JP6320702B2 - Medical information processing apparatus, program and system - Google Patents

Description

  Embodiments described herein relate generally to a medical information processing apparatus, a program, and a system.

  Traditionally, in rehabilitation, many specialists aim to lead better lives for those with mental and physical disabilities caused by various causes such as illness, trauma, and aging, and congenital disabilities. Support that cooperated by is performed. For example, rehabilitation is supported by a number of specialists such as rehabilitation specialists, rehabilitation nurses, physical therapists, occupational therapists, speech therapists, clinical psychologists, prosthetic braces, and social workers.

  On the other hand, in recent years, development of a motion capture technique for digitally recording the movement of a person or an object has progressed. As a method of motion capture technology, for example, an optical type, a mechanical type, a magnetic type, a camera type and the like are known. For example, a camera system is known in which a marker is attached to a person, the marker is detected by a tracker such as a camera, and the movement of the person is digitally recorded by processing the detected marker. In addition, as a method that does not use markers and trackers, an infrared sensor is used to measure the distance from the sensor to a person, and the movement of the person is digitally detected by detecting various movements of the person's size and skeleton. The recording method is known. As a sensor using such a method, for example, Kinect (registered trademark) is known.

Japanese Patent Laid-Open No. 9-56697

  The problem to be solved by the present invention is to provide a medical information processing apparatus, a program, and a system capable of improving the quality of instruction performed by an instructor who instructs each of a plurality of subjects.

The medical information processing apparatus according to the embodiment includes an acquisition unit, a calculation unit, and an output control unit. An acquisition part acquires the operation | movement information showing the coordinate change of each part of the said subject person for every said subject person about the some subject person who respectively performs predetermined operation | movement. A calculation part calculates the index value which shows the implementation condition of the said predetermined operation | movement for every said subject based on the said motion information for every said subject. The output control unit determines, for each target person, a teaching priority indicating which target person is to be prioritized based on the index value for each target person, and based on the determined teaching priority. Then, support information for supporting the instructor instructing the plurality of subjects is output.

FIG. 1 is a diagram illustrating an example of a configuration of a medical information processing apparatus according to the first embodiment. FIG. 2 is a diagram illustrating an example of the configuration of the motion information collection unit according to the first embodiment. FIG. 3A is a diagram for explaining processing of the motion information generation unit according to the first embodiment. FIG. 3B is a diagram for explaining processing of the motion information generation unit according to the first embodiment. FIG. 3C is a diagram for explaining processing of the motion information generation unit according to the first embodiment. FIG. 4 is a diagram illustrating an example of skeleton information generated by the motion information generation unit according to the first embodiment. FIG. 5 is a diagram illustrating an example of the configuration of the medical information processing apparatus according to the first embodiment. FIG. 6 is a diagram illustrating an example of operation information stored by the operation information storage unit according to the first embodiment. FIG. 7 is a diagram illustrating an example of information stored in the implementation menu storage unit according to the first embodiment. FIG. 8 is a diagram illustrating an example of information stored in the implementation menu storage unit according to the first embodiment. FIG. 9 is a diagram illustrating an example of information stored in the negligence operation storage unit according to the first embodiment. FIG. 10 is a diagram illustrating an example of information stored in the participation information storage unit according to the first embodiment. FIG. 11 is a diagram illustrating an example of information stored in the warning condition storage unit according to the first embodiment. FIG. 12 is a diagram for explaining processing of the determination unit according to the first embodiment. FIG. 13 is a diagram for explaining a display screen displayed by the output control unit according to the first embodiment. FIG. 14 is a flowchart illustrating an overall processing procedure performed by the medical information processing apparatus according to the first embodiment. FIG. 15 is a flowchart illustrating a procedure of proficiency level calculation processing according to the first embodiment. FIG. 16 is a flowchart illustrating a procedure of risk degree calculation processing according to the first embodiment. FIG. 17 is a flowchart illustrating a procedure of a laziness calculation process according to the first embodiment. FIG. 18 is a flowchart illustrating a procedure of the degree of participation calculation process according to the first embodiment. FIG. 19 is a diagram illustrating an example of information stored in the personal information storage unit according to the second embodiment. FIG. 20A is a diagram for explaining processing of the calculation unit according to the second embodiment. FIG. 20B is a diagram for explaining processing of the calculation unit according to the second embodiment. FIG. 21 is a diagram for explaining processing of the output control unit according to another embodiment. FIG. 22A is a diagram for explaining processing of the output control unit according to another embodiment. FIG. 22B is a diagram for explaining processing of the output control unit according to another embodiment. FIG. 23 is a diagram for explaining processing of the output control unit according to another embodiment. FIG. 24 is a diagram illustrating an example of information stored in the dangerous biological information storage unit according to another embodiment. FIG. 25 is a diagram illustrating an example of a configuration of a medical information processing apparatus according to another embodiment. FIG. 26 is a diagram illustrating an example of information stored in the implementation menu storage unit according to another embodiment. FIG. 27 is a diagram for explaining an example when applied to a service providing apparatus.

  Hereinafter, a medical information processing apparatus, a program, and a system according to an embodiment will be described with reference to the drawings. The medical information processing apparatus described below may be used as a single medical information processing apparatus, or may be used by being incorporated in a system such as a medical record system or a rehabilitation department system. .

(First embodiment)
FIG. 1 is a block diagram illustrating a configuration example of the medical information processing apparatus 100 according to the first embodiment. The medical information processing apparatus 100 according to the first embodiment is an apparatus that supports rehabilitation performed in, for example, a medical institution, a home, or a workplace. Here, “rehabilitation” refers to techniques and methods for improving the potential of patients with long-term treatment periods, such as disabilities, chronic diseases, geriatric diseases, etc., and restoring and promoting life functions and thus social functions. . Such techniques and methods include, for example, function training for restoring and promoting life functions and social functions. Here, examples of the functional training include walking training and joint range-of-motion training. In addition, a person who is a target of rehabilitation is referred to as a “subject”. The target person is, for example, a sick person, an injured person, an elderly person, a disabled person, or the like. In addition, when rehabilitation is performed, a person who assists the subject is referred to as “assistant”. This assistant is, for example, a medical worker such as a doctor, a physical therapist, or a nurse engaged in a medical institution, a caregiver who cares for the subject at home, a family member, a friend, or the like. Rehabilitation is also abbreviated as “rehabilitation”.

  As shown in FIG. 1, in the first embodiment, the medical information processing apparatus 100 is connected to a motion information collection unit 10A, a motion information collection unit 10B, a motion information collection unit 10C, and a motion information collection unit 10D. In FIG. 1, the case where four motion information collection units 10 </ b> A to 10 </ b> D are connected to the medical information processing apparatus 100 is illustrated, but the embodiment is not limited to this, for example, the medical information processing apparatus An arbitrary number of operation information collection units may be connected to 100. In the following description, the operation information collection units 10A to 10D are collectively referred to as “operation information collection unit 10”. Further, the operation information collection unit 10 and the medical information processing apparatus 100 may be connected via a network. In this case, for example, the motion information collection unit 10 and the medical information processing apparatus 100 may be arranged in different rooms or in different facilities.

  FIG. 2 is a diagram illustrating an example of the configuration of the motion information collection unit 10 according to the first embodiment. The motion information collection unit 10 detects the motion of a person or an object in a space where rehabilitation is performed, and collects motion information representing the motion of the person or the object. Note that the operation information will be described in detail when the processing of the operation information generation unit 14 described later is described. For example, Kinect (registered trademark) is used as the operation information collection unit 10.

  As illustrated in FIG. 2, the motion information collection unit 10 includes a color image collection unit 11, a distance image collection unit 12, a voice recognition unit 13, and a motion information generation unit 14. Note that the configuration of the operation information collection unit 10 illustrated in FIG. 2 is merely an example, and the embodiment is not limited thereto.

  The color image collection unit 11 photographs a subject such as a person or an object in a space where rehabilitation is performed, and collects color image information. For example, the color image collection unit 11 detects light reflected from the subject surface with a light receiving element, and converts visible light into an electrical signal. Then, the color image collection unit 11 generates color image information for one frame corresponding to the shooting range by converting the electrical signal into digital data. The color image information for one frame includes, for example, shooting time information and information in which RGB (Red Green Blue) values are associated with each pixel included in the one frame. The color image collection unit 11 shoots a moving image of the shooting range by generating color image information of a plurality of continuous frames from visible light detected one after another. The color image information generated by the color image collection unit 11 may be output as a color image in which the RGB values of each pixel are arranged in a bitmap. Further, the color image collection unit 11 includes, for example, a complementary metal oxide semiconductor (CMOS) or a charge coupled device (CCD) as a light receiving element.

  The distance image collection unit 12 photographs a subject such as a person or an object in a space where rehabilitation is performed, and collects distance image information. For example, the distance image collection unit 12 irradiates the surrounding area with infrared rays, and detects a reflected wave obtained by reflecting the irradiation wave on the surface of the subject with the light receiving element. Then, the distance image collection unit 12 obtains the distance between the subject and the distance image collection unit 12 based on the phase difference between the irradiation wave and the reflected wave and the time from irradiation to detection, and corresponds to the shooting range. The distance image information for the frame is generated. The distance image information for one frame includes, for example, shooting time information and information in which each pixel included in the shooting range is associated with the distance between the subject corresponding to the pixel and the distance image collection unit 12. included. The distance image collection unit 12 captures a moving image of the shooting range by generating distance image information of a plurality of continuous frames from reflected waves detected one after another. The distance image information generated by the distance image collection unit 12 may be output as a distance image in which color shades corresponding to the distance of each pixel are arranged in a bitmap. The distance image collection unit 12 includes, for example, a CMOS or a CCD as a light receiving element. This light receiving element may be shared with the light receiving element used in the color image collection unit 11. The unit of the distance calculated by the distance image collection unit 12 is, for example, meters [m].

  The voice recognition unit 13 collects surrounding voices, performs sound source direction specification and voice recognition. The voice recognition unit 13 has a microphone array including a plurality of microphones, and performs beam forming. Beam forming is a technique for selectively collecting sound from a specific direction. For example, the voice recognition unit 13 specifies the direction of the sound source by beam forming using a microphone array. The voice recognition unit 13 recognizes a word from the collected voice using a known voice recognition technique. That is, the speech recognition unit 13 generates, as a speech recognition result, for example, information associated with a word recognized by the speech recognition technology, a direction in which the word is emitted, and a time at which the word is recognized.

  The motion information generation unit 14 generates motion information representing the motion of a person or an object. This motion information is generated by, for example, capturing a human motion (gesture) as a series of a plurality of postures (poses). In brief, the motion information generation unit 14 first obtains the coordinates of each joint forming the skeleton of the human body from the distance image information generated by the distance image collection unit 12 by pattern matching using a human body pattern. The coordinates of each joint obtained from the distance image information are values represented by a distance image coordinate system (hereinafter referred to as “distance image coordinate system”). Therefore, the motion information generation unit 14 then represents the coordinates of each joint in the distance image coordinate system in a three-dimensional space coordinate system in which rehabilitation is performed (hereinafter referred to as a “world coordinate system”). Convert to The coordinates of each joint represented in this world coordinate system become the skeleton information for one frame. Further, the skeleton information for a plurality of frames is the operation information. Hereinafter, processing of the motion information generation unit 14 according to the first embodiment will be specifically described.

  3A to 3C are diagrams for explaining processing of the motion information generation unit 14 according to the first embodiment. FIG. 3A shows an example of a distance image generated by the distance image collection unit 12. In FIG. 3A, for convenience of explanation, an image expressed by a line drawing is shown. However, an actual distance image is an image expressed by shading of colors according to the distance. In this distance image, each pixel has a “pixel position X” in the left-right direction of the distance image, a “pixel position Y” in the up-down direction of the distance image, and a subject corresponding to the pixel and the distance image collection unit 12. It has a three-dimensional value associated with “distance Z”. Hereinafter, the coordinate value of the distance image coordinate system is expressed by the three-dimensional value (X, Y, Z).

  In the first embodiment, the motion information generation unit 14 stores in advance human body patterns corresponding to various postures by learning, for example. Each time the distance image collection unit 12 generates distance image information, the motion information generation unit 14 acquires the generated distance image information of each frame. Then, the motion information generation unit 14 performs pattern matching using a human body pattern on the acquired distance image information of each frame.

  Here, the human body pattern will be described. FIG. 3B shows an example of a human body pattern. In the first embodiment, since the human body pattern is a pattern used for pattern matching with distance image information, it is expressed in the distance image coordinate system, and is similar to the person depicted in the distance image, on the surface of the human body. Information (hereinafter referred to as “human body surface”). For example, the human body surface corresponds to the skin or clothing surface of the person. Furthermore, as shown in FIG. 3B, the human body pattern includes information on each joint forming the skeleton of the human body. That is, in the human body pattern, the relative positional relationship between the human body surface and each joint is known.

  In the example shown in FIG. 3B, the human body pattern includes information on 20 joints from the joint 3a to the joint 3t. Of these, the joint 3a corresponds to the head, the joint 3b corresponds to the center of both shoulders, the joint 3c corresponds to the waist, and the joint 3d corresponds to the center of the buttocks. The joint 3e corresponds to the right shoulder, the joint 3f corresponds to the right elbow, the joint 3g corresponds to the right wrist, and the joint 3h corresponds to the right hand. The joint 3i corresponds to the left shoulder, the joint 3j corresponds to the left elbow, the joint 3k corresponds to the left wrist, and the joint 3l corresponds to the left hand. Further, the joint 3m corresponds to the right hip, the joint 3n corresponds to the right knee, the joint 3o corresponds to the right ankle, and the joint 3p corresponds to the right foot. Further, the joint 3q corresponds to the left hip, the joint 3r corresponds to the left knee, the joint 3s corresponds to the left ankle, and the joint 3t corresponds to the foot foot of the left foot.

  In addition, although FIG. 3B demonstrated the case where the human body pattern had information of 20 joints, the embodiment is not limited to this, and the position and number of joints may be arbitrarily set by the operator. . For example, when only the movement change of the limbs is captured, the information on the joints 3b and 3c among the joints 3a to 3d may not be acquired. In addition, when capturing changes in the movement of the right hand in detail, not only the joint 3h but also the joint of the finger of the right hand may be newly set. The joint 3a, the joint 3h, the joint 3l, the joint 3p, and the joint 3t in FIG. 3B are different from so-called joints because they are the end portions of the bones, but are important points that represent the position and orientation of the bones. For the sake of convenience, it is described here as a joint.

  The motion information generation unit 14 performs pattern matching with the distance image information of each frame using the human body pattern. For example, the motion information generation unit 14 extracts a person with a certain posture from the distance image information by pattern matching the human body surface of the human body pattern shown in FIG. 3B and the distance image shown in FIG. 3A. In this way, the motion information generation unit 14 obtains the coordinates of the human body surface depicted in the distance image. Further, as described above, in the human body pattern, the relative positional relationship between the human body surface and each joint is known. Therefore, the motion information generation unit 14 calculates the coordinates of each joint in the person from the coordinates of the human body surface depicted in the distance image. In this way, as shown in FIG. 3C, the motion information generation unit 14 acquires the coordinates of each joint forming the skeleton of the human body from the distance image information. Note that the coordinates of each joint obtained here are the coordinates of the distance coordinate system.

  In addition, the motion information generation part 14 may use the information showing the positional relationship of each joint supplementarily when performing pattern matching. The information representing the positional relationship between the joints includes, for example, the joint relationship between the joints (for example, “joint 3a and joint 3b are coupled”) and the movable range of each joint. A joint is a site that connects two or more bones. The angle between the bones changes according to the change in posture, and the range of motion differs depending on the joint. For example, the range of motion is represented by the maximum and minimum values of the angles formed by the bones connected by each joint. For example, when learning the human body pattern, the motion information generation unit 14 also learns the range of motion of each joint and stores it in association with each joint.

  Subsequently, the motion information generation unit 14 converts the coordinates of each joint in the distance image coordinate system into values represented in the world coordinate system. The world coordinate system is a coordinate system in a three-dimensional space where rehabilitation is performed. For example, the position of the motion information collection unit 10 is the origin, the horizontal direction is the x axis, the vertical direction is the y axis, and the direction is orthogonal to the xy plane. Is a coordinate system with z as the z-axis. The coordinate value in the z-axis direction may be referred to as “depth”.

  Here, the process of converting from the distance image coordinate system to the world coordinate system will be described. In the first embodiment, it is assumed that the motion information generation unit 14 stores in advance a conversion formula for converting from the distance image coordinate system to the world coordinate system. For example, this conversion formula receives the coordinates of the distance image coordinate system and the incident angle of the reflected light corresponding to the coordinates, and outputs the coordinates of the world coordinate system. For example, the motion information generation unit 14 inputs the coordinates (X1, Y1, Z1) of a certain joint and the incident angle of reflected light corresponding to the coordinates to the conversion formula, and coordinates (X1, Y1) of the certain joint , Z1) are converted into coordinates (x1, y1, z1) in the world coordinate system. Since the correspondence relationship between the coordinates of the distance image coordinate system and the incident angle of the reflected light is known, the motion information generation unit 14 inputs the incident angle corresponding to the coordinates (X1, Y1, Z1) into the conversion equation. can do. Although the case has been described here in which the motion information generation unit 14 converts the coordinates of the distance image coordinate system to the coordinates of the world coordinate system, it is also possible to convert the coordinates of the world coordinate system to the coordinates of the distance coordinate system. is there.

  Then, the motion information generation unit 14 generates skeleton information from the coordinates of each joint represented in the world coordinate system. FIG. 4 is a diagram illustrating an example of skeleton information generated by the motion information generation unit 14. The skeleton information of each frame includes shooting time information of the frame and coordinates of each joint. For example, as illustrated in FIG. 4, the motion information generation unit 14 generates skeleton information in which joint identification information and coordinate information are associated with each other. In FIG. 4, the shooting time information is not shown. The joint identification information is identification information for identifying a joint and is set in advance. For example, joint identification information “3a” corresponds to the head, and joint identification information “3b” corresponds to the center of both shoulders. Similarly for the other joint identification information, each joint identification information indicates a corresponding joint. The coordinate information indicates the coordinates of each joint in each frame in the world coordinate system.

  In the first line of FIG. 4, joint identification information “3a” and coordinate information “(x1, y1, z1)” are associated. That is, the skeleton information in FIG. 4 represents that the head is present at the coordinates (x1, y1, z1) in a certain frame. Also, in the second row of FIG. 4, joint identification information “3b” and coordinate information “(x2, y2, z2)” are associated. That is, the skeleton information in FIG. 4 indicates that the center of both shoulders exists at the position of coordinates (x2, y2, z2) in a certain frame. Similarly, other joints indicate that each joint exists at a position represented by each coordinate in a certain frame.

  In this way, every time the distance image information of each frame is acquired from the distance image collection unit 12, the motion information generation unit 14 performs pattern matching on the distance image information of each frame, and the world coordinate from the distance image coordinate system. By converting into a system, skeleton information of each frame is generated. Then, the motion information generation unit 14 outputs the generated skeleton information of each frame to the medical information processing apparatus 100 and stores it in the later-described motion information storage unit 131.

  Note that the processing of the motion information generation unit 14 is not limited to the above-described method. For example, in the above description, the method in which the motion information generation unit 14 performs pattern matching using a human body pattern has been described, but the embodiment is not limited thereto. For example, instead of the human body pattern or together with the human body pattern, a pattern matching method using a pattern for each part may be used.

  For example, in the above description, the method in which the motion information generation unit 14 obtains the coordinates of each joint from the distance image information has been described, but the embodiment is not limited thereto. For example, the motion information generation unit 14 may obtain a coordinate of each joint using color image information together with distance image information. In this case, for example, the motion information generation unit 14 performs pattern matching between the human body pattern expressed in the color image coordinate system and the color image information, and obtains the coordinates of the human body surface from the color image information. The coordinate system of this color image does not include the “distance Z” information referred to in the distance image coordinate system. Therefore, for example, the motion information generation unit 14 obtains the information of “distance Z” from the distance image information, and obtains the coordinates of the world coordinate system of each joint by calculation processing using these two pieces of information.

  Further, the motion information generation unit 14 needs the color image information generated by the color image collection unit 11, the distance image information generated by the distance image collection unit 12, and the voice recognition result output by the voice recognition unit 13. Accordingly, the information is appropriately output to the medical information processing apparatus 100 and stored in the operation information storage unit 131 described later. The pixel position of the color image information and the pixel position of the distance image information can be associated in advance according to the positions of the color image collection unit 11 and the distance image collection unit 12 and the shooting direction. For this reason, the pixel position of the color image information and the pixel position of the distance image information can be associated with the world coordinate system calculated by the motion information generation unit 14. Furthermore, by using this correspondence and the distance [m] calculated by the distance image collection unit 12, the height and the length of each part of the body (the length of the arm and the length of the abdomen) can be obtained, or on the color image. It is possible to obtain a distance between two designated pixels. Similarly, the shooting time information of the color image information and the shooting time information of the distance image information can be associated in advance. In addition, the motion information generation unit 14 refers to the speech recognition result and the distance image information, and if there is a joint 3a in the vicinity of the direction in which the speech-recognized word is issued at a certain time, the person including the joint 3a is displayed. It can be output as an emitted word. Furthermore, the motion information generation unit 14 also appropriately outputs information indicating the positional relationship between the joints to the medical information processing apparatus 100 as necessary, and stores the information in the later-described motion information storage unit 131.

  In addition, the motion information generation unit 14 generates depth image information for one frame corresponding to the imaging range, using the depth which is a coordinate value in the z-axis direction of the world coordinate system. The depth image information for one frame includes, for example, shooting time information and information in which each pixel included in the shooting range is associated with a depth corresponding to the pixel. In other words, the depth image information is obtained by associating depth information in place of the distance information associated with each pixel of the distance image information, and representing each pixel position in the same distance image coordinate system as the distance image information. be able to. The motion information generation unit 14 outputs the generated depth image information to the medical information processing apparatus 100 and stores it in the motion information storage unit 131 described later. It should be noted that the depth image information may be output as a depth image in which color shades corresponding to the depth of each pixel are arranged in a bitmap.

  Although the case where the motion information collecting unit 10 detects the motion of a single person has been described here, the embodiment is not limited to this. If it is included in the shooting range of the motion information collection unit 10, the motion information collection unit 10 may detect the motions of a plurality of persons. When a plurality of persons are photographed in the distance image information of the same frame, the motion information collection unit 10 associates the skeleton information of the plurality of persons generated from the distance image information of the same frame, This is output to the medical information processing apparatus 100 as operation information.

  Further, the configuration of the operation information collection unit 10 is not limited to the above configuration. For example, when motion information is generated by detecting the motion of a person by other motion capture, such as optical, mechanical, magnetic, etc., the motion information collection unit 10 does not necessarily include the distance image collection unit 12. It does not have to be. In such a case, the motion information collection unit 10 includes, as motion sensors, a marker that is worn on the human body in order to detect a human motion, and a sensor that detects the marker. Then, the motion information collection unit 10 detects motion of a person using a motion sensor and generates motion information. Further, the motion information collecting unit 10 associates the pixel position of the color image information with the coordinates of the motion information using the position of the marker included in the image photographed by the color image collecting unit 11, and if necessary, Output to the medical information processing apparatus 100 as appropriate. For example, the motion information collection unit 10 may not include the speech recognition unit 13 when the speech recognition result is not output to the medical information processing apparatus 100.

  Furthermore, in the embodiment described above, the motion information collection unit 10 outputs the coordinates of the world coordinate system as the skeleton information, but the embodiment is not limited to this. For example, the motion information collection unit 10 outputs the coordinates of the distance image coordinate system before conversion, and the conversion from the distance image coordinate system to the world coordinate system may be performed on the medical information processing apparatus 100 side as necessary. Good.

  Returning to the description of FIG. The medical information processing apparatus 100 performs processing for supporting rehabilitation using the motion information output from the motion information collection unit 10. The medical information processing apparatus 100 is an information processing apparatus such as a computer or a workstation, for example, and includes an output unit 110, an input unit 120, a storage unit 130, and a control unit 140, as shown in FIG.

  The output unit 110 outputs various types of information for supporting rehabilitation. For example, the output unit 110 displays a GUI (Graphical User Interface) for an operator operating the medical information processing apparatus 100 to input various requests using the input unit 120, or is generated in the medical information processing apparatus 100. The output image or the like is displayed, or a warning sound is output. For example, the output unit 110 is a monitor, a speaker, headphones, a headphone portion of a headset, or the like. Further, the output unit 110 may be a display of a system that is worn on the user's body, such as a glasses-type display or a head-mounted display.

  The input unit 120 accepts input of various information for supporting rehabilitation. For example, the input unit 120 receives input of various requests from an operator of the medical information processing apparatus 100 and transfers the received various requests to the medical information processing apparatus 100. For example, the input unit 120 is a mouse, a keyboard, a touch command screen, a trackball, a microphone, a microphone portion of a headset, or the like. The input unit 120 may be a sensor that acquires biological information such as a sphygmomanometer, a heart rate monitor, or a thermometer.

  The storage unit 130 is, for example, a semiconductor memory device such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk device or an optical disk device. The control unit 140 can be realized by an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA) or a central processing unit (CPU) executing a predetermined program.

  The configuration of the medical information processing apparatus 100 according to the first embodiment has been described above. Based on such a configuration, the medical information processing apparatus 100 according to the first embodiment can improve the quality of instruction performed by an instructor who instructs each of a plurality of subjects. The instructor is, for example, a medical worker such as a doctor, a physical therapist, or a nurse engaged in a medical institution, or a sports trainer engaged in a sports facility.

  For example, when a subject performs rehabilitation, in order to perform rehabilitation safely and effectively, a rehabilitation leader guides the subject. For example, the instructor provides support to each target person, such as teaching the correct rehabilitation action (form) to the target person, supporting the target person's body, and encouraging the target person. .

  By the way, since the number of subjects requiring rehabilitation increases and the number of leaders is insufficient, one leader is required to teach a plurality of subjects at once. As an example, in a medical institution, several instructors are assigned to each room where rehabilitation is performed, and rehabilitation instruction is provided in such a way that a plurality of target persons who perform rehabilitation in that room are appropriately instructed as necessary. Has been done.

  However, when one instructor taught a plurality of subjects at once, high-quality instruction was not always provided. For example, some instructors may be motivated as a result of an instructor instructing one subject while another subject falls, or as a result of being unable to participate equally in each subject. May fall. Therefore, the medical information processing apparatus 100 according to the first embodiment improves the quality of guidance performed by a leader who individually guides a plurality of subjects by supporting the leader by the process described below. Can do.

  Hereinafter, in the first embodiment, one instructor has four persons, a target person A (Mr. A), a target person B (Mr. B), a target person C (Mr. C), and a target person D (Mr. D). A case where each of the four subjects is instructed when the subject performs squat training will be described. In this case, the motion information collection units 10A to 10D illustrated in FIG. 1 capture the target persons A to D and generate motion information. In addition, embodiment is not limited to this, For example, the number of subjects and the rehabilitation item which a subject performs may be changed arbitrarily. In addition, here, a case where four subjects perform the same type of rehabilitation will be described, but the present invention is not limited to this, and each subject may perform a different event. Further, a case where a plurality of subjects are photographed by one motion information collecting unit 10 may be used.

  FIG. 5 is a diagram illustrating an example of the configuration of the medical information processing apparatus 100 according to the first embodiment. As shown in FIG. 5, in the medical information processing apparatus 100, the storage unit 130 includes an operation information storage unit 131, an execution menu storage unit 132, a dangerous operation storage unit 133, a negligence operation storage unit 134, and a participation information storage. Unit 135 and a warning condition storage unit 136.

  The motion information storage unit 131 stores various information collected by the motion information collection unit 10. Specifically, the motion information storage unit 131 stores the motion information generated by the motion information generation unit 14. More specifically, the motion information storage unit 131 stores skeleton information for each frame generated by the motion information generation unit 14. Here, it is also possible to store the color image information, the distance image information, and the speech recognition result output by the motion information storage unit 131 and the motion information generation unit 14 in association with each other for each frame.

  FIG. 6 is a diagram illustrating an example of operation information stored in the operation information storage unit 131 according to the first embodiment. As illustrated in FIG. 6, the motion information storage unit 131 stores motion information in which a name number, an implementation date, and motion information are associated with each other for each name. Here, the “name” indicates the name of the target person. The “target person number” is an identifier for uniquely specifying the target person, and is assigned to each target person. “Implementation date” indicates the date and time when the subject performed the squat training. “Operation information” indicates information collected by the operation information collection unit 10.

  As illustrated in FIG. 6, the operation information storage unit 131 includes “name: A”, “name number: 1”, “implementation date: 20120801_1”, “operation information: color image information, distance image information, speech recognition result, “Skeleton information”, etc. are stored. The above information includes the operation information in the “first” squat training conducted on “August 1” of “2012” by a person of “name: A” whose “name number” is “1”. It indicates that operation information including “color image information”, “distance image information”, “voice recognition result”, and “skeleton information” is stored.

  Here, in the motion information shown in FIG. 6, “color image information”, “distance image information”, “speech recognition result”, and “skeleton information” for every frame captured when the squat training is executed. Are stored in association with time in chronological order.

  Further, as shown in FIG. 6, the motion information storage unit 131 includes “name: A”, “name number: 1”, “implementation date: 20120802”, “motion information: color image information, distance image information, voice recognition”. Results, skeleton information, etc. are stored. In other words, the motion information storage unit 131 similarly stores motion information in the “second” squat training performed by the person of “name: A” on “August 1” of “2012”.

  Further, as illustrated in FIG. 6, the motion information storage unit 131 also performs “color image information”, “distance image information”, and “speech recognition result” for a person with “name: B” and “name number: 2”. And motion information including “skeleton information” is stored. As described above, the motion information storage unit 131 stores the squat training motion information collected for each subject in association with each subject. Note that the operation information shown in FIG. 6 is merely an example. That is, the motion information storage unit 131 can further store information other than “color image information”, “distance image information”, “speech recognition result”, and “skeleton information” shown in FIG. For example, when the motion information collection unit 10 does not have the voice recognition unit 13, the voice recognition result is stored as not included.

  The “color image information” and “distance image information” included in the operation information include bitmap (bitmap), JPEG, other binary format image data, or a link to the image data. In addition to the above-described recognition information, the “voice recognition result” included in the operation information may be voice data itself or a link to the recognition information or voice data.

  The implementation menu storage unit 132 stores information for calculating a proficiency level indicating a proficiency level related to the predetermined motion of the target person. For example, the implementation menu storage unit 132 is referred to by a proficiency level calculation unit 142A described later. For example, the execution menu storage unit 132 is registered in advance by an operator of the medical information processing apparatus 100.

  FIG. 7 is a diagram illustrating an example of information stored in the implementation menu storage unit 132 according to the first embodiment. As illustrated in FIG. 7, the execution menu storage unit 132 stores information in which the execution menu, the unskilled operation information, and the addition skill level are associated with each other. Here, the “execution menu” is a rehabilitation event performed by the subject. “Unexperienced operation information” is operation information representing an operation performed by an unskilled person on the corresponding execution menu. The “added proficiency level” is a value added to the proficiency level when the proficiency level is calculated by the proficiency level calculation unit 142A. The type of rehabilitation is an example of a predetermined operation.

  As illustrated in FIG. 7, the execution menu storage unit 132 includes an execution menu “squat training”, unskilled movement information “knees appearing more than 5 cm ahead of the ankle”, and additional learning level “−1”. Is stored. That is, when the subject performs squat training, if the subject's knees are more than 5 cm ahead of the ankle when the subject performs squat training, the skill level calculation unit 142A sets the subject's additional skill level to “ It stores that "-1" is added. For example, when the subject faces the motion information collecting unit 10, z of the subject's knee (joint 3 n or 3 r) The determination is made according to whether or not the coordinate value is 5 cm or more smaller than the z-coordinate value of the ankle (joint 3o or 3s). In addition, the execution menu storage unit 132 includes an execution menu “jump training” and unfamiliar movement information “within 1 second after jump landing, the knee has entered 5 cm or more inside (inner thigh). Or within 1 second after jump landing. If the knee is 5 cm or more outside (the crotch) ”, the information associated with the additional proficiency“ −1 ”is stored. That is, when the subject performs jump training, the execution menu storage unit 132 moves the subject's knees inward by 5 cm or more within 1 second after the jump landing, or the subject's knee within 1 second after the jump landing. When the movement of the knees going out by 5 cm or more is performed, it is stored that “−1” is added to the proficiency level of the subject by the proficiency level calculation unit 142A. This inexperienced movement information “the knee enters 5 cm or more inward (inner thigh) within 1 second after jump landing” or “the knee comes out 5 cm or more outside in 1 second after jump landing” When facing the motion information collection unit 10, the knee (joint 3n or 3r) at the time when the z coordinate value of the subject's foot (joint 3p or 3t) becomes 0 (z coordinate on the floor surface). This is determined by comparing the value of the x coordinate with the value of the x coordinate of the knee (joint 3n or 3r) one second after that. Specifically, when the right knee (joint 3n) is used, when the value of the x coordinate is a value larger by 5 cm or more, it is determined that the inner crotch is a value smaller by 5 cm or more. Similarly, the execution menu storage unit 132 stores information that associates the execution menu, the unskilled operation information, and the addition proficiency level with respect to the other execution menus.

  The implementation menu storage unit 132 illustrated in FIG. 7 is an example, and the embodiment is not limited thereto. For example, as the unskilled operation information stored in the execution menu storage unit 132, an arbitrary item may be registered by the operator. Further, an arbitrary value may be registered by the operator as the addition proficiency level stored in the execution menu storage unit 132.

  The dangerous action storage unit 133 stores information for calculating a degree of danger that represents the degree of danger in the predetermined action of the subject person. For example, the dangerous motion storage unit 133 is referred to by a risk level calculation unit 142B described later. For example, the execution menu storage unit 132 is registered in advance by an operator of the medical information processing apparatus 100.

  FIG. 8 is a diagram illustrating an example of information stored in the implementation menu storage unit 132 according to the first embodiment. As illustrated in FIG. 8, the dangerous action storage unit 133 stores information in which dangerous action information is associated with an addition risk level. Here, the “dangerous motion information” is motion information indicating the motion of the target person who suggests that the target person may fall into a dangerous situation. For example, the state in which the target person is likely to fall or the target person is excessive This is operation information indicating a state of fatigue. The “addition risk level” is a value added to the risk level when the risk level is calculated by the risk level calculation unit 142B.

  As illustrated in FIG. 8, the dangerous motion storage unit 133 associates the dangerous motion information “the ground contact point of the foot and the center of gravity of the body are greatly shifted in the horizontal direction” and the added risk “+3”. Remember. That is, the dangerous motion storage unit 133 adds “+3” to the risk level of the subject by the risk level calculation unit 142B when the ground contact point of the subject's foot and the center of gravity of the body are greatly shifted in the horizontal direction. Remember that. This dangerous motion information “the ground contact point of the foot and the center of gravity of the body are greatly shifted in the lateral direction” means that the z coordinate value of the subject's foot (joint 3p or 3t) is 0 (z on the floor surface), for example. In the case of (coordinate), the determination is made according to whether or not the horizontal distance between the foot sole and the waist (joint 3d) is 30 cm or more. Further, the dangerous motion storage unit 133 stores information that associates the dangerous motion information “knee vibrates in small increments” with the addition risk “+1”. In other words, the dangerous motion storage unit 133 stores that “+1” is added to the risk level of the subject by the risk level calculation unit 142B when an operation that the subject's knee vibrates in small increments is performed. This dangerous motion information “the knee vibrates in small increments” is, for example, when the subject faces the motion information collecting unit 10 and the displacement of the x coordinate value of the subject's knee (joint 3n or 3r) is a threshold value. It is determined according to whether or not it is above. Similarly, the dangerous action storage unit 133 stores information that associates the dangerous action information with the addition risk level for the other dangerous action information.

  Note that the dangerous motion storage unit 133 illustrated in FIG. 8 is an example, and the embodiment is not limited thereto. For example, as the dangerous motion information stored in the dangerous motion storage unit 133, any item may be registered by the operator. Further, an arbitrary value may be registered by the operator as the addition risk stored in the dangerous motion storage unit 133.

  The laziness action storage unit 134 stores information for calculating a degree of laziness that represents the degree to which the subject performs a predetermined action. For example, the negligence operation storage unit 134 is referred to by a later-described neglect level calculation unit 142C. Further, for example, the negligence operation storage unit 134 is registered in advance by an operator of the medical information processing apparatus 100.

  FIG. 9 is a diagram illustrating an example of information stored in the negligence operation storage unit 134 according to the first embodiment. As shown in FIG. 9, the laziness operation storage unit 134 stores information in which the execution menu, the laziness operation information, and the addition laziness level are associated with each other. Here, the “lazy action information” is action information that represents an action when the subject does not perform the corresponding action menu with the correct action (form) and is omitted. The “additional negligence level” is a value added to the negligence level when the negligence level calculation unit 142C calculates the negligence level.

  As shown in FIG. 9, the lazy action storage unit 134 includes an execution menu “squat training”, lazy action information “the waist height for 10 seconds has not changed by 20 cm or more”, and the added negligence level “+1”. Is stored. That is, when the subject performs squat training, if the subject's waist height has not changed by 20 cm or more, the negligence calculating unit 142C adds “+1” to the subject's neglect. Is stored. This negligence action information “the waist height has not changed by 20 cm or more” is determined, for example, depending on whether the y coordinate value of the subject's waist (joint 3d) has changed by 20 cm or more in 10 seconds. Is done. Further, the negligence action storage unit 134 stores information in which the execution menu “jump training”, the negligence action information “the foot is not separated from the floor surface”, and the addition neglect degree “+1” are associated with each other. That is, in the case where the subject performs jump training, the negligence motion storage unit 134 adds “+1” to the subject's negligence by the neglect level calculation unit 142C if the subject's feet are not separated from the floor surface. I remember that. This negligence action information “the foot is not separated from the floor surface” is, for example, whether the y coordinate value of the subject's foot (joint 3p or 3t) is greater than the floor surface by 10 cm or more in 10 seconds. It is determined according to Similarly, for the other implementation menus, the negligence operation storage unit 134 stores information in which the implementation menu, the negligence operation information, and the addition negligence level are associated with each other.

  Note that the negligent action storage unit 134 illustrated in FIG. 9 is an example, and the embodiment is not limited thereto. For example, arbitrary items may be registered by the operator as the idle action information stored in the idle action storage unit 134. In addition, an arbitrary value may be registered by the operator for the addition negligence stored in the negligence operation storage unit 134.

  The involvement information storage unit 135 stores information for calculating the degree of involvement representing the degree of involvement from the leader to the subject. The involvement mentioned here is not limited to the guidance of the instructor by the instructor, but the instructor chats with the instructor to relax the instructor, or the instructor encourages the subject by tapping the subject's shoulder. To do. For example, the participation information storage unit 135 is referred to by an after-mentioned participation degree calculation unit 142D. For example, the participation information storage unit 135 is registered in advance by an operator of the medical information processing apparatus 100.

  FIG. 10 is a diagram illustrating an example of information stored in the participation information storage unit 135 according to the first embodiment. As illustrated in FIG. 10, the participation information storage unit 135 stores information in which the participation condition is associated with the addition participation degree. Here, “participation information” is information indicating that the leader is involved in the target person. The “additional participation degree” is a value added to the participation degree when the participation degree is calculated by the participation degree calculation unit 142D.

  As illustrated in FIG. 10, the participation information storage unit 135 stores information in which the participation information “call the name of the subject” is associated with the addition participation degree “+1”. That is, the participation information storage unit 135 stores that “+1” is added to the degree of participation of the subject by the degree-of-interest calculation unit 142D when the name of the subject is called. This participation information “call the name of the subject” is determined depending on whether or not a speech recognition result in which the name of the subject is recognized as a word has been acquired. Further, the participation information storage unit 135 stores information in which the participation information “performs the target person for 10 seconds or more” and the added participation degree “+3” are associated with each other. That is, the participation information storage unit 135 stores that “+3” is added to the degree of participation of the subject by the degree-of-interest calculation unit 142D when the subject is called for 10 seconds or more. This participation information “call the subject for 10 seconds or more” is determined depending on whether the time required for the speech recognition result in which the name of the subject is recognized as a word is 10 seconds or more. . In addition, the participation information storage unit 135 stores information in which the participation information “the leader approaches the subject” is associated with the added participation degree “+3”. In other words, the participation information storage unit 135 stores that “+3” is added to the degree of participation of the subject by the degree of involvement calculation unit 142D when the instructor approaches the subject. This participation information “the leader approaches the target person” is whether or not a person wearing a white coat (detecting the white coat by pattern matching) is detected in the shooting area of the motion information collection unit 10 that takes a picture of the target person. It is determined accordingly. In addition, the participation information storage unit 135 stores information in which the participation information “directly instruct the subject (the instructor contacts the subject)” is associated with the additional degree of participation “+5”. In other words, the participation information storage unit 135 adds “+5” to the degree of participation of the subject by the degree-of-interest calculation unit 142D when the leader directly instructs the subject (the leader contacts the subject). Remember. This participation information “directly instruct the subject (the instructor contacts the subject)” means, for example, that the hand (joint 3h or 3l) of a person wearing a white coat is within 10 cm from any joint of the subject Judgment is made according to whether or not it is approaching. In addition, the participation information storage unit 135 similarly stores information in which the participation information is associated with the addition participation degree for the other pieces of participation information.

  Note that the participation information storage unit 135 illustrated in FIG. 10 is an example, and the embodiment is not limited thereto. For example, as the participation information stored in the participation information storage unit 135, any item may be registered by the operator. In addition, an arbitrary value may be registered by the operator for the addition participation degree stored in the participation information storage unit 135.

  The warning condition storage unit 136 stores a warning condition for determining whether or not a warning is required for the instructor. This warning is given to notify the leader that guidance for a certain subject is necessary. For example, the warning condition storage unit 136 is referred to by the determination unit 143 described later. Further, for example, the warning condition storage unit 136 is registered in advance by an operator of the medical information processing apparatus 100.

  FIG. 11 is a diagram illustrating an example of information stored in the warning condition storage unit 136 according to the first embodiment. As shown in FIG. 11, the warning condition storage unit 136 stores information in which parameters and warning conditions are associated with each other. Here, the “parameter” is a parameter for determining whether or not guidance to the subject is unnecessary, and is, for example, a proficiency level, a risk level, a negligence level, and an involvement level. The “warning condition” is a threshold value of a corresponding parameter set to determine whether or not guidance for the subject is unnecessary.

  As illustrated in FIG. 11, the warning condition storage unit 136 stores information in which a parameter “skill level” is associated with a warning condition “less than −10”. That is, the warning condition storage unit 136 stores that it is determined that guidance for the subject person is necessary when the proficiency level calculated for each subject person is less than −10. Further, the warning condition storage unit 136 stores information in which the parameter “risk level” is associated with the warning condition “+10 or more”. That is, the warning condition storage unit 136 stores that it is determined that guidance for the target person is necessary when the degree of risk calculated for each target person is +10 or more. Further, the warning condition storage unit 136 stores information in which the parameter “degree of neglect” is associated with the warning condition “+10 or more”. That is, the warning condition storage unit 136 stores that it is determined that guidance for the target person is necessary when the degree of laziness calculated for each target person is +10 or more. Further, the warning condition storage unit 136 stores information in which the parameter “degree of participation” is associated with the warning condition “less than −10”. That is, the warning condition storage unit 136 stores that it is determined that guidance for the subject person is necessary when the degree of participation calculated for each subject person is less than −10. In addition, when other index values are used, the warning condition storage unit 136 may store the index value and a warning condition according to the index value.

  Note that the warning condition storage unit 136 illustrated in FIG. 11 is an example, and the embodiment is not limited thereto. For example, as the parameters stored in the warning condition storage unit 136, any item may be registered by the operator. Further, an arbitrary value may be registered by the operator as the warning condition stored in the participation information storage unit 135.

  Returning to the description of FIG. In the medical information processing apparatus 100, the control unit 140 includes an acquisition unit 141, a calculation unit 142, a determination unit 143, and an output control unit 144.

  The acquisition unit 141 acquires, for each target person, motion information representing the motion of the target person for a plurality of target persons who perform predetermined actions. For example, the acquisition unit 141 acquires the operation information collected by the operation information collection unit 10 and stored in the operation information storage unit 131. For example, the acquisition unit 141 acquires color image information, distance image information, voice recognition results, skeleton information, and the like stored for each frame by the motion information storage unit 131. For example, the acquisition unit 141 acquires all color image information, distance image information, and skeleton information related to a series of operations in squat training of the subject.

  The calculation unit 142 calculates an index value indicating the implementation status of the predetermined action for each target person based on the action information for each target person. For example, as the index value, the calculation unit 142 adjusts the proficiency level indicating the level of proficiency of the target person regarding the predetermined motion, the risk level indicating the degree of danger in the target user's predetermined motion, and the target person appropriately adjusting the predetermined motion. At least one of the negligence levels indicating the degree of going is calculated. In addition, for example, the calculation unit 142 further calculates the degree of involvement representing the degree of involvement from the leader to the subject for each subject.

  For example, the calculation unit 142 calculates the proficiency level, the risk level, the negligence level, and the participation level for each target person using the information stored in the motion information storage unit 131. Specifically, the calculation unit 142 calculates the proficiency level, the risk level, the negligence level, and the participation level for each of the target people A to D using the motion information of the target people A to D.

  For example, as illustrated in FIG. 5, the calculation unit 142 includes a proficiency level calculation unit 142A, a risk level calculation unit 142B, a neglect level calculation unit 142C, and an involvement level calculation unit 142D.

  The proficiency level calculation unit 142A calculates the proficiency level for each target person. For example, the proficiency level calculation unit 142 </ b> A accepts a designation operation for designating an execution menu performed by the target person from the operator via the input unit 120. Then, the proficiency level calculation unit 142A refers to the execution menu storage unit 132 and acquires unskilled operation information corresponding to the specified execution menu. Then, the proficiency level calculating unit 142A determines whether or not the motion information of each subject acquired by the acquiring unit 141 corresponds to unskilled motion information. Then, when the motion information of the subject person corresponds to the unskilled motion information, the skill level calculation unit 142A adds the addition skill level corresponding to the unskilled motion information to the skill level of the subject person. Then, the proficiency level calculation unit 142A outputs the proficiency level of each subject to the determination unit 143.

  As an example, the case where the subject A performs squat training will be described. In this case, the proficiency level calculation unit 142A accepts a designation operation for designating that the subject person A performs squat training. Then, the proficiency level calculation unit 142A refers to the implementation menu storage unit 132, and acquires the unskilled operation information “knee comes out 5 cm or more ahead of the ankle” corresponding to the squat training. The proficiency level calculating unit 142A determines whether or not the acquired motion information of each subject corresponds to the unskilled motion information every time the motion information of the subject A is acquired by the acquiring unit 141. Specifically, the proficiency level calculating unit 142A determines that the z coordinate value of the right knee (joint 3n) of the subject A is the right ankle (joint 3o) when the subject faces the motion information collecting unit 10. It is determined whether or not the value is 5 cm or more smaller than the z coordinate value. When the z-coordinate value of the subject's A right knee is smaller than the z-coordinate value of the right ankle by 5 cm or more, the proficiency level calculating unit 142A sets the squat addition proficiency “−1” to the subject A's. Add to proficiency. Then, the proficiency level calculation unit 142A outputs the proficiency level of the subject A to the determination unit 143. In the proficiency level calculation unit 142A, “0” is set as the initial value of proficiency level of each subject person. Moreover, although the case where the value of the z coordinate of the subject's right knee and right ankle is used is illustrated here, the embodiment is not limited to this. For example, the proficiency level calculation unit 142A may The value of the z coordinate of the left ankle may be used. Further, for example, the proficiency level calculation unit 142A may use the values of the z coordinates of the knees and ankles of both feet. In this case, the proficiency level calculating unit 142A may add the additional proficiency level when one of the knees is 5 cm or more ahead of the ankle, and the knees of both legs are 5 cm or more ahead of the ankle. If you are out, you may add additional proficiency.

  The risk level calculation unit 142B calculates the risk level for each target person. For example, the risk level calculation unit 142B refers to the dangerous motion storage unit 133 and determines whether the motion information of each subject acquired by the acquisition unit 141 corresponds to the dangerous motion information. Then, when the motion information of the target person corresponds to the dangerous motion information, the risk level calculation unit 142B adds the addition risk level corresponding to the dangerous motion information to the risk level of the target person. Then, the risk level calculation unit 142B outputs the risk level of each subject person to the determination unit 143.

  As an example, the risk level calculation unit 142B refers to the dangerous action storage unit 133 and acquires all dangerous action information stored in the dangerous action storage unit 133. Then, every time the acquisition unit 141 acquires the motion information of the subject person A, the risk level calculation unit 142B determines whether or not the acquired motion information of the subject person A corresponds to the dangerous motion information. Specifically, when the z-coordinate value of the foot (joint 3p or 3t) of the subject A is 0 (the z-coordinate of the floor surface), the risk level calculation unit 142B determines the foot and waist (joint 3d). And calculate the horizontal distance. Then, when the calculated horizontal distance is 30 cm or more, the risk level calculation unit 142B adds the addition risk level “+3” corresponding to the dangerous motion information to the risk level of the subject person A. Then, the risk level calculation unit 142B outputs the proficiency level of the subject A to the determination unit 143. Note that “0” is set as the initial value of the risk level of each subject person in the risk level calculation unit 142B. Further, the risk level calculation unit 142B is not limited to the above example. For example, the other person's motion information stored in the dangerous motion storage unit 133 is acquired in the same manner. Is determined to correspond to each dangerous motion information, and the risk level of the subject person A is calculated according to the determination result.

  The negligence level calculation unit 142C calculates the neglect level for each target person. For example, the laziness calculation unit 142 </ b> C receives a designation operation for designating an execution menu performed by the target person from the operator via the input unit 120. Then, the laziness calculation unit 142C refers to the laziness operation storage unit 134, and acquires laziness operation information corresponding to the designated execution menu. Then, the laziness calculation unit 142C determines whether or not the motion information of each subject acquired by the acquisition unit 141 corresponds to the laziness motion information. Then, when the target person's motion information corresponds to the negligent motion information, the negligence level calculation unit 142C adds an addition negligence level corresponding to the negligent motion information to the target person's neglect level. Then, the laziness calculation unit 142C outputs the laziness level of each subject to the determination unit 143.

  As an example, the case where the subject A performs squat training will be described. In this case, the laziness calculation unit 142C accepts a designation operation that designates that the subject person A performs squat training. Then, the laziness calculation unit 142C refers to the implementation menu storage unit 132, and acquires laziness action information “the waist height has not changed by 20 cm or more” corresponding to the squat training. Then, each time the acquisition unit 141 acquires the motion information of the subject person A, the negligence level calculation unit 142C determines whether or not the acquired motion information of each subject person corresponds to the negligence motion information. Specifically, when the subject faces the motion information collection unit 10, the laziness calculation unit 142 </ b> C selects a frame group from the acquired subject motion information frame to 10 seconds before the motion information storage unit 131. Get from. Then, the laziness calculation unit 142C determines whether or not the difference between the maximum value and the minimum value of the y coordinate of the waist (joint 3d) of the subject A is 10 cm or more in 10 seconds. Then, if the difference between the maximum value and the minimum value of the y-coordinate of the waist of the subject A is not more than 20 cm, the laziness calculation unit 142C adds the squat addition laziness “+1” to the laziness of the subject A. . Then, the laziness calculation unit 142C outputs the laziness level of the subject A to the determination unit 143. In addition, “0” is set as an initial value of each subject's neglect level in the neglect level calculating unit 142C.

  The participation degree calculation unit 142D calculates the participation degree for each target person. For example, the participation degree calculation unit 142D refers to the participation information storage unit 135 and determines whether or not the motion information of each subject acquired by the acquisition unit 141 corresponds to the participation information. Then, when the motion information of the target person corresponds to the participation information, the participation degree calculation unit 142D adds the addition participation degree corresponding to the participation information to the participation degree of the target person. Then, the degree of participation calculation unit 142D outputs the degree of participation of each subject to the determination unit 143.

  As an example, the participation degree calculation unit 142D refers to the participation information storage unit 135 and acquires all the participation information stored in the participation information storage unit 135. Then, each time the acquisition unit 141 acquires the motion information of the subject person A, the participation degree calculation unit 142D determines whether the acquired motion information of the subject person A corresponds to the participation information. Specifically, when the speech recognition result in which the name “Mr. A” of the subject person A is recognized as a word is acquired, the participation degree calculation unit 142D corresponds to the participation information “call the name of the subject person”. The addition participation degree “+1” is added to the participation degree of the target person A. In addition, when the person wearing the white coat is detected from the color image information of the subject A by pattern matching, the degree of involvement calculating unit 142D adds the degree of participation “corresponding to the instructor approaches the subject”. +3 ”is added to the degree of participation of the subject person A. Then, the degree of participation calculation unit 142D outputs the degree of participation of the subject A to the determination unit 143. Note that “0” is set as the initial value of the degree of participation of each target person in the degree-of-engagement calculation unit 142D. In addition, the degree-of-involvement calculation unit 142D is not limited to the above example. For example, similarly, the other person's A motion information stored in the dangerous motion storage unit 133 is acquired. Is determined to correspond to each piece of participation information, and the degree of participation of the subject person A is calculated according to the determination result.

  Note that the present invention is not limited to the above example. For example, the participation information “the leader approaches the subject” may be detected by another method. For example, the participation degree calculation unit 142D may detect the face image of the leader from the color image information of the target person A using the face image of the leader. Further, for example, if the operation information collection unit 10A that captures the subject A can detect an RFID (Radio Frequency Identification) tag, the participation degree calculation unit 142D collects the operation information from the RFID tag possessed by the instructor. When detected by the part 10A, it may be detected that the instructor has approached the subject A. Further, the present invention is not limited to this, and when the other person is detected in the color image information of the subject A, the involvement degree calculation unit 142D detects this as a leader if the person is not another subject. May be. Specifically, the participation degree calculation unit 142D determines the target person by detecting training clothes by pattern matching, performing face recognition, or detecting a person performing a rehabilitation operation. It is good also as a structure which determines with it being a leader if it is not a target person.

  In addition, the participation degree calculation unit 142D subtracts a predetermined value from the calculated participation degree as time passes. This is because as time passes, the subject feels less involved with the leader. For example, the participation degree calculation unit 142D stores the time when the participation degree is updated. Then, the degree-of-engagement calculation unit 142D adds “−1” to the degree of participation of the subject every time one minute has elapsed since the previous degree of participation was updated.

  As described above, the calculation unit 142 uses the proficiency level calculation unit 142A, the risk level calculation unit 142B, the negligence level calculation unit 142C, and the involvement level calculation unit 142D to determine the proficiency level, the risk level, the negligence level, and the involvement level. Calculate for each. Then, the calculation unit 142 outputs the calculated proficiency level, risk level, neglect level, and participation level for each target person to the determination unit 143.

  Here, the case where the calculation unit 142 calculates the proficiency level, the risk level, the negligence level, and the participation level has been described, but the embodiment is not limited thereto. For example, the calculation unit 142 may calculate at least one of the proficiency level, the risk level, and the neglect level. Specifically, the calculation unit 142 may calculate only the proficiency level, or may calculate the risk level and the negligence level.

  The determination unit 143 sets, for each target person, a teaching priority level indicating which target person is to be prioritized based on at least one of the calculated proficiency level, risk level, and negligence level for each target person. decide. For example, the determination unit 143 receives the proficiency level, the risk level, the negligence level, and the participation level for each target person from the calculation unit 142. And the determination part 143 determines whether each parameter of the proficiency degree of each received subject, a risk level, a negligence level, and an engagement degree corresponds to warning conditions, respectively. And the determination part 143 determines a guidance priority according to the parameter applicable to warning conditions.

  FIG. 12 is a diagram for explaining processing of the determination unit 143 according to the first embodiment. FIG. 12 shows an example of a determination result for determining whether each of the proficiency level, the risk level, the neglect level, and the engagement level for each target person satisfies the warning condition. In FIG. 12, “X” marks exemplified in the items of each parameter of proficiency level, risk level, negligence level, and involvement level indicate that the parameters of each subject correspond to warning conditions, and “○” mark indicates Indicates that the parameters of each subject did not meet the warning conditions. Each parameter is preset with a priority. This priority is information that defines which parameter among the proficiency level, the risk level, the negligence level, and the involvement level is to be prioritized when the determination unit 143 determines the teaching priority level. Parameters that should be prioritized in alphabetical order from “A”. In the example of FIG. 12, the parameter that should be given the highest priority is the risk of the priority “A”, then the proficiency of the priority “B”, the degree of involvement of the priority “C”, and the neglect of the priority “D”. Degree and priority are set. The guidance priority is information indicating which subject is given priority for guidance, and indicates, for example, subjects who should be prioritized in alphabetical order from the guidance priority “A”. Since the risk level may directly cause damage such as injury or illness of the target person, as illustrated in FIG. 12, the priority level of the risk level is higher than the priority levels of other parameters. High is preferred.

  As illustrated in FIG. 12, the determination unit 143 refers to the warning condition storage unit 136 illustrated in FIG. 11, and determines whether the received proficiency level, risk level, negligence level, and engagement level parameters correspond to the warning condition. Is determined for each subject. As an example, the determination unit 143 receives the proficiency level “−13”, the risk level “10”, the negligence level “2”, and the involvement level “2” of the subject A. In this case, since the received proficiency level “−13” is “less than −10”, the determination unit 143 determines that the proficiency level of the target person A satisfies the warning condition. Further, the determination unit 143 determines that the risk level of the target person A falls under the warning condition because the received risk level “10” is “10 or more”. The determination unit 143 determines that the negligence level of the subject person A does not correspond to the warning condition because the accepted negligence level “2” is not “10 or more”. Further, the determination unit 143 determines that the proficiency level of the subject person A does not correspond to the warning condition because the received degree of participation “2” is not “less than −10”. And the determination part 143 determines the priority of the parameter with the highest priority among the parameters applicable to warning conditions as a guidance priority of a subject. In this case, since the priority of the parameter corresponding to the warning condition is the proficiency level of priority “B” and the risk level of priority “A”, the determination unit 143 targets the priority level “A” of the risk level. It is determined as the instruction priority of person A. In addition, the determination unit 143 determines whether or not each parameter of the proficiency level, the risk level, the negligence level, and the participation level of each target person corresponds to the warning condition for other target persons, The priority of the parameter corresponding to the warning condition is determined as the teaching priority. If all parameters do not meet the warning condition as in the target person C illustrated in FIG. 12, the target person is given a priority (for example, “E”) lower than the priority set in the parameter. C is determined as the instruction priority.

  Note that the method by which the determining unit 143 determines the teaching priority is not limited to the above example. For example, the determination unit 143 assigns a score to the priority of each parameter, and totals the score of the parameter corresponding to the warning condition. Then, the determination unit 143 may determine the instruction priority in descending order of the total score.

  The output control unit 144 outputs support information for supporting a leader who teaches a plurality of subjects based on the guidance priority. For example, the output control unit 144 outputs, to the output unit 110, information that ranks the target person according to the high instruction priority as support information. Further, for example, the output control unit 144 outputs information indicating parameters corresponding to the warning condition among the parameters of the proficiency level, the risk level, the neglect level, and the participation level to the output unit 110 as support information.

  FIG. 13 is a diagram for explaining a display screen displayed by the output control unit 144 according to the first embodiment. As illustrated in FIG. 13, the display screen of the output unit 110 includes display areas 21 to 25. Among these, in the display areas 21 to 24, color images (moving images) of the subjects A to D photographed by the motion information collection units 10A to 10D are displayed in substantially real time. In the display area 25, the names of the subjects are displayed in descending order of instruction priority. FIG. 13 shows an example of a display screen displayed when the process illustrated in FIG. 12 is performed by the determination unit 143.

  As illustrated in FIG. 13, the output control unit 144 causes the output unit 110 to display display areas 21 to 24 that display the color images of the subjects A to D, respectively. Here, the output control unit 144 displays, for each target person, information indicating parameters corresponding to the warning condition in the display areas 21 to 24 for displaying each target person. For example, when the proficiency level of the subject person corresponds to the warning condition, the output control unit 144 causes the display areas 21 to 24 to display an icon “needs guidance”. Further, the output control unit 144 displays an icon “danger” in the display areas 21 to 24 when the risk level of the subject person corresponds to the warning condition. Further, the output control unit 144 displays an icon of “laziness” in the display areas 21 to 24 when the subject's degree of neglect corresponds to the warning condition. In addition, when the degree of participation of the subject corresponds to the warning condition, the output control unit 144 displays an icon “Letting for a long time” in the display areas 21 to 24. In the example illustrated in FIG. 13, the output control unit 144 causes the display area 21 to display “danger” and “need guidance” icons because the target person A satisfies the warning conditions of proficiency and risk. Further, the output control unit 144 causes the display area 22 to display icons of “lazy” and “left unattended for a long time” because the target person B corresponds to the warning condition of neglect and degree of involvement. Further, the output control unit 144 does not display any icon in the display area 23 because the target person C does not meet any warning condition. Further, the output control unit 144 displays an icon “needs guidance” in the display area 24 because the target person D corresponds to the warning condition of the proficiency level.

  In addition, the output control unit 144 causes the output unit 110 to display the display area 25 that displays the names of the subjects in descending order of instruction priority. Here, in FIG. 12, the instruction priority of the subject person A is “A”, the instruction priority of the subject person B is “C”, and the instruction priority of the subject person C is “E”. The instruction priority of the person D is “B”. In this case, the output control unit 144 rearranges and displays the names of the subjects in descending order of instruction priority. Specifically, the output control unit 144 displays the names of the target persons in the display area 25 in the order of “target person A”, “target person D”, “target person B”, and “target person C”. Moreover, the output control part 144 displays the icon which shows a parameter with a high priority among the parameters applicable to warning conditions about each subject in the display area 25. FIG. Specifically, the output control unit 144 displays a “danger” icon next to the name of the subject person A because the subject person A satisfies the warning conditions for the proficiency level and the danger level. Further, the output control unit 144 displays a “long time left” icon next to the name of the subject person B because the subject person B satisfies the warning conditions of the neglect level and the degree of involvement. Further, the output control unit 144 does not display any icon next to the name of the subject C because the subject C does not meet any warning condition. Further, since the target person D corresponds to the proficiency level warning condition, the output control unit 144 displays a “need guidance” icon next to the name of the target person D.

  As described above, the output control unit 144 provides, as support information, an icon indicating a parameter corresponding to the warning condition among the parameters of the proficiency level, the risk level, the negligence level, and the participation level, and the guidance priority level. A display area 25 in which subjects are ranked is displayed on the output unit 110.

  Note that the present invention is not limited to the above example. For example, the output control unit 144 may output a sound as support information when a warning condition is met. Specifically, the output control unit 144 may output a voice “Mr. A is dangerous” when the risk level of the subject person A meets the warning condition.

  Next, processing of the medical information processing apparatus 100 according to the first embodiment will be described with reference to FIGS. FIG. 14 is a flowchart illustrating an overall processing procedure performed by the medical information processing apparatus 100 according to the first embodiment. FIG. 15 is a flowchart illustrating a procedure of proficiency level calculation processing according to the first embodiment. FIG. 16 is a flowchart illustrating a procedure of risk degree calculation processing according to the first embodiment. FIG. 17 is a flowchart illustrating a procedure of a laziness calculation process according to the first embodiment. FIG. 18 is a flowchart illustrating a procedure of the degree of participation calculation process according to the first embodiment.

  As shown in FIG. 14, in the medical information processing apparatus 100, when the input unit 120 accepts a guidance start instruction to start guidance by a leader (Yes in step S101), the acquisition unit 141 operates a plurality of subjects. Information is acquired for each subject (step S102). The medical information processing apparatus 100 is in a standby state until a guidance start instruction is received (No at step S101).

  Then, the proficiency level calculating unit 142A performs proficiency level calculation processing that is processing for calculating the proficiency level for each target person (step S103). Subsequently, the risk level calculation unit 142B performs a risk level calculation process that is a process for calculating the risk level for each target person (step S104). Subsequently, the negligence level calculation unit 142C performs a negligence level calculation process that is a process of calculating the negligence level for each target person (step S105). Subsequently, the degree-of-engagement calculation unit 142D performs a degree-of-engagement calculation process that is a process for calculating the degree of participation for each target person (step S106). The proficiency level calculation process, the risk level calculation process, the negligence level calculation process, and the participation level calculation process will be described later with reference to FIGS.

  Then, the determination unit 143 determines the teaching priority of each target person using each parameter calculated by the proficiency level calculation process, the risk level calculation process, the neglect level calculation process, and the participation level calculation process (step S107). . For example, the determination unit 143 determines whether each parameter of the proficiency level, the risk level, the neglect level, and the degree of participation for each target person corresponds to the warning condition, and according to the parameter corresponding to the warning condition, Determine teaching priority.

  And the output control part 144 outputs assistance information based on each subject's instruction | indication priority (step S108). For example, the output control unit 144 uses the icon indicating the parameter corresponding to the warning condition among the parameters of the proficiency level, the risk level, the negligence level, and the involvement level as the support information, or the target person according to the high teaching priority. Are displayed on the output unit 110.

  The medical information processing apparatus 100 repeatedly executes the processing from step S102 to step S108 until the input unit 120 receives an instruction to end instruction to end instruction by the instructor (No at step S109). Then, when the medical information processing apparatus 100 receives a guidance end instruction (Yes at Step S109), the medical information processing apparatus 100 ends the process.

  Here, the proficiency level calculation process shown in step S103 of FIG. 14 will be described with reference to FIG. As illustrated in FIG. 15, the proficiency level calculating unit 142A determines whether or not the acquired motion information of each target person corresponds to unskilled motion information each time the acquisition unit 141 acquires the motion information of each target person. Is determined (step S201). Then, when it is determined that the motion information of each subject person corresponds to the unskilled motion information (Yes in step S201), the skill level calculation unit 142A uses the addition skill level corresponding to the unskilled motion information as the subject skill level. (Step S202).

  Then, the proficiency level calculation unit 142A outputs the proficiency level of each target person to the determination unit 143 (step S203), and ends the process. Note that the proficiency level calculation unit 142A proceeds to step S203 when it is determined that the motion information of each subject does not correspond to the unskilled motion information (No in step S201).

  Further, the risk level calculation process shown in step S104 of FIG. 14 will be described with reference to FIG. As illustrated in FIG. 16, the risk level calculation unit 142 </ b> B determines whether or not the acquired motion information of each target person corresponds to the dangerous motion information every time the acquisition unit 141 acquires the motion information of each target person. Is determined (step S301). Then, when it is determined that the motion information of each target person corresponds to the dangerous motion information (Yes in step S301), the risk level calculation unit 142B adds the addition risk level corresponding to the dangerous motion information to the risk level of the target person. (Step S302).

  Then, the risk level calculation unit 142B outputs the risk level of each subject to the determination unit 143 (step S303), and ends the process. If it is determined that the motion information of each subject does not correspond to the dangerous motion information (No at Step S301), the risk level calculation unit 142B proceeds to Step S303.

  Further, the neglect level calculation process shown in step S105 of FIG. 14 will be described with reference to FIG. As shown in FIG. 17, each time the acquisition unit 141 acquires the motion information of each subject, the neglect level calculation unit 142C determines whether or not the acquired motion information of each subject corresponds to the negligence motion information. Is determined (step S401). Then, when it is determined that the motion information of each subject person corresponds to the dangerous motion information (Yes in step S401), the negligence level calculation unit 142C adds the addition negligence level corresponding to the negligence motion information to the negligence level of the subject person. (Step S402).

  Then, the laziness calculation unit 142C outputs the laziness level of each subject person to the determination unit 143 (step S403), and ends the process. In addition, when it is determined that the action information of each subject does not correspond to the action information of negligence (No at Step S401), the degree of neglect calculation unit 142C proceeds to Step S403.

  In addition, with reference to FIG. 18, the degree of participation calculation process shown in step S106 of FIG. 14 will be described. As illustrated in FIG. 18, each time the acquisition unit 141 acquires the motion information of each target person, the participation degree calculation unit 142 </ b> D determines whether the acquired motion information of each target person corresponds to the participation information. Determination is made (step S501). Then, when it is determined that the motion information of each target person corresponds to the participation information (Yes in step S501), the participation degree calculation unit 142D adds the addition participation degree corresponding to the participation information to the participation degree of the target person ( Step S502).

  Then, the participation degree calculation unit 142D subtracts a predetermined value from the calculated participation degree as time elapses (step S503). Then, the degree-of-engagement calculation unit 142D outputs the degree of participation of each subject person to the determination unit 143 (step S504), and ends the process. If it is determined that the motion information of each subject does not correspond to the participation information (No at Step S501), the participation degree calculation unit 142D proceeds to Step S503.

  In addition, said process procedure is an example and embodiment is not limited to this. For example, the medical information processing apparatus 100 may execute the proficiency level calculation process, the risk level calculation process, the negligence level calculation process, and the participation level calculation process in any order of the operator, or perform these processes in parallel. May be executed.

  As described above, the medical information processing apparatus 100 according to the first embodiment acquires, for each target person, motion information representing the motion of the target person for a plurality of target persons who perform predetermined actions. The medical information processing apparatus 100 calculates at least one of the proficiency level, the risk level, and the negligence level for each target person based on the operation information for each target person. The medical information processing apparatus 100 determines the instruction priority for each target person based on at least one of the calculated proficiency level, risk level, and negligence level for each target person. Then, the medical information processing apparatus 100 outputs support information for supporting the instructors who respectively instruct a plurality of subjects based on the instruction priority. For this reason, the medical information processing apparatus 100 can improve the quality of guidance performed by the instructors who respectively instruct a plurality of subjects. For example, the medical information processing apparatus 100 displays information that ranks the target persons according to the high instruction priority as support information on the output unit 110, so that the target person who currently needs instruction is notified to the instructor. can do. Thereby, the leader can support the target person at a necessary timing.

  Further, for example, the medical information processing apparatus 100 causes the output unit 110 to display, as support information, an icon indicating a parameter corresponding to a warning condition among parameters of proficiency level, risk level, neglect level, and participation level. Therefore, the medical information processing apparatus 100 can appropriately notify the instructor of the content of support necessary for each target person. Thereby, the leader can appropriately perform the support necessary for the target person. For example, the leader can prevent the subject from falling by supporting the body of the subject who has lost the balance. In addition, for example, the leader can prevent a decrease in the motivation of the subject by being equally involved with each subject.

(Second Embodiment)
In the first embodiment, the case has been described in which the proficiency level, the risk level, the neglect level, and the involvement level are calculated on a uniform basis for all subjects. However, it is preferable to calculate each parameter using the personal information of the subject. For example, since the risk of injury due to a fall is higher for elderly people than for young people, it is preferable to calculate a higher risk level for elderly people than for young people in situations where they are likely to fall. Therefore, in the second embodiment, a case will be described in which each parameter is calculated using the personal information of the target person.

  The medical information processing apparatus 100 according to the second embodiment has basically the same configuration as the medical information processing apparatus 100 illustrated in FIG. 5, and the storage unit 130 further includes a personal information storage unit 137. Part of the processing in the calculation unit 142 is different. Therefore, in the second embodiment, the description will focus on the points that differ from the first embodiment, and the description of the points having the same functions as the configuration described in the first embodiment will be omitted.

  The personal information storage unit 137 according to the second embodiment stores the personal information of the target person. For example, the personal information storage unit 137 is referred to by the proficiency level calculation unit 142A, the risk level calculation unit 142B, the neglect level calculation unit 142C, and the participation level calculation unit 142D. Information stored in the personal information storage unit 137 is acquired in advance from an electronic medical record system or the like.

  FIG. 19 is a diagram illustrating an example of information stored in the personal information storage unit 137 according to the second embodiment. As illustrated in FIG. 19, the personal information storage unit 137 stores information in which the subject name, age (year), and ADL scale are associated with each other. Here, the “target person name” is information indicating the name of the target person. “Age (year)” is information indicating the age of the target person. The “ADL (Activities of Daily Living) Scale” is a criterion for activities and activities in daily life. For example, if you can perform activities and activities in daily life by yourself, a high score is given, and activities in daily life If the activity requires assistance, a low score is awarded. For example, FIM (Function Independence Measure) or BI (Barthel Index) can be applied as the ADL scale.

  As illustrated in FIG. 19, the personal information storage unit 137 stores the subject name “A”, the age “85”, and the ADL scale “3” in association with each other. That is, the personal information storage unit 137 stores that Mr. A is 85 years old and Mr. A's ADL scale is 3. Also, the personal information storage unit 137 stores the subject name “B”, the age “20”, and the ADL scale “8” in association with each other. In other words, the personal information storage unit 137 stores that Mr. B is 20 years old and his ADL scale is 8. Similarly, the personal information storage unit 137 stores information in which the subject name, age (year), and ADL scale are associated with each other. The information stored in the personal information storage unit 137 is not limited to this, and information such as the height, weight, blood pressure, etc. of the subject may be stored.

  The calculation unit 142 according to the second embodiment further uses the personal information stored in the personal information storage unit 137 that stores the personal information of the subject, and calculates at least one of the proficiency level, the risk level, and the negligence level. Calculate for each target person.

  For example, in the calculation unit 142, the risk level calculation unit 142B calculates the addition risk level for each target person using the dangerous motion storage unit 133 and the personal information storage unit 137. Then, the risk level calculation unit 142B calculates the risk level of each target person using the calculated addition risk level for each target person. In the following, as an example of the case where the calculation unit 142 calculates each parameter using personal information, the risk level calculation unit 142B will describe processing for calculating the risk level using personal information. It is not limited to. For example, the skill level calculation unit 142A, the laziness level calculation unit 142C, and the participation level calculation unit 142D may calculate each parameter using personal information.

  20A and 20B are diagrams for explaining the processing of the calculation unit 142 according to the second embodiment. FIG. 20A illustrates a process in which the calculation unit 142 calculates the addition risk degree for the subject person A, and FIG. 20B illustrates a process in which the calculation unit 142 calculates the addition risk degree for the subject person B.

  As shown in FIG. 20A, the risk level calculation unit 142B refers to the dangerous motion storage unit 133 and the personal information storage unit 137, and stores all the dangerous motion information stored in the dangerous motion storage unit 133 and the individual of the subject A. Get information. Then, the risk degree calculation unit 142B calculates the addition risk degree for the subject person A using the addition risk degree preset in the dangerous action storage unit 133 and the personal information of the subject person A. Specifically, the risk degree calculation unit 142B calculates the addition risk degree for each subject using the formula “preset addition risk degree × (age / 100−ADL scale / 100)”. More specifically, the risk level calculation unit 142B sets the added risk level of “3 × (85) for the dangerous motion information of the subject A“ the ground contact point of the foot and the center of gravity of the body are greatly shifted in the horizontal direction ”. /100−3/100)=2.46 ”. Further, the risk level calculation unit 142B calculates the addition risk level of the dangerous motion information “the knee vibrates in small increments” of the subject A as “1 × (85 / 100−3 / 100) = 0.82”. .

  Further, as shown in FIG. 20B, the risk level calculation unit 142B calculates the addition risk level for the subject person B using the addition risk level preset in the dangerous motion storage unit 133 and the personal information of the subject person B. To do. Specifically, the risk level calculation unit 142B sets the risk level of the subject B's dangerous motion information “the ground contact point of the foot and the center of gravity of the body are greatly shifted in the horizontal direction” as “3 × (20 / 100−8 / 100) = 0.36 ”. Further, the risk level calculation unit 142B calculates the addition risk level of the dangerous motion information “the knee vibrates in small increments” of the subject B as “1 × (20 / 100−8 / 100) = 0.12”. . In addition, although description is abbreviate | omitted here, the risk level calculation part 142B calculates the addition risk level of each object person similarly about another object person.

  As described above, the risk level calculation unit 142B calculates the addition risk level of each target person using the personal information of the target person. Specifically, the risk level calculation unit 142B calculates the addition risk level of each target person so that the risk level increases as the age of the target person increases and decreases as the ADL scale of the target person increases. Then, when the motion information of the target person acquired by the acquisition unit 141 corresponds to the dangerous motion information, the risk level calculation unit 142B adds the calculated addition risk level for each target person to the risk level of the target person. .

  Here, as an example of the case where the calculation unit 142 calculates each parameter using personal information, the case where the risk level calculation unit 142B calculates the addition risk level using personal information has been described. It is not limited to this. For example, the proficiency level calculation unit 142A calculates an additional proficiency level that decreases as the age of the subject increases. Further, for example, the laziness calculation unit 142C calculates the added laziness that increases as the age of the subject person decreases. Further, for example, the degree-of-involvement calculation unit 142D calculates an addition degree of participation that decreases as the age of the subject increases. Further, the above-described expression “preset addition risk level × (age / 100−ADL scale / 100)” is merely an example, and the present invention is not limited to this. For example, the risk level calculation unit 142B may calculate the addition risk level for the target person A using only the age of the target person A, or may use the ADL scale of the target person A only for the target person A. The risk of addition may be calculated.

  As described above, the medical information processing apparatus 100 according to the second embodiment further uses at least one of the proficiency level, the risk level, the negligence level, and the participation level for each target person by further using the personal information of the target person. calculate. That is, since the medical information processing apparatus 100 calculates each parameter according to each target person, the instruction priority can be accurately determined.

(Other embodiments)
Although the first and second embodiments have been described so far, the present invention may be implemented in various different forms other than the above-described embodiments.

(Display only index value)
In the above embodiment, the case where the output control unit 144 outputs support information in the medical information processing apparatus 100 has been described. However, the embodiment is not limited thereto. For example, the output control unit 144 may output the index value calculated by the calculation unit 142. As a specific example, the output control unit 144 may display the degree of risk on the output unit 110 for each subject. Thereby, a leader becomes easy to judge the priority of guidance by browsing the danger degree for every displayed subject. For this reason, the medical information processing apparatus 100 can improve the quality of the guidance performed by the instructor.

(Highlighting)
In the above embodiment, the case where the medical information processing apparatus 100 displays information that ranks the target person according to the high instruction priority as the support information has been described. However, the embodiment is limited to this. is not. For example, the medical information processing apparatus 100 may output support information that highlights information related to the subject with the highest instruction priority.

  FIG. 21 is a diagram for explaining the processing of the output control unit 144 according to another embodiment. FIG. 21 illustrates a case where the medical information processing apparatus 100 includes a main display and a second display as the output unit 110. In the example illustrated in FIG. 21, the target person with the highest instruction priority is the target person A.

  As shown in FIG. 21, in the medical information processing apparatus 100, the output control unit 144 causes the second display to display a display screen similar to the display screen illustrated in FIG. Further, the output control unit 144 displays the color image of the subject A having the highest instruction priority among the subjects A to D on the display screen 26 of the main display. Note that the method by which the output control unit 144 highlights information related to the subject with the highest instruction priority is not limited to this. For example, the output control unit 144 may display the display area 21 of the subject A in a color tone different from the display areas 22 to 24 of the other subjects in FIG. Further, for example, the output control unit 144 may enlarge and display the display area 21 of the subject A so as to be larger than the display areas 22 to 24 of other subjects.

  In this way, the medical information processing apparatus 100 outputs support information that highlights information related to the subject with the highest instruction priority. For this reason, the medical information processing apparatus 100 can clearly indicate to the instructor the subject who currently needs the most guidance.

(Display attention image)
Further, for example, the medical information processing apparatus 100 may further display an attention image (summary image) that is an image at a time when a change that should be noticed by the subject occurs. For example, the medical information processing apparatus 100 causes the output unit 110 to display a color image of the subject person A when the degree of risk of the subject person A meets the warning condition as the attention image.

  22A and 22B are diagrams for explaining processing of the output control unit 144 according to another embodiment. In FIG. 22A, the time change of the risk level of the subject person A after the subject person A starts rehabilitation is shown. In FIG. 22A, the vertical axis indicates the risk level of the subject A, and the horizontal axis indicates time. FIG. 22B shows an example of the display area 21 for displaying the subject A. The display area 21 includes a color image 27 of the subject A at the time when the degree of danger of the subject A meets the warning condition as the attention image.

  As illustrated in FIGS. 22A and 22B, the output control unit 144 acquires the time corresponding to the warning condition from the determination unit 143 when the risk level of the subject A corresponds to the warning condition. Then, the output control unit 144 causes the display area 21 to display the color image information 27 of the subject A at the acquired time. For example, the output control unit 144 calculates a time difference between the time corresponding to the warning condition and the current time, and displays the calculated time difference together with the color image 27. Specifically, the output control unit 144 displays “15 seconds” together with the color image 27 when the time difference between the time corresponding to the warning condition and the current time is 15 seconds.

  Here, although the case where the medical information processing apparatus 100 displays the color image of the subject at the time when the degree of risk of the subject meets the warning condition as the attention image has been described, the embodiment is limited to this. It is not something. For example, the medical information processing apparatus 100 may display a color image of the subject at the time when other index values such as the proficiency level, the neglect level, and the degree of participation meet the warning condition. The displayed color image is not limited to a still image, and may be a moving image for a predetermined time (for example, 5 seconds) including a time point when the index value meets the warning condition.

  In addition, for example, the target image is not limited to an image at the time when the index value of the target person meets the warning condition, and may be an image at the time when the index value of the target person changes most. Specifically, the medical information processing apparatus 100 may calculate the inclination of the index value from the graph illustrated in FIG. 22A and display an image at the time when the calculated inclination is maximum as the attention image.

  In this way, the medical information processing apparatus 100 causes the output unit 110 to display an attention image that is an image at a time when a change that should be noticed by the subject has occurred. For this reason, the instructor can easily browse the image to be noticed even if the subject's image is missed when a change to be noticed by the subject has occurred.

(Creation of instruction report)
In addition, for example, the medical information processing apparatus 100 may output a guidance report in which the main points in the guidance of each subject person are extracted after the guidance by the leader is completed. For example, the medical information processing apparatus 100 causes the output unit 110 to display, for each target person, information including the attention image and the time change of the index value as a guidance report.

  FIG. 23 is a diagram for explaining processing of the output control unit 144 according to another embodiment. FIG. 23 illustrates a case where a guidance report is displayed on the display screen of the output unit 110. As shown in FIG. 23, on the display screen of the output unit 110, a guidance report 28 including a color image 27 32 minutes after the start of the subject A and a graph showing the change in the degree of risk over time is displayed. In addition, on the display screen of the output unit 110, a guidance report 30 including a color image 29 two minutes after the start of the subject C and a graph showing the temporal change in the proficiency level is displayed.

  As illustrated in FIG. 23, when the input control unit 144 receives an instruction to end instruction, the output control unit 144 extracts a target image based on the time change of each index value illustrated in FIG. 22A. Specifically, when the instruction of the subject person A ends, the output control unit 144 sets the time corresponding to the warning condition for the index value having the highest priority among the index values corresponding to the warning condition for the subject person A. To get. In the example illustrated in FIG. 23, the output control unit 144 acquires the time when the risk level of the subject person A meets the warning condition. Then, the output control unit 144 extracts the color image 27 of the subject A corresponding to the acquired time from the motion information storage unit 131. And the output control part 144 produces | generates the instruction | indication report containing the extracted color image 27 and the graph which shows the time change of the subject A's risk degree. Then, the output control unit 144 displays the generated instruction report on the display screen of the output unit 110. Similarly, the output control unit 144 extracts a color image at a time when the risk level of each target person meets the warning condition every time the instruction to each target person is completed, Generate and display a report. The instruction report displayed by the output control unit 144 may be stored in the storage unit 130, an external storage device, a computer-readable recording medium, or the like, or may be printed on a paper medium by a printer. good.

  As described above, the medical information processing apparatus 100 outputs a guidance report in which the main points in the guidance of each subject person are extracted after the guidance by the leader is completed. For this reason, even if the leader does not participate in the guidance, the leader can know the main points of the guidance that has been provided.

(Combined use of biosensors)
In addition, for example, the medical information processing apparatus 100 may calculate the degree of risk by further using biological information acquired from a biological sensor. For example, when the medical information processing apparatus 100 includes a biosensor such as a pulse meter or a sphygmomanometer as the input unit 120, the medical information processing apparatus 100 may calculate the degree of risk using the pulse and blood pressure values acquired from the biosensor. good.

  In this case, for example, in the medical information processing apparatus 100, the storage unit 130 further includes a dangerous biological information storage unit 138.

  The dangerous biological information storage unit 138 stores biological information that is determined to be dangerous. For example, the dangerous biological information storage unit 138 is referred to by the risk level calculation unit 142B. For example, the dangerous biological information storage unit 138 is registered in advance by an operator of the medical information processing apparatus 100.

  FIG. 24 is a diagram illustrating an example of information stored in the dangerous living body information storage unit 138 according to another embodiment. As illustrated in FIG. 24, the dangerous biological information storage unit 138 stores information in which dangerous biological information is associated with an added risk level. Here, the “dangerous biological information” is biological information of the subject that suggests the possibility that the subject is in a dangerous situation.

  As illustrated in FIG. 24, the dangerous biological information storage unit 138 stores information in which the dangerous biological information “pulse is 130 / min or more” and the added risk “+4” are associated with each other. That is, the dangerous biological information storage unit 138 stores that “+3” is added to the risk level of the target person by the risk level calculation unit 142B when the pulse of the target person is 130 times or more per minute. Further, the dangerous biological information storage unit 138 stores information in which the dangerous biological information “blood pressure is 130 mmHg or higher” is associated with the added risk “+5”. That is, the dangerous biological information storage unit 138 stores that “+5” is added to the risk level of the subject by the risk level calculation unit 142B when the subject's maximum blood pressure is 130 mmHg or more. Similarly, the dangerous biological information storage unit 138 stores information in which the dangerous biological information is associated with the added risk level in the same manner for other dangerous biological information.

  The dangerous biological information storage unit 138 shown in FIG. 24 is an example, and the embodiment is not limited to this. For example, arbitrary items may be registered by the operator as the dangerous biological information stored in the dangerous biological information storage unit 138. In addition, an arbitrary value may be registered by the operator as the addition risk stored in the dangerous biological information storage unit 138.

  The risk level calculation unit 142B according to the other embodiment basically has the same function as the function described in the first embodiment. Then, the risk level calculation unit 142B further refers to the dangerous biological information storage unit 138 and determines whether the biological information of the subject acquired by the input unit 120 corresponds to the dangerous biological information. Then, when the biological information of the subject corresponds to the dangerous biological information, the risk level calculation unit 142B adds the added risk level corresponding to the dangerous biological information to the risk level of the subject person. Then, the risk level calculation unit 142B outputs the risk level of each subject person to the determination unit 143.

  As an example, the risk level calculation unit 142B refers to the dangerous biological information storage unit 138 and acquires all the dangerous biological information stored in the dangerous biological information storage unit 138. Then, each time the biometric information of the subject A is acquired by the input unit 120, the risk level calculation unit 142B determines whether or not the acquired biometric information of the subject A corresponds to the dangerous biometric information. Specifically, the risk level calculation unit 142B adds the added risk level “+4” corresponding to the risk biological information to the risk level of the target person A when the pulse of the target person A is 130 times or more per minute. To do. Then, the risk level calculation unit 142B outputs the risk level of the subject A to the determination unit 143.

  As described above, the medical information processing apparatus 100 calculates the degree of risk by further using the biological information acquired from the biological sensor. In other words, the medical information processing apparatus 100 calculates the risk level based on the risk that cannot be estimated only by the motion information of the subject person, so that the possibility that the subject person will be in a dangerous state can be reduced.

(When multiple leaders teach)
For example, when a plurality of leaders are involved in guidance, the medical information processing apparatus 100 may calculate the degree of involvement for each leader. For example, the medical information processing apparatus 100, when two leaders, the leader E and the leader F, are involved in the guidance of the subject A, the degree of involvement of the leader E in the subject A and the leader The degree of participation in which F is involved in the target person A may be calculated.

  FIG. 25 is a diagram illustrating an example of the configuration of the medical information processing apparatus 100 according to another embodiment. As illustrated in FIG. 25, in the medical information processing apparatus 100 according to another embodiment, the control unit 140 further includes a determination unit 145 as compared with the medical information processing apparatus 100 illustrated in FIG. 4.

  The determination part 145 which concerns on other embodiment determines the leader who is concerned with each subject. For example, the determination unit 145 determines a leader who is involved in each target person by voiceprint authentication. Specifically, the determination unit 145 stores a voiceprint of a leader (for example, the leader E or the leader F). The determination unit 145 performs voiceprint authentication on the acquired speech recognition result every time the acquisition unit 141 acquires the speech recognition result. Thereby, the determination unit 145 determines which instructor's voiceprint the acquired voice recognition result corresponds to. Then, the determination unit 145 associates information indicating a leader determined to correspond to the voiceprint with the voice recognition result, and outputs the information to the participation degree calculation unit 142D.

  Further, for example, the determination unit 145 determines a leader who is involved in each target person by face image recognition. Specifically, the determination unit 145 stores a face image of a leader (for example, the leader E or the leader F). The determination unit 145 performs face image recognition on the acquired color image information every time the acquisition unit 141 acquires color image information. Thereby, the determination unit 145 determines which instructor is included in the acquired color image information. Then, the determination unit 145 associates information indicating the leader determined to be included in the color image information with the color image information, and outputs the information to the participation degree calculation unit 142D.

  The degree-of-engagement calculation unit 142D according to another embodiment calculates the degree of participation of each target person for each instructor. As an example, the participation degree calculation unit 142D refers to the participation information storage unit 135 and acquires all the participation information stored in the participation information storage unit 135. Then, each time the acquisition unit 141 acquires the motion information of the subject person A, the participation degree calculation unit 142D determines whether the acquired motion information of the subject person A corresponds to the participation information. Then, when the motion information of the subject person A corresponds to the participation information, the participation level calculation unit 142D refers to information indicating the leader associated with the motion information and calculates the participation level for each leader. To do.

  Specifically, when the speech recognition result in which the name “Mr. A” of the subject person A is recognized as a word is acquired, the degree-of-engagement calculation unit 142D indicates the instructor associated with the speech recognition result. Browse information. Then, the degree-of-engagement calculation unit 142D gives the degree of participation information “leader E−target person A” to the degree of participation (leader E−target person A) indicating the involvement of the leader (for example, the leader E) associated with the target person A. The addition participation degree “+1” corresponding to “call the name of the subject” is added. Then, the degree-of-engagement calculation unit 142D outputs the degree of participation (leader E-target person A) to the determination unit 143.

  As described above, when a plurality of leaders are involved in guidance, the medical information processing apparatus 100 calculates the degree of involvement of each target person for each leader. For this reason, the medical information processing apparatus 100 can calculate the degree of participation in detail. Further, for example, the medical information processing apparatus 100 can notify whether or not each instructor can be equally involved with each target person.

  Furthermore, when a plurality of leaders teach, guidance priority may be determined for each leader. For example, the determination unit 143 may determine the instruction priority for each instructor when a plurality of subjects have the same instruction priority.

  Here, when the leader E and the leader F teach the subject G and the subject H, the case where the subject G and the subject H have the same guidance priority will be described. The leader E is a leader who specializes in sports medicine, and the leader F is a leader who has a long working experience at a nursing home. In addition, the target person G is a 25-year-old person who performs squat training, and the target person H is a 70-year-old person who performs walking training.

  In this case, the storage unit 130 stores personal information for each of the leader E, the leader F, the subject G, and the subject H. Specifically, the memory | storage part 130 memorize | stores the information that the leader E preferentially guides the subject who performs rehabilitation, such as squat training and jump training. Moreover, the memory | storage part 130 memorize | stores the information that the leader F preferentially guides an older subject. In addition, the storage unit 130 stores information indicating that the type of rehabilitation performed by the subject G is “Squat training” and information indicating that the subject is “25 years old”. In addition, the storage unit 130 stores information indicating that the type of rehabilitation performed by the subject H is “walking training” and information indicating “70 years old”.

  First, the case where the guidance priority of the leader E is determined will be described. The determination unit 143 obtains information from the storage unit 130 that the leader E preferentially guides the target person who performs rehabilitation such as squat training and jump training. Then, the determination unit 143 acquires the personal information of the target person G and the target person H from the storage unit 130 and compares them. In this case, the rehabilitation type of the subject G is “squat training”, and the rehabilitation type of the subject H is “walking training”. Therefore, the determination unit 143 generates information indicating that the subject person G is preferentially taught over the subject person H as the guidance priority of the leader E, and outputs the information to the output control unit 144.

  Next, the case where the guidance priority of the leader F is determined will be described. The determination unit 143 acquires information from the storage unit 130 to the effect that the leader F preferentially guides older subjects. Then, the determination unit 143 acquires the personal information of the target person G and the target person H from the storage unit 130 and compares them. In this case, the target person G is “25 years old” and the target person H is “70 years old”. For this reason, the determination unit 143 generates information indicating that the subject person H is preferentially taught over the subject person G as the guidance priority of the leader F, and outputs the information to the output control unit 144.

  Thus, the determination part 143 determines a guidance priority for every leader, when a some leader guides. In the example described above, the case where the subject person G and the subject person H have the same instruction priority has been described, but the present invention is not limited to this. Moreover, when the output control part 144 receives the instruction | indication priority for every instructor, it displays that. In the above example, the output control unit 144 provides the display screen of the output unit 110 with an area for displaying the instruction priority of the instructor E and an area for displaying the instruction priority of the instructor F. The instruction priority for each instructor is displayed. Further, for example, if the instructor E and the instructor F have dedicated output units 110 (headsets, dedicated monitors, etc.), the output control unit 144 outputs only their own instruction priority to each instructor. You may do it.

(Application other than rehabilitation)
Moreover, although said embodiment demonstrated the case where the medical information processing apparatus 100 was applied to the subject who performs rehabilitation, embodiment is not limited to this. For example, the medical information processing apparatus 100 may be applied when confirming whether a predetermined operation is correctly performed.

  FIG. 26 is a diagram illustrating an example of information stored in the implementation menu storage unit 132 according to another embodiment. As shown in FIG. 26, the execution menu storage unit 132 according to another embodiment associates the execution menu “screw mounting of the ventilation fan” with the unskilled operation division method “the elbow is higher than the shoulder”. Remember. That is, the implementation menu storage unit 132 determines that the proficiency level of the subject is “−1” by the proficiency level calculation unit 142A when the subject's elbow is higher than the shoulder when the subject is attaching the screw of the ventilation fan. "Is added. This inexperienced movement information “the elbow is higher than the shoulder” means that, for example, the y-coordinate value of the subject's elbow (joint 3f or 3j) is greater than the y-coordinate value of the shoulder (joint 3e or 3i). The determination is made according to whether or not the value is large. Similarly, the execution menu storage unit 132 stores information that associates the execution menu, the unskilled operation information, and the addition proficiency level with respect to the other execution menus.

  Thus, the medical information processing apparatus 100 is not limited to the case where the medical information processing apparatus 100 is applied to a target person who performs rehabilitation, and can check whether various predetermined operations are being performed with correct operations.

(Application to service providing equipment)
FIG. 27 is a diagram for explaining an example when applied to a service providing apparatus. As shown in FIG. 27, the service providing apparatus 200 is arranged in a service center and connected to, for example, a medical institution, a terminal apparatus 300 arranged in a home, or a workplace via the network 5. The operation information collection unit 10 is connected to each of the terminal devices 300 disposed in the medical institution, home, and workplace. Each terminal device 300 includes a client function that uses a service provided by the service providing device 200. The network 5 may employ any type of communication network such as the Internet or WAN (Wide Area Network) regardless of whether it is wired or wireless.

  For example, the service providing apparatus 200 has a function similar to that of the medical information processing apparatus 100 described with reference to FIG. 5 and provides the terminal apparatus 300 as a service using the function. That is, the service providing apparatus 200 includes functional units that are similar to the acquisition unit 141, the calculation unit 142, and the output control unit 144, respectively. And the function part similar to the acquisition part 141 acquires the operation | movement information showing an object person's operation | movement for every object person about the some object person who respectively performs predetermined operation | movement. Then, a functional unit similar to the calculation unit 142 calculates an index value indicating the implementation status of the predetermined operation for each target person based on the operation information for each target person. Then, a function unit similar to the output control unit 144 outputs an index value.

  For example, the service providing apparatus 200 receives an upload of a frame group of operation information to be processed from the terminal apparatus 300. Then, the service providing apparatus 200 performs the above processing to calculate an index value. Then, the service providing apparatus 200 causes the terminal device 300 to download the index value.

  As a result, if the operation information collection unit 10 is in a medical institution, home, workplace, etc., the service providing apparatus 200 provides the above function to the target person and instructor who performs rehabilitation in the medical institution, home, workplace, etc. Can be provided. The service providing apparatus 200 may perform the same processing as the determination unit 143 described above, determine the instruction priority for each target person, and cause the terminal apparatus 300 to download the instruction priority.

(Remote location guidance)
Further, for example, when the leader and the target person are away from each other, the medical information processing apparatus 100 may support remote guidance. For example, in FIG. 27, the service providing apparatus 200 can support a leader when a leader in a medical institution teaches a subject who performs rehabilitation at home.

  Specifically, the service providing apparatus 200 receives an upload of a frame group of operation information to be processed from the terminal device 300 at home. Then, the service providing apparatus 200 performs the above processing to calculate an index value. Then, the service providing apparatus 200 causes the terminal device 300 to download the index value. As a result, the service providing apparatus 200 can support remote location guidance.

  In this case, the function of the service providing apparatus 200 (function similar to that of the medical information processing apparatus 100) may be realized by the terminal apparatus 300 of the medical institution or may be realized by the terminal apparatus 300 at home. That is, in FIG. 27, the service providing apparatus 200, the medical institution terminal apparatus 300, and the home terminal apparatus 300 function as a medical information processing system.

  For example, when the index value is calculated in the service providing device 200, the terminal device 300 at home uses the operation information indicating the operation of the subject for the subject for which the motion information collection unit 10 performs the predetermined operation. Collect for each subject. Then, the terminal device 300 at home transmits operation information to the service providing device 200. The service providing apparatus 200 receives operation information from the terminal device 300 at home. Then, in the service providing apparatus 200, a functional unit similar to the calculation unit 142 calculates an index value indicating the implementation status of the predetermined operation for each target person based on the operation information for each target person. Then, the service providing apparatus 200 transmits the index value to the terminal apparatus 300 of the medical institution. The terminal device 300 of the medical institution receives the index value from the service providing device 200. Then, the terminal device 300 of the medical institution outputs the index value to the monitor.

  As another example, when the index value is calculated in the home terminal device 300, the home terminal device 300 uses the motion of the subject for the subject for which the motion information collection unit 10 performs a predetermined motion. Collect action information for each subject. Then, in the terminal device 300 at home, a functional unit similar to the calculation unit 142 calculates an index value indicating the implementation status of the predetermined operation for each target person based on the operation information for each target person. Then, the terminal device 300 at home transmits the index value to the terminal device 300 of the medical institution. Then, the terminal device 300 of the medical institution receives the index value from the terminal device 300 at home. Then, the terminal device 300 of the medical institution outputs the received index value to the monitor.

  As another example, when the index value is calculated in the terminal device 300 of the medical institution, the terminal device 300 at home has the subject information on which the motion information collecting unit 10 performs a predetermined motion. Collect action information representing actions for each subject. Then, the terminal device 300 at home transmits the operation information to the terminal device 300 of the medical institution. Then, the terminal device 300 of the medical institution receives operation information from the terminal device 300 at home. Then, in the terminal device 300 of the medical institution, a functional unit similar to the calculation unit 142 calculates an index value indicating the implementation status of the predetermined operation for each target person based on the operation information for each target person. Then, the terminal device 300 of the medical institution outputs the index value to the monitor.

  Further, the configuration of the medical information processing apparatus 100 in the above-described embodiment is merely an example, and the integration and separation of each unit can be performed as appropriate. For example, the acquisition unit 141 and the calculation unit 142 can be integrated.

  Further, the functions of the acquisition unit 141, the calculation unit 142, the determination unit 143, and the output control unit 144 described in the above-described embodiments can be realized by software. For example, the functions of the acquisition unit 141, the calculation unit 142, the determination unit 143, and the output control unit 144 are the processing described as being performed by the acquisition unit 141, the calculation unit 142, the determination unit 143, and the output control unit 144 in the above embodiment. This is realized by causing a computer to execute a medical information processing program that defines the above procedure. The medical information processing program is stored in, for example, a hard disk or a semiconductor memory device, and is read and executed by a processor such as a CPU or MPU. The medical information processing program is recorded on a computer-readable recording medium such as a CD-ROM (Compact Disc-Read Only Memory), an MO (Magnetic Optical disk), a DVD (Digital Versatile Disc), and distributed. obtain.

  Although several embodiments of the present invention have been described, these embodiments are presented by way of example and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, replacements, and changes can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope and gist of the invention, and are also included in the invention described in the claims and the equivalents thereof.

  According to at least one embodiment described above, the medical information processing apparatus and the program according to the present embodiment can improve the quality of instruction performed by an instructor who instructs each of a plurality of subjects.

DESCRIPTION OF SYMBOLS 100 Medical information processing apparatus 141 Acquisition part 142 Calculation part 143 Determination part 144 Output control part

Claims (13)

For a plurality of subjects who each perform a predetermined motion, an acquisition unit that obtains, for each subject, motion information representing coordinate changes of each part of the subject,
Based on the operation information for each target person, a calculation unit that calculates an index value indicating the implementation status of the predetermined action for each target person;
Based on the index value for each target person, a priority for teaching that indicates which target person is to be prioritized is determined for each target person, and based on the determined teaching priority, A medical information processing apparatus comprising: an output control unit that outputs support information for supporting a leader who guides the subject . The calculation unit includes, as the index value, a proficiency level indicating a level of proficiency related to the predetermined motion of the target person, a risk level indicating a degree of danger in the predetermined motion of the target person, and the target person specifying the predetermined value The medical information processing apparatus according to claim 1, wherein at least one of the negligence levels indicating the degree of neglecting the operation is calculated. Before SL calculating section further calculates a degree of involvement representing the degree of involvement in the subject from the leaders for each of the subject,
The output control unit further on the basis of the relevance, medical information processing apparatus according to the guidance priority to claim 1 or 2, wherein the determining for each of the subject. The calculating unit is further using private information of the subject, a medical information processing apparatus according to the index value in any one of claims 1-3, characterized in that calculating for each of the subject. Before SL output control unit, the support information for supporting a leader to guide the plurality of subjects, and outputs in terms of the teaching priority is to highlight the information about the highest subjects The medical information processing apparatus according to any one of claims 1 to 4 . The output control unit outputs an attention image, which is an image at a time when a change that should be noted in the index value of the subject occurs, as support information for assisting a leader who guides the plurality of subjects. medical image processing apparatus according to any one of claims 1-5, characterized in that. The medical information processing apparatus according to claim 6 , wherein the output control unit outputs a guidance report including the attention image as the support information. The medical information processing apparatus according to claim 2 , wherein the calculation unit calculates the degree of risk by further using biological information of the subject. When there are a plurality of leaders who guide the plurality of subjects, the plurality of leaders further comprises a determination unit that determines leaders involved in the subject,
The medical information processing apparatus according to claim 3 , wherein the calculation unit calculates the degree of participation for each pair of the leader and the target person. For a plurality of subjects who each perform a predetermined motion, an acquisition procedure for obtaining, for each subject, motion information representing coordinate changes of each part of the subject,
A calculation procedure for calculating an index value indicating an implementation status of the predetermined action for each target person based on the action information for each target person;
Based on the index value for each target person, a priority for teaching that indicates which target person is to be prioritized is determined for each target person, and based on the determined teaching priority, A medical information processing program for causing a computer to execute an output control procedure for outputting support information for supporting a leader who guides a subject . A medical information processing system including a terminal for a subject connected to each other via a network and a terminal for a leader,
The target terminal is
For a subject who performs a predetermined motion, a collection unit that collects motion information representing coordinate changes of each part of the subject for each subject,
Based on the operation information for each target person, a calculation unit that calculates an index value indicating the implementation status of the predetermined action for each target person;
Based on the index value for each target person, a priority for teaching that indicates which target person is to be prioritized is determined for each target person, and based on the determined teaching priority, A transmission unit for transmitting support information for supporting a leader who guides the subject to the terminal for the leader,
The instructor terminal is
A receiving unit for receiving the support information from the target user terminal;
A medical information processing system comprising: an output unit that outputs the support information . A medical information processing system including a terminal for a subject connected to each other via a network and a terminal for a leader,
The target terminal is
For a subject who performs a predetermined motion, a collection unit that collects motion information representing coordinate changes of each part of the subject for each subject,
A transmitter that transmits the operation information to the instructor terminal;
The instructor terminal is
A receiving unit for receiving the operation information from the target user terminal;
A calculation unit that calculates, for each target person, an index value indicating an implementation status of the predetermined operation based on the operation information;
Based on the index value for each target person, a priority for teaching that indicates which target person is to be prioritized is determined for each target person, and based on the determined teaching priority, A medical information processing system comprising: an output unit that outputs support information for supporting a leader who guides the subject . A medical information processing system including a target person terminal, a leader terminal, and a service providing apparatus connected to each other via a network,
The target terminal is
For a subject who performs a predetermined motion, a collection unit that collects motion information representing coordinate changes of each part of the subject for each subject,
A transmission unit for transmitting the operation information to the service providing device,
The service providing apparatus includes:
A receiving unit for receiving the operation information from the target user terminal;
A calculation unit that calculates, for each target person, an index value indicating an implementation status of the predetermined operation based on the operation information;
Based on the index value for each target person, a priority for teaching that indicates which target person is to be prioritized is determined for each target person, and based on the determined teaching priority, A transmission unit for transmitting support information for supporting a leader who guides the subject to the terminal for the leader,
The instructor terminal is
A receiving unit for receiving the support information from the service providing device;
A medical information processing system comprising: an output unit that outputs the support information .

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