patient

Abstract

The present invention is characterized by comprising: at least one camera installed at a predetermined position in a hospital to capture an image; a movement sensing unit which senses the movement of the patient in the image; a face recognition unit for recognizing the face of the patient in the image and specifying the identity of the patient; and a path guidance unit for providing a moving path to a destination to the identity-specified patient approaching the camera, and calculating congestion of the destination and the moving path to provide an optimal moving path.

Description

Automatic navigation system and method for patients

[0001] The present invention relates to an automatic navigation system and method for a patient, and more particularly, to an automatic navigation system and method for a patient that guides the patient to an optimal path to the destination to be traveled within a hospital.

[0002] Recently, hospitals, especially general hospitals, have been undergoing a major expansion. While this expansion offers the advantage of providing patients with a wider range of high-quality services, it also presents a challenge: patients have difficulty finding their way around the hospital. Children and the elderly, in particular, often struggle to find their destinations without a guardian. Currently, the only option is to frequently ask nearby hospital staff for directions.

[0003] Large hospitals have desks or kiosks to guide patients, but these are focused on simply providing directions to their destinations, and have limitations in providing optimal directions considering factors such as congestion and the number of people waiting.

[0004] In this regard, Korean Patent No. 10-2237981 provides a guide robot that interacts with a transportation-disadvantaged person by recognizing the face of the person using a camera and displaying route information and an estimated time of arrival to the destination while driving along an optimal route. This prior art document, while possessing the technical feature of recognizing the face of a transportation-disadvantaged person and providing guidance on the optimal route to the destination, still suffers from limitations in that it fails to provide optimal guidance when traveling to multiple destinations, taking into account factors such as congestion and waiting times.

[0005] Accordingly, there is a need for an automatic navigation system and method for patients who are identified by a camera and who are provided with a path to their destination, while also providing an optimal path by taking into account congestion.

[0006] The present invention aims to provide an automatic route guidance system and method for a patient, which provides a path to a destination to a patient whose identity is identified by a camera, and which provides an optimal path by taking into account congestion.

[0007] The present invention seeks to provide an optimal travel route by taking into account congestion, number of people waiting, etc. when a patient must visit multiple destinations.

[0008] In order to achieve the above object, the present invention is characterized by including: one or more cameras installed at a certain location in a hospital to capture images; a motion detection unit for detecting the movement of a patient in the image; a facial recognition unit for recognizing the face of the patient in the image to identify the patient; and a path guidance unit for providing a path to a destination to a patient whose identity has been identified approaching the camera, and calculating the congestion level on the destination and the path to provide an optimal path.

[0009] Preferably, the one or more cameras may be placed at a corner, a crossroads, or a location where patients are waiting in a hospital.

[0010] Preferably, the motion detection unit can detect the direction of movement of the patient within the image.

[0011] Preferably, the route guidance unit may include an overall congestion calculation module that calculates the overall congestion by adding up the values ​​obtained by multiplying the destination congestion, the congestion change rate, and the route intersection congestion by weights.

[0012] Preferably, the route guidance unit may further include a destination selection module that, when there are multiple destinations that the patient must visit, calculates the overall congestion of the multiple destinations, selects the destination with the lowest overall congestion, and provides a travel route.

[0013] Preferably, the overall congestion calculation module calculates the overall congestion at regular intervals, and the destination selection module can re-select the destination with the lowest overall congestion each time the overall congestion is calculated in the overall congestion calculation module.

[0014] Preferably, the route guidance unit may include a destination congestion calculation module that calculates the destination congestion by dividing the number of patients located within the destination space by the size of the destination space.

[0015] Preferably, the route guidance unit may include a congestion change rate calculation module that calculates a congestion change rate by dividing the difference between the number of patients approaching the destination and the number of patients leaving the destination by the size of the destination space.

[0016] Preferably, the route guidance unit may include a route intersection congestion calculation module that calculates the route intersection congestion by adding up the congestion of one or more intersections existing on the route to the destination, and multiplies the value obtained by multiplying the value obtained by multiplying the number of people located within the intersection space by the size of the intersection space by a weight value and the value obtained by multiplying the value obtained by multiplying the value obtained by multiplying the number of people moving in different directions within the intersection space by the size of the intersection space by a weight value to calculate the route intersection congestion.

[0017] In addition, the present invention is characterized by including the steps of: capturing an image with one or more cameras installed at a certain location within a hospital; detecting the movement of a patient within the image; recognizing the face of the patient within the image to identify the patient; and calculating the overall congestion level of a plurality of destinations that a patient with an identified identity who approaches the camera must visit, selecting the destination with the lowest overall congestion level, and providing the destination to the patient.

[0018] The present invention has the advantage of being able to provide an optimal travel route by taking into account the congestion and the number of people waiting at the destinations when a patient visits multiple destinations.

[0019] The present invention has the advantage of providing convenience by reducing unnecessary travel time and waiting time from the patient's perspective.

[0020] The present invention has the advantage of saving human and material resources by distributing patients in consideration of congestion, number of people waiting, etc. when multiple patients must visit multiple destinations to receive treatment or examination from a hospital perspective.

[0021] Figure 1 shows a configuration diagram of an automatic guidance system for a patient according to an embodiment of the present invention.

[0022] FIG. 2 illustrates an example of a location where one or more cameras according to an embodiment of the present invention are installed within a hospital.

[0023] Figure 3 shows a configuration diagram of a path guidance unit according to an embodiment of the present invention.

[0024] Figure 4 shows a flowchart for selecting a destination of an optimal route among multiple destinations according to an embodiment of the present invention.

[0025] Figure 5 shows a map within a hospital to explain destination congestion calculation according to an embodiment of the present invention.

[0026] Figure 6 shows a map within a hospital to explain calculation of the rate of change in congestion according to an embodiment of the present invention.

[0027] Figure 7 shows a map within a hospital to explain calculation of traffic line intersection congestion according to an embodiment of the present invention.

[0028] Figure 8 shows a map within a hospital to explain how a destination selection module according to an embodiment of the present invention selects an optimal movement path.

[0029] One or more cameras installed at specific locations within the hospital to capture images;

[0030] A motion detection unit that detects the patient's movement within the image;

[0031] A facial recognition unit that recognizes the patient's face in the image and identifies the patient; and

[0032] A route guidance unit that provides a route to a destination to a patient whose identity is identified by accessing the above camera, and calculates the congestion level on the destination and the route to provide an optimal route;

[0033] An automated guidance system for patients including:

[0034] Hereinafter, the present invention will be described in detail with reference to the contents described in the attached drawings. However, the present invention is not limited or restricted by the exemplary embodiments. The same reference numerals in each drawing indicate components that perform substantially the same functions.

[0035] The purpose and effects of the present invention can be naturally understood or made clearer by the following description, and the purpose and effects of the present invention are not limited solely by the following description. Furthermore, in describing the present invention, if a detailed description of known technologies related to the present invention is deemed to unnecessarily obscure the gist of the present invention, such detailed description will be omitted.

[0036] The terminology used herein is merely used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the description of the invention, but do not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0037] While terms like "first" and "second" may be used to describe various components, these components should not be limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, a first component could be referred to as a "second component," and similarly, a second component could also be referred to as a "first component."

[0038] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0039] When interpreting components, even if there is no explicit description, it is interpreted as including the margin of error. When describing temporal relationships, for example, when temporal continuity is described with phrases such as "after," "following," "next to," or "before," this also includes cases where the relationship is not continuous, unless "immediately" or "directly" is used.

[0040] Hereinafter, the technical configuration of the present invention will be described in detail with reference to the attached drawings.

[0041] Fig. 1 illustrates a configuration diagram of an automatic guidance system (100) for a patient according to an embodiment of the present invention. Referring to Fig. 1, the automatic guidance system (100) for a patient may include one or more cameras (110), a motion detection unit (120), a facial recognition unit (130), and a guidance unit (140).

[0042] The automatic patient navigation system (100) can provide an optimal travel route by considering the level of congestion and the number of patients waiting at each destination when a patient visits multiple destinations. The automatic patient navigation system (100) can provide convenience to patients by reducing unnecessary travel time and waiting time. From a hospital perspective, the automatic patient navigation system (100) can save human and material resources by distributing patients based on factors such as congestion and the number of patients waiting at each destination when multiple patients need to visit multiple destinations for treatment or testing.

[0043] An automatic guidance system (100) for patients may be equipped with an image sensor such as a camera. RFID (Radio Frequency IDentification) and UWB (Ultra-wideband) sensors can identify a person, but cannot individually identify the identity of multiple people moving at the same time, and cannot identify individual body movements, and each individual must carry an RFID or UWB sensing device. Radar can identify body movements, but cannot individually identify a person. On the other hand, an automatic guidance system (100) for patients according to an embodiment of the present invention can individually identify the identities of multiple people and their body movements at the same time using an image sensor, and each individual does not need to carry a sensing device.

[0044] A camera (110) can be installed at a specific location within the hospital and capture images. One or more cameras (110) can be installed at a specific location within the hospital. The camera (110) can capture images in real time and provide the captured images to a motion detection unit (120) or a facial recognition unit (130).

[0045] Figure 2 illustrates an example of locations where one or more cameras are installed within a hospital according to an embodiment of the present invention. Referring to Figure 2, one or more cameras (110) may be positioned at corners, intersections, or other locations where patients are waiting within the hospital. Cameras (110a to 110e) may be installed at locations where patients are waiting within the hospital to capture images of waiting patients, while cameras (110f to 110i) may be installed at intersections, including corners and intersections, to capture images of stationary or moving patients.

[0046] Cameras (110a to 110e) installed at locations where patients wait in a hospital can provide information used when calculating destination congestion and congestion change rates, and cameras (110f to 110i) installed at intersections including corners and intersections can provide information used when calculating route intersection congestion.

[0047] The motion detection unit (120) can detect the patient's movement within the video. Through automatic motion recognition, the motion detection unit (120) can detect whether the patient is approaching, moving away, or stationary from the camera (110). The motion detection unit (120) can detect the patient's direction of movement within the video. The motion detection unit (120) can detect whether the patient is entering or leaving the destination.

[0048] The motion detection unit (120) can detect the patient's movements by recognizing the patient's actions in real time. Specifically, the motion detection unit (120) can detect the patient's movements using artificial intelligence.

[0049] The facial recognition unit (130) can identify a patient by recognizing the patient's face in the image. The facial recognition unit (130) can identify the patient by comparing the patient's face recognized in the image with patient information in the database. The facial recognition unit (130) can identify the face of any person, not just the patient in the image. For example, the facial recognition unit (130) can identify the identities of patients, general visitors, medical staff, and other individuals.

[0050] The facial recognition unit (130) can recognize the patient's face in real time and identify the patient. Specifically, the facial recognition unit (130) can detect the patient's movement using artificial intelligence. The facial recognition unit (130) can provide high accuracy (over 99.9%) that is robust to time and environmental changes. The facial recognition unit (130) can provide a fast and easy identity verification function through high-speed / high-efficiency facial recognition. The facial recognition unit (130) provides a powerful anti-spoofing function to distinguish whether the face being filmed is a real person or a fake video. The motion detection unit (120) and the facial recognition unit (130) can utilize existing video devices such as CCTVs installed in hospitals.

[0051] The AI ​​for motion detection and facial recognition mentioned above can be based on a Deep Neural Network (DNN) or a Convolutional Neural Network (CNN). A convolutional neural network (CNN) is a type of deep learning model inspired by the structure of the visual cortex of animals, designed to process data with grid patterns, such as images. A convolutional neural network typically includes convolutional layers, pooling layers, and fully connected layers. Convolutional and pooling layers can exist repeatedly within a neural network, and input data can be transformed into output through these layers. Convolutional layers utilize kernels (or masks) to extract features. The element-wise product between each element of the kernel and the input value is calculated at each location and summed to obtain the output value, which is called a feature map. This process can be repeated by applying multiple kernels to form an arbitrary number of feature maps. In a convolutional neural network, convolutional and pooling layers perform feature extraction, while fully connected layers map the extracted features to the final output, such as classification operations.

[0052] Convolutional neural networks, such as neural networks, can be trained to minimize output errors. Separate from the forward propagation process, which extracts values ​​from the input layer to the output layer, backpropagation occurs within the neural network, calculating the error between the input training data and the corresponding neural network output values ​​and updating the weights of the nodes in each layer to reduce this error. The training process in convolutional neural networks can be summarized as finding the kernel that extracts the output values ​​with the lowest error based on the given training data. The kernel is the only parameter that is automatically learned during the training of the convolutional layer. On the other hand, in convolutional neural networks, kernel size, number of kernels, padding, etc. are hyperparameters that must be set before the training process begins. Therefore, convolutional neural network models can be distinguished based on the kernel size, number of kernels, and number of convolutional and pooling layers.

[0053] Fig. 3 illustrates a configuration diagram of a route guidance unit (140) according to an embodiment of the present invention. Referring to Fig. 3, the route guidance unit (140) may include a destination congestion calculation module (141), a congestion change rate calculation module (142), a route intersection congestion calculation module (143), a total congestion calculation module (144), and a destination selection module (145).

[0054] The route guidance unit (140) provides a route to a destination to a patient whose identity is identified by the camera (110), and can calculate the congestion level on the destination and the route to provide an optimal route. The route guidance unit (140) can provide a route to a destination based on the camera (110). When there are multiple destinations for a specific patient to travel to, the route guidance unit (140) can change and provide guidance in real time to the destination with the lowest overall congestion level.

[0055] The path guidance unit (140) can count the number of patients waiting, approaching, or leaving per unit time by using a camera (110), a motion detection unit (120), and a facial recognition unit (130) installed in the hospital.

[0056] Figure 4 illustrates a flowchart for selecting an optimal route destination among multiple destinations according to an embodiment of the present invention. Referring to Figure 4, the route guidance unit (140) calculates the destination congestion, congestion change rate, and route intersection congestion to calculate a final overall congestion level, and then selects the destination with the lowest overall congestion level. Here, the "destination" may refer not only to a specific location, but also to a route leading to that location.

[0057] The destination congestion calculation module (141) can calculate destination congestion (density) by dividing the number of patients located within a destination space by the size of the destination space. Destination congestion can refer to the patient density at a specific destination, with a higher value indicating greater congestion. The destination congestion can be expressed as a formula in Mathematical Expression 1 below.

[0058] [Mathematical Formula 1]

[0059] Destination crowding = number of patients / size of specific destination space

[0060] At this time, the size of a specific destination space may be a value calculated in advance through hospital drawings, etc.

[0061] The destination congestion calculation module (141) calculates the congestion change rate, route intersection congestion, etc. at the same time, and can be repeatedly calculated and updated at specific time intervals.

[0062] FIG. 5 illustrates a hospital map for explaining the destination crowding calculation according to an embodiment of the present invention. Referring to FIG. 5, the examination room is 50 in size and has 10 patients, so the destination crowding is 0.2 (= 10 / 50). Similarly, the consultation room is 20 in size and has 2 patients, so the destination crowding is 0.1 (= 2 / 20). The general affairs department is 25 in size and has 50 patients, so the destination crowding is 2 (50 / 25). The radiology room is 10 in size and has 10 patients, so the destination crowding is 1 (10 / 10). The blood collection room is 10 in size and has 50 patients, so the destination crowding is 5 (50 / 10).

[0063] Destination congestion is highest in the blood collection room, general affairs department, radiology room, examination room, and consultation room, in that order. This indicates that the blood collection room is the most crowded due to its high patient density. Therefore, if route guidance is based solely on destination congestion, patients will be directed to the consultation room, which has the lowest congestion.

[0064] The congestion change rate calculation module (142) can calculate the congestion change rate by dividing the difference between the number of patients approaching a destination and the number of patients leaving the destination by the size of the destination space. The congestion change rate can refer to the increase / decrease rate of patients at a specific destination per unit time. A larger value indicates a change in the direction of increased congestion, and a smaller value indicates a change in the direction of decreased congestion. The congestion change rate can be expressed as a formula in Mathematical Expression 2 below.

[0065] [Equation 2]

[0066] Congestion change rate = (Number of patients approaching a specific destination - Number of patients leaving) / Size of space at a specific destination

[0067] The rate of change in congestion is calculated at the same time as the destination congestion, route intersection congestion, etc., and can be calculated repeatedly and updated at specific time intervals.

[0068] Figure 6 illustrates a hospital map illustrating the calculation of the rate of change in congestion according to an embodiment of the present invention. Referring to Figure 6, the number of patients approaching the examination room is 10, the number of patients leaving is 50, and the size is 50, so the rate of change in congestion is -0.8 (=(10-50) / 50). Likewise, the consultation room has 50 patients approaching, 10 patients leaving, and a size of 20, so the rate of change in congestion is 2 (=(50-10) / 20); the general affairs department has 20 patients approaching, 20 patients leaving, and a size of 25, so the rate of change in congestion is 0 (=(20-20) / 25); the imaging room has 20 patients approaching, 30 patients leaving, and a size of 10, so the rate of change in congestion is 1 (=(20-30) / 10); and the blood collection room has 20 patients approaching, 10 patients leaving, and a size of 10, so the rate of change in congestion is 1 (=(20-10) / 10).

[0069] The rate of change in congestion is highest in the consulting room, blood collection room, imaging room, general affairs department, and treatment room, in that order. This indicates that the consulting room has a high rate of patient growth. Therefore, if route guidance is based solely on the rate of change in congestion, patients will be directed to the treatment room with the lowest rate of change.

[0070] The intersection congestion calculation module (143) calculates intersection congestion by adding up the congestion of one or more intersections existing on the movement path to the destination, and can calculate the intersection congestion by adding up the values ​​obtained by multiplying the number of people located in the intersection space by the size of the intersection space and the number of people moving in different directions in the intersection space by the size of the intersection space by a weight. The intersection congestion may refer to the density of people at all intersections existing on the way to the destination. The intersection congestion can be expressed as a formula as in Mathematical Formula 3 below.

[0071] [Equation 3]

[0072] Traffic line intersection congestion = Traffic line intersection 1 congestion + Traffic line intersection 2 congestion + 쪋 + Traffic line intersection n congestion

[0073] Here, n represents the number of intersections that exist for the patient to reach the destination.

[0074] The traffic congestion of each intersection, i.e. traffic congestion of intersection n, can be expressed in a formula as shown in Mathematical Formula 4 below.

[0075] [Equation 4]

[0076] Congestion at intersection n = a*number of people / size of intersection space + b*number of people moving in different directions / size of intersection space

[0077] Here, a and b are specific constants and represent weights.

[0078] The intersection congestion level is calculated at the same time as the congestion change rate, destination congestion, etc., and can be calculated repeatedly and updated at specific time intervals.

[0079] Fig. 7 shows a hospital map for explaining calculation of traffic flow intersection congestion according to an embodiment of the present invention. Referring to Fig. 7, assuming that the weights are the same, when a patient moves from the current location to the examination room, the patient passes through the first intersection (I1). Since the number of people passing through the first intersection (I1) is 40, the number of people moving in different directions is 20, and the size of the intersection is 50, the traffic flow intersection congestion is 1.2 (= 40 / 50 + 20 / 50). When a patient moves from the current location to the blood collection room, the patient passes through the first intersection (I1). Since the number of people passing through the first intersection (I1) is 40, the number of people moving in different directions is 35, and the size of the intersection is 50, the traffic flow intersection congestion is 1.5 (= 40 / 50 + 35 / 50).

[0080] When a patient moves from their current location to the consultation room, they pass the second intersection (I2). Since the number of people passing through the second intersection (I2) is 60, the number of people moving in different directions is 50, and the size of the intersection is 100, the traffic congestion rate is 1.1 (=60 / 100+50 / 100). When a patient moves from their current location to the general affairs department, they pass the second intersection (I2). Since the number of people passing through the second intersection (I2) is 60, the number of people moving in different directions is 35, and the size of the intersection is 100, the traffic congestion rate is 0.95 (=60 / 100+35 / 100). When a patient moves from the current location to the imaging room, he or she passes the second intersection (I2). Since the number of people passing the second intersection (I2) is 60, the number of people moving in different directions is 55, and the size of the intersection is 100, the route intersection congestion is 1.15 (=60 / 100+55 / 100).

[0081] When calculating the congestion of a crossing, the method of counting the number of people may vary depending on the ability to control who enters and exits. In areas where access can be controlled, such as health screening centers and wards, the number of people counted may represent patients plus medical staff. In areas where access control is difficult, such as outpatient areas, the number of people counted may represent patients plus visitors.

[0082] The overall congestion calculation module (144) can calculate the overall congestion by multiplying the destination congestion, congestion change rate, and route intersection congestion by weights and adding the resulting values. In other words, if there are destinations 1, 2, and 3 to which each patient must travel, the overall congestion can be calculated for each destination. The overall congestion can be expressed as a formula in Mathematical Expression 5 below.

[0083] [Equation 5]

[0084] Total congestion = c*destination congestion + d*congestion change rate + e*traffic intersection congestion

[0085] Here, c, d, and e are specific constants and represent weights.

[0086] Referring to Figures 5, 6, and 7, the overall congestion level of the examination room is 0.6 (destination congestion level 0.2 + congestion change rate -0.8 + route intersection congestion level 1.2), the overall congestion level of the consultation room is 3.2 (destination congestion level 0.1 + congestion change rate 2 + route intersection congestion level 1.1), the overall congestion level of the general affairs department is 2.95 (destination congestion level 2 + congestion change rate 0 + route intersection congestion level 0.95), the overall congestion level of the imaging room is 3.15 (destination congestion level 1 + congestion change rate 1 + route intersection congestion level 1.15), and the overall congestion level of the blood collection room is 7.5 (destination congestion level 5 + congestion change rate 1 + route intersection congestion level 1.5). Overall congestion levels are highest in the blood collection room, consultation room, imaging room, administrative office, and examination room, with the blood collection room being the most crowded. Therefore, patients are directed to the examination room with the lowest overall congestion.

[0087] Figure 8 illustrates a hospital map illustrating how the destination selection module (145) according to an embodiment of the present invention selects an optimal travel route. Referring to Figure 8, when a patient has multiple destinations to visit, the destination selection module (145) can calculate the overall congestion of the multiple destinations and select the destination with the lowest overall congestion to provide a travel route. For example, among the examination room, consultation room, general affairs department, radiology room, and blood collection room, the examination room has the lowest overall congestion, so the route can be guided to the examination room first.

[0088] Since the overall congestion is updated periodically, the destination selection module (145) can re-select the destination with the lowest overall congestion every time the overall congestion is calculated in the overall congestion calculation module (144).

[0089] Meanwhile, since the destination selection module (145) can linearly or nonlinearly predict the overall congestion at a specific point in the future when the overall congestion is calculated at a specific time interval, it can calculate the overall congestion at the time of arrival by considering the time remaining until a specific patient arrives at a specific destination at the current time and guide the patient to a destination with the lowest overall congestion at that time.

[0090] Below, another embodiment of the present invention, an automatic guidance method for a patient, is described.

[0091] An automatic navigation method for a patient may include a step of capturing an image with one or more cameras installed at a certain location in a hospital, a step of detecting the movement of a patient in the image, a step of recognizing the face of the patient in the image to identify the patient, and a step of calculating the overall congestion of a plurality of destinations to be visited by a patient whose identity is identified by approaching the camera, and selecting a destination with the lowest overall congestion and providing the destination to the patient.

[0092] The step of capturing images with one or more cameras installed at a certain location within the hospital means an operation performed by one or more cameras (110) described above.

[0093] The step of detecting the patient's movement in the image refers to the operation performed in the aforementioned motion detection unit (120).

[0094] The step of recognizing the patient's face in the image and identifying the patient means the operation performed in the aforementioned facial recognition unit (130).

[0095] The step of calculating the overall congestion of multiple destinations that a patient with a specific identity who accesses the above camera must visit, selecting the destination with the lowest overall congestion, and providing it to the patient refers to an operation performed in the above-described route guidance unit (140).

[0096] While the present invention has been described in detail through representative examples above, those skilled in the art will understand that various modifications to the above-described embodiments are possible without departing from the scope of the present invention. Therefore, the scope of the present invention should not be limited to the described embodiments, but should be defined not only by the claims described below but also by all changes or modifications derived from the claims and equivalent concepts.

[0097] [Explanation of symbols]

[0098] 100: Automated Guidance System for Patients

[0099] 110: Camera 120: Motion detector

[0100] 130: Facial recognition unit 140: Directions unit

[0101] 141: Destination congestion calculation module 142: Congestion change rate calculation module

[0102] 143: Route intersection congestion calculation module 144: Overall congestion calculation module

[0103] 145: Destination Selection Module

[0104] The present invention aims to provide an automatic route guidance system and method for a patient, which provides a path to a destination to a patient whose identity is identified by a camera, and which provides an optimal path by taking into account congestion.

[0105] The present invention seeks to provide an optimal travel route by taking into account congestion, number of people waiting, etc. when a patient must visit multiple destinations.

Claims

1. One or more cameras installed at a specific location within the hospital to capture images; A motion detection unit that detects the patient's movement within the image; A facial recognition unit that recognizes the patient's face in the image and identifies the patient; and A route guidance unit that provides a route to a destination to a patient whose identity is identified by accessing the camera, and calculates the congestion level on the destination and the route to provide an optimal route; An automated guidance system for patients including:

2. In paragraph 1, One or more of the above cameras, An automatic navigation system characterized by being placed at a fork in the road including a corner or intersection or at a location where a patient is waiting in a hospital.

3. In paragraph 1, The above motion detection unit, An automatic guidance system characterized by detecting the direction of movement of a patient in the above image.

4. In paragraph 1, The above route guide is, A total congestion calculation module that calculates the total congestion by adding up the weighted values ​​of destination congestion, congestion change rate, and route intersection congestion; An automatic guidance system characterized by including:

5. In paragraph 4, The above route guide is, If the patient has multiple destinations to visit, A destination selection module that calculates the overall congestion of multiple destinations, selects the destination with the lowest overall congestion, and provides a travel route; An automatic guidance system characterized by further including:

6. In paragraph 5, The above overall congestion calculation module is, Calculate the overall congestion at regular intervals, The above destination selection module, An automatic route guidance system characterized in that the destination with the lowest overall congestion is re-selected each time the overall congestion is calculated in the above overall congestion calculation module.

7. In paragraph 1, The above route guide is, A destination congestion calculation module that calculates the destination congestion by dividing the number of patients located within the destination space by the size of the destination space; An automatic guidance system characterized by including:

8. In paragraph 1, The above route guide is, A congestion change rate calculation module that calculates the congestion change rate by dividing the difference between the number of patients approaching the destination and the number of patients leaving the destination by the size of the destination space; An automatic guidance system characterized by including:

9. In paragraph 1, The above route guide is, The route intersection congestion is calculated by adding the congestion of one or more intersections on the route to the destination. A route intersection congestion calculation module that calculates the congestion of an intersection by multiplying the value obtained by dividing the number of people located within an intersection by the size of the intersection and the value obtained by multiplying the value obtained by dividing the number of people moving in different directions within an intersection by the size of the intersection by a weight, and adding the values; An automatic guidance system characterized by including:

10. A step of recording images using one or more cameras installed at a certain location within the hospital; A step of detecting the patient's movement within the image; A step of recognizing the patient's face in the above image to identify the patient; and A step of calculating the overall congestion of multiple destinations that a patient with a specific identity who accesses the above camera must visit, selecting the destination with the lowest overall congestion, and providing the destination to the patient; An automated guidance method for patients including:

Images