Telemedicine support system

Abstract

The present invention relates to a telemedicine support system and, more specifically, to a telemedicine support system, which remotely measures physical functions of brain disease patients and the like who have difficulty in directly visiting a hospital without the help of a guardian, analyzes the measured physical data so as to perform accurate gait evaluation and the like, and performs telemedicine on the basis of the data such that a customized prescription can be provided to a patient.

Description

Non-face-to-face medical treatment assistance system

[0001] The present invention relates to a non-face-to-face medical treatment assistance system, and more specifically, to a non-face-to-face medical treatment assistance system that remotely measures the physical functions of patients with brain diseases who have difficulty visiting a hospital in person without the assistance of a guardian, analyzes the measured physical data to enable accurate gait evaluation, and performs non-face-to-face treatment based on such data, thereby providing a customized prescription to the patient.

[0002] With the recent rapid advancement of electronic communication technology, interest in developing technologies that can manage personal health in a networked environment is growing. For example, technologies are being developed to detect biometric information and provide the information using wearables or smart devices.

[0003] Additionally, the user's biometric information detected in this way is not only stored in the user's terminal, but is also actively used to provide feedback on the user's health management.

[0004] Meanwhile, telemedicine, which has recently been widely discussed, relies on health information accumulated by users throughout their lives to obtain user-related data. To this end, numerous personalized health management apps utilizing wearable devices and personal terminals have been developed.

[0005] FIG. 1 is a diagram illustrating a conventional healthcare system, which is disclosed in Korean Patent Publication No. 10-2018-0069445 (June 25, 2018). As illustrated in FIG. 1, this conventional healthcare system (90) includes a wearable device (91), a user terminal device (93), an electronic device (95), and a server (97).

[0006] The above wearable device (91) refers to a configuration that senses or monitors a user's bio-signals to generate health data.

[0007] For example, the wearable device (91) can measure the user's blood sugar, and for this purpose, the wearable device (91) can include a Continuous Glucose Monitoring System (CGMS). Here, CGMS refers to a continuous blood sugar monitoring system that measures blood sugar levels that change in real time by inserting a glucose sensor under a person's skin. However, the CGMS that measures blood sugar levels in an invasive manner is only one embodiment of the wearable device (91), and it goes without saying that various types of sensors, such as a non-invasive method, for example, a blood sugar measurement sensor in the form of a patch that is attached to the user's body, can be included in the wearable device (91).

[0008] In addition, the wearable device (91) can measure the user's level of exercise, heart rate, etc., and for this purpose, the wearable device (91) can be equipped with an acceleration sensor, a gyro sensor, etc.

[0009] In addition, the wearable device (91) can measure various health data, such as measuring body composition using human body resistance and measuring the user's sleep information using a Kinect sensor.

[0010] The wearable device (91) transmits health data to a user terminal device (93), and for this purpose, the wearable device (91) may include various communication chips such as a Wi-Fi chip, a Bluetooth chip, a wireless communication chip, and an NFC chip.

[0011] The above wearable device (91) may be configured in the form of a smartwatch, as shown in FIG. 1, or may be configured as various types of devices, such as a patch, that can be worn on the user's wrist, arm, waist, or ankle to measure health data.

[0012] The above user terminal device (93) refers to a configuration that receives health data from the wearable device (91) and transmits the received health data to at least one of the electronic device (95) and the server (97).

[0013] The electronic device (95) is configured to receive health data from the user terminal device (93). As shown in FIG. 1, if the electronic device (95) is a refrigerator, the electronic device (95) that has received the health data can provide guide information including information on food that the user should consume, and if the electronic device (95) is a treadmill, it can present information on calories that the user should consume.

[0014] The server (97) above is configured to receive and store healthcare information from at least one of a wearable device (91), a user terminal device (93), and an electronic device (95), and the healthcare information includes the user's physical information and behavioral information. The physical information may include the user's blood sugar, body fat index, body temperature, blood pressure, etc., and the behavioral information may include information on food consumed by the user, exercise information on the user's exercise time and type of exercise, etc.

[0015] However, this conventional healthcare system (90) has a limitation in that the collected information, such as physical information and behavioral information, is recorded separately and cannot be integrated and provided in the form of integrated information.

[0016] In other words, the conventional technology only presented numerical values ​​measured through the wearable device (91) or made simple evaluations through quantitative comparison with reference values, and had limitations in collecting measured information and deriving comprehensive conclusions.

[0017] In particular, if we assume that this healthcare system is used for the rehabilitation of patients who have undergone knee surgery, etc., although the measured results can be provided to the patient through various measurement methods, these are merely numbers, and when a patient without professional knowledge sees them, they cannot help but have questions about what their current condition is and how they should proceed with rehabilitation exercises.

[0018] Accordingly, the relevant industry is demanding the introduction of a new system that goes beyond simply distributing and listing measured factors when constructing a healthcare system, but instead comprehensively collects the measured information and synthesizes the collected information so that experts can provide appropriate feedback to users.

[0019] (Patent Document 1) Korean Patent Publication No. 10-2018-0069445 (June 25, 2018)

[0020] The present invention has been devised to solve the above problems.

[0021] The purpose of the present invention is to provide a non-face-to-face medical treatment assistance system that enables non-face-to-face treatment and continuous observation of patients with brain diseases who have difficulty visiting a hospital in person.

[0022] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that improves the health of patients and prevents the deterioration of their health by remotely treating patients who cannot visit a hospital without the assistance of a guardian and providing customized prescriptions tailored to the patient.

[0023] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that enables medical staff to perform non-face-to-face medical treatment based on quantitative data based on the patient's daily life data.

[0024] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that can accurately monitor the physical functions of a patient undergoing rehabilitation without visiting a hospital or the like by utilizing a tracker.

[0025] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that can evaluate a user's joint range of motion, gait, balance, etc. through a body measurement unit including a first tracker and a second tracker.

[0026] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that analyzes body data generated by a body measurement unit to determine whether a user walks normally.

[0027] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that classifies data in a direction where the data dispersion is reduced, thereby enabling accurate evaluation of whether the gait of a user wearing a tracker is normal or abnormal.

[0028] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating a spatial symmetry ratio (α / β) by dividing the knee angle (α) of the affected side by the knee angle (β) of the healthy side, and classifying data by comparing the calculated spatial symmetry ratio (α / β) with a reference spatial symmetry ratio.

[0029] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating the swing phase ratio of the healthy side and classifying the data by comparing the calculated swing phase ratio of the healthy side with a reference swing phase ratio.

[0030] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating a shank pitch range of the affected side and a thigh pitch range of the affected side, and classifying the data by comparing the calculated shank pitch range of the affected side and the thigh pitch range of the affected side with the shank pitch range of the reference affected side and the thigh pitch range of the reference affected side, respectively.

[0031] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating a shin pitch range of the healthy side and a thigh pitch range of the healthy side, and classifying the data by comparing the calculated shin pitch range of the healthy side and the thigh pitch range of the healthy side with the shin pitch range of the reference healthy side and the thigh pitch range of the reference healthy side, respectively.

[0032] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that outputs a class when the Gini coefficient is 0 in the process of data branching by a data analysis module, thereby determining whether the user's walking condition is normal, and if abnormal, accurately determining whether it is a knee problem or an ankle problem.

[0033] Another object of the present invention is to provide a non-face-to-face medical examination assistance system that configures a self-questionnaire, provides questionnaire data to a user terminal, and receives response data to the provided questionnaire data, thereby enabling remote questionnaires to be smoothly performed without visiting a hospital or the like.

[0034] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that configures a daily record book, provides alarm data such as meal alarms, medication alarms, and exercise alarms to a user terminal, and receives record data for the provided alarm data, thereby systematically recording and managing the user's daily life, such as whether or not he or she eats, whether or not he or she takes medication, and whether or not he or she exercises.

[0035] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that enables non-face-to-face remote treatment by transmitting user data generated by connecting to a physical function evaluation unit, a self-questionnaire unit, and a daily record unit to a medical staff terminal, and allowing the medical staff terminal to view the user data and input treatment data thereon.

[0036] Another object of the present invention is to provide a non-face-to-face medical treatment assistance system that inputs medical treatment data on a medical staff terminal side and provides a user-tailored prescription to a user terminal side based on the input medical treatment data, thereby enabling the user's daily life to be taken care of.

[0037] In order to achieve the above-mentioned purpose, the present invention is implemented by an embodiment having the following configuration.

[0038] According to one embodiment of the present invention, the present invention includes a body measurement unit that measures body data related to body functions, and a server unit that receives and analyzes the body data, and the server unit is characterized by including a body function evaluation unit that determines the user's condition through analysis of the body data.

[0039] According to another embodiment of the present invention, the body function evaluation unit is characterized in that it includes a data receiving module that receives the body data from the body measurement unit, and a data analysis module that is connected to the data receiving module and analyzes the received body data to evaluate the user's gait.

[0040] According to another embodiment of the present invention, the data analysis module is characterized in that it classifies data in a direction in which the dispersion of the data decreases and evaluates the user's gait as normal gait and abnormal gait.

[0041] According to another embodiment of the present invention, the data analysis module is characterized in that it includes a spatial symmetry analysis module that calculates a spatial symmetry ratio (α / β) obtained by dividing the knee angle (α) of the affected side by the knee angle (β) of the healthy side, and compares the calculated spatial symmetry ratio (α / β) with a reference spatial symmetry ratio, and determines as true a ratio that is less than or equal to the reference spatial symmetry ratio, and as false a ratio that is greater than the reference spatial symmetry ratio.

[0042] According to another embodiment of the present invention, the data analysis module is characterized in that it includes a sound side swing phase analysis module that calculates a swing phase ratio of the sound side, compares the calculated swing phase ratio of the sound side with a reference swing phase ratio, and sets a swing phase ratio smaller than or equal to the reference swing phase ratio as True, and a swing phase ratio larger than the reference swing phase ratio as False.

[0043] According to another embodiment of the present invention, the data analysis module is characterized in that it includes an affected-side shin pitch analysis module that calculates a shin pitch range of the affected side, compares the calculated shin pitch range of the affected side with a shin pitch range of the reference affected side, and sets a case where the shin pitch range is less than or equal to the reference shin pitch range of the affected side as true, and a case where the shin pitch range is greater than the reference shin pitch range of the affected side as false.

[0044] According to another embodiment of the present invention, the data analysis module is characterized in that it includes an affected-side thigh pitch analysis module that calculates a thigh pitch range of the affected side, compares the calculated thigh pitch range of the affected side with a thigh pitch range of the reference affected side, and determines as true a range that is less than or equal to the thigh pitch range of the reference affected side, and determines as false a range that is greater than the thigh pitch range of the reference affected side.

[0045] According to another embodiment of the present invention, the data analysis module is characterized in that it includes a sound side shin pitch analysis module that calculates a shin pitch range of the sound side, compares the calculated shin pitch range of the sound side with a shin pitch range of the reference sound side, and determines as true a case that is less than or equal to the shin pitch range of the reference sound side, and determines as false a case that is greater than the shin pitch range of the reference sound side.

[0046] According to another embodiment of the present invention, the data analysis module is characterized in that it includes a healthy thigh pitch analysis module that calculates a thigh pitch range of the healthy side, compares the calculated thigh pitch range of the healthy side with the thigh pitch range of the reference healthy side, and determines as true a range that is less than or equal to the thigh pitch range of the reference healthy side, and determines as false a range that is greater than the thigh pitch range of the reference healthy side.

[0047] According to another embodiment of the present invention, the physical function evaluation unit is characterized in that it includes a gait judgment module that is connected to the data analysis module and outputs a class when the Gini coefficient is 0.

[0048] According to another embodiment of the present invention, the present invention is characterized in that the measuring unit includes a first tracker positioned on the shin side of the user and a second tracker positioned on the thigh side of the user.

[0049] According to another embodiment of the present invention, the server unit is characterized in that it includes a self-questionnaire unit that generates questionnaire data and transmits it to a user terminal side, and receives and analyzes response data from the user terminal side to the questionnaire data.

[0050] According to another embodiment of the present invention, the present invention is characterized in that the server unit includes a daily record unit that generates alarm data and transmits it to the user terminal side, and receives and analyzes the user terminal side's record data for the alarm data.

[0051] According to another embodiment of the present invention, the present invention is characterized in that the server unit includes a non-face-to-face treatment unit that is connected to the physical function evaluation unit, the self-questionnaire unit, and the daily record unit, and transmits user data to a medical staff terminal side and receives treatment data from the medical staff terminal side.

[0052] According to another embodiment of the present invention, the present invention is characterized in that the server unit includes a customized prescription unit that provides a customized prescription to the user terminal side based on the treatment data received in the non-face-to-face treatment unit.

[0053] The present invention can obtain the following effects through the combination and use of the configuration described above and the following examples.

[0054] The present invention has the effect of providing a non-face-to-face treatment assistance system that enables patients with brain diseases, etc. who have difficulty visiting a hospital in person, to be treated and continuously observed remotely.

[0055] The present invention provides a non-face-to-face medical treatment assistance system that improves the health of patients and prevents the deterioration of their health by remotely treating patients who cannot visit the hospital without the assistance of a guardian and providing customized prescriptions tailored to the patient.

[0056] The present invention has the effect of providing a non-face-to-face treatment assistance system that enables medical staff to perform non-face-to-face treatment based on quantitative data based on the patient's daily data.

[0057] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that can accurately monitor the physical functions of a patient undergoing rehabilitation without having to visit a hospital or the like by utilizing a tracker.

[0058] The present invention provides a non-face-to-face medical treatment assistance system that can evaluate a user's joint range of motion, gait, balance, etc. through a body measurement unit including a first tracker and a second tracker.

[0059] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that analyzes body data generated by a body measurement unit to determine whether a user is walking normally.

[0060] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that classifies data in a direction in which the dispersion of the data is reduced, thereby enabling an accurate assessment of whether the gait of a user wearing a tracker is normal or abnormal.

[0061] The present invention provides a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating a spatial symmetry ratio (α / β) by dividing the knee angle (α) of the affected side by the knee angle (β) of the healthy side, and classifying data by comparing the calculated spatial symmetry ratio (α / β) with a reference spatial symmetry ratio.

[0062] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating the swing phase ratio of the healthy side and classifying the data by comparing the calculated swing phase ratio of the healthy side with a reference swing phase ratio.

[0063] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating a shank pitch range of the affected side and a thigh pitch range of the affected side, and classifying the data by comparing the calculated shank pitch range of the affected side and the thigh pitch range of the affected side with the shank pitch range of the reference affected side and the thigh pitch range of the reference affected side, respectively.

[0064] The present invention provides a non-face-to-face medical treatment assistance system that accurately determines a user's gait by calculating a shin pitch range of the healthy side and a thigh pitch range of the healthy side, and classifying the data by comparing the calculated shin pitch range of the healthy side and the thigh pitch range of the healthy side with the reference shin pitch range of the reference healthy side and the reference thigh pitch range of the reference healthy side, respectively.

[0065] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that outputs a class when the Gini coefficient is 0 in the process of data branching by a data analysis module, thereby determining whether the user's walking condition is normal, and if abnormal, accurately determining whether it is a knee problem or an ankle problem.

[0066] The present invention has the effect of providing a non-face-to-face medical examination assistance system that enables remote consultation to be performed smoothly without visiting a hospital, etc., by configuring a self-questionnaire, providing consultation data to a user terminal, and receiving response data to the provided consultation data.

[0067] The present invention provides a non-face-to-face medical treatment assistance system that configures a daily record book, provides alarm data such as a meal alarm, a medication alarm, and an exercise alarm to a user terminal, and receives record data for the provided alarm data, thereby enabling the user's daily life, such as whether or not he or she has eaten, taken his or her medication, and exercised, to be systematically recorded and managed.

[0068] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that enables non-face-to-face remote treatment by transmitting user data generated by connecting a physical function evaluation unit, a self-questionnaire unit, and a daily record unit to a medical staff terminal side, and allowing the medical staff terminal side to view the user data and input treatment data thereon.

[0069] The present invention has the effect of providing a non-face-to-face medical treatment assistance system that, when medical data is input on the medical staff terminal side, provides a user-tailored prescription to the user terminal side based on the input medical data, thereby enabling the user's daily life to be taken care of.

[0070] Figure 1 is a drawing illustrating a conventional healthcare system.

[0071] FIG. 2 is a drawing illustrating a non-face-to-face medical treatment assistance system according to one embodiment of the present invention.

[0072] Figure 3 is a drawing illustrating a server section.

[0073] Figure 4 is a diagram illustrating a physical function evaluation unit.

[0074] Figure 5 is a diagram illustrating the analysis process by the data analysis module.

[0075] Figure 6 is a diagram illustrating data analysis code.

[0076] Figure 7 is a diagram illustrating data analysis verification.

[0077] Hereinafter, preferred embodiments of a non-face-to-face medical treatment assistance system according to the present invention will be described in detail with reference to the attached drawings. In the following description of the present invention, if a detailed description of a known function or configuration is judged to unnecessarily obscure the gist of the present invention, such detailed description will be omitted. Unless otherwise defined, all terms in this specification have the same general meaning as those skilled in the art to which the present invention pertains. If there is a conflict between the meaning of a term used in this specification and the definition used in this specification, the definition used in this specification shall prevail.

[0078] The present invention relates to a non-face-to-face medical treatment assistance system (1), which remotely measures the physical functions of patients with brain diseases who have difficulty visiting a hospital in person without the assistance of a guardian, analyzes the measured physical data to enable accurate gait evaluation, and performs non-face-to-face medical treatment based on such data, thereby enabling customized prescriptions to be provided to the patient. Fig. 2 is a drawing illustrating a non-face-to-face medical treatment assistance system (1) according to one embodiment of the present invention. Referring to Fig. 2, the non-face-to-face medical treatment assistance system (1) includes a body measurement unit (10) and a server unit (30).

[0079] The above body measurement unit (10) refers to a configuration that measures body data related to a body function. Preferably, the body function may be the user's walking. The body data is data measured when the user walks while wearing the body measurement unit (10) on the body, and may include acceleration, which is a change in speed per unit time, angular velocity, which is an angle rotated per unit time based on a center of rotation, etc., and angles between movements changed based on a center of rotation, etc.

[0080] The above body measurement unit (10) may be composed of a plurality of units, and may be worn on both the healthy side (normal lower limb) and the affected side (abnormal lower limb) for purposes such as comparing normal and abnormal legs. In addition, the body measurement unit (10) is configured with a communication means to transmit the measured body data to the user terminal side, and the body data received at the user terminal can be transmitted to the server unit (30) via a network as illustrated in FIG. 2. The user terminal refers to a terminal used by the user side, and the terminal refers to a device that enables data reception and data transmission, such as a computer, a smart phone, or a tablet PC. Referring to FIG. 2, the body measurement unit (10) includes a first tracker (11) and a second tracker (13).

[0081] The first tracker (11) is configured to measure data related to walking by being positioned on the shin side of the user's lower body. It can be considered to be positioned on the proximal tibia below the knee, based on the user's knee. The first tracker (11) is configured in multiple pieces and can be worn on both the user's left and right legs.

[0082] The second tracker (13) is configured to measure data related to walking by being positioned on the thigh side of the user's lower body. It can be seen that it is positioned on the distal femur side above the knee with the user's knee as a reference. The second tracker (13), like the first tracker (11), is also configured in multiple units and can be positioned on both the left and right legs of the user.

[0083] The above server unit (30) refers to a configuration that receives and analyzes the body data generated by the body measurement unit (10). In one embodiment of the present invention, although it is not excluded that data is directly transmitted from the body measurement unit (10) to the server unit (30), it can be seen that, preferably, the body data generated by the body measurement unit (10) is received by the user terminal and then transmitted to the server unit (30) via a network.

[0084] As illustrated in FIG. 2, the server unit (30) is connected to a user terminal (U) and a medical staff terminal (M) via a network, so that the medical staff terminal (M) can download data uploaded to the server unit (30) by the user terminal (U) by accessing the network from the server unit (30), and conversely, the user terminal (U) can download data uploaded to the server unit (30) by the medical staff terminal (M) from the server unit (30). Therefore, even if the user terminal (U) and the medical staff terminal (M) are located in remote locations, easy communication between them becomes possible.

[0085] Figure 3 is a drawing illustrating a server unit (30), which includes a physical function evaluation unit (31), a self-questionnaire unit (33), a daily record unit (35), a non-face-to-face treatment unit (37), and a customized prescription unit (39).

[0086] The above-described physical function evaluation unit (31) refers to a configuration that determines the user's condition through analysis of the above-described physical data. Preferably, the physical function evaluation unit (31) can determine whether the user's gait is normal or abnormal, and if abnormal, can determine whether there is a problem with the knees or ankles, etc. Fig. 4 is a drawing illustrating the above-described physical function evaluation unit (31), and the above-described physical function evaluation unit (31) includes a data reception module (311), a data analysis module (313), and a gait judgment module (315).

[0087] The above data receiving module (311) is configured to receive the body data from the body measurement unit (10), and for this purpose, the data receiving module (311) can be connected to the user terminal (U). The data receiving module (311) is configured to be connected to a data analysis module (313) to be described later and to provide the received body data to the data analysis module (313).

[0088] The above data analysis module (313) refers to a configuration that analyzes the body data received by being connected to the data receiving module (311) to evaluate the user's gait. Preferably, the data analysis module (313) may be configured to classify the data in a direction in which the data dispersion is reduced to evaluate the user's gait as normal gait and abnormal gait. Referring to FIG. 4, the data analysis module (313) includes a spatial symmetry analysis module (3131), a sound-side swing phase analysis module (3132), an affected-side shin pitch analysis module (3133), an affected-side thigh pitch analysis module (3134), a sound-side shin pitch analysis module (3135), and a sound-side thigh pitch analysis module (3136).

[0089] The above spatial symmetry analysis module (3131) is configured to calculate a spatial symmetry ratio (α / β) (Spatial Symmetry Ratio, SSR) by dividing the knee angle (α) of the affected side by the knee angle (β) of the healthy side, and compare the calculated spatial symmetry ratio (α / β) with a reference spatial symmetry ratio, and determine that a sample smaller than or equal to the reference spatial symmetry ratio is true, and a sample larger than the reference spatial symmetry ratio is false. Fig. 5 is a diagram illustrating an analysis process by the data analysis module (313). Referring to Fig. 5, 351 samples can be first classified by the spatial symmetry analysis module (3131) with the reference spatial symmetry ratio set to 0.812. The Gini coefficient (after classification by the spatial symmetry analysis module (313)) , Pi: ratio of each class) is 0.663, which confirms that the data dispersion is still high even with only the classification by the spatial symmetry analysis module (3131) once. Therefore, as illustrated in FIG. 5, after the data classification by the spatial symmetry analysis module (3131), classification by the sound-side swing phase analysis module (3132) described later can be performed. In addition, the data classification by the spatial symmetry analysis module (3131) is not performed only once at the beginning, but can be performed repeatedly as illustrated in FIG. 5.

[0090] The above-described healthy side swing phase analysis module (3132) is configured to calculate the swing phase ratio of the healthy side, compare the calculated swing phase ratio of the healthy side with a reference swing phase ratio, and determine that a ratio that is less than or equal to the reference swing phase ratio is True, and a ratio that is greater than the reference swing phase ratio is False. The swing phase refers to the period during which the foot is in the air from the toe lift to the heel touch again during the walking process. Preferably, data classification by the healthy side swing phase analysis module (3132) can be considered to be performed after data classification by the spatial symmetry analysis module (3131).

[0091] Referring to FIG. 5, when data is classified by the spatial symmetry analysis module (3131) by setting the smaller or equal to the reference spatial symmetry ratio as True and setting the larger than the reference spatial symmetry ratio as False, the data classified as True can be classified by setting the reference swing phase ratio to 0.376, and classifying the smaller or equal to 0.376 as True and classifying the larger than 0.376 as False. In addition, the data is branched in the direction of lowering the Gini coefficient by setting the reference swing phase ratio to 0.403, and classifying the smaller or equal to 0.403 as True and classifying the larger than 0.403 as False for the data classified as False by the spatial symmetry analysis module (3131).

[0092] The above-mentioned affected-side shin pitch analysis module (3133) refers to a configuration that calculates the shin pitch range of the affected side, compares the calculated shin pitch range of the affected side with the shin pitch range of the reference affected side, and sets a value that is less than or equal to the reference shin pitch range of the affected side as True, and a value that is greater than the reference shin pitch range of the affected side as False. Preferably, data classification by the affected-side shin pitch analysis module (3133) can be considered to be performed after data classification by the spatial symmetry analysis module (3131).

[0093] Referring to FIG. 5, data classified as True by the spatial symmetry analysis module (3131) and data classified as True by the healthy-side swing phase analysis module (3132) are reclassified by the spatial symmetry analysis module (3131), and data classification can be performed by the affected-side shin pitch analysis module (3133) on the data classified as True. And when the data classified as True by the healthy side swing phase analysis module (3132) based on the data classified as True by the spatial symmetry analysis module (3131) is again classified by the spatial symmetry analysis module (3131), the data classified as False is again classified by the spatial symmetry analysis module (3131), and at this time, data classification can be performed by the affected side shin pitch analysis module (3133) based on the data classified as True.

[0094] The above-mentioned affected thigh pitch analysis module (3134) is a configuration that calculates the thigh pitch range of the affected side, compares the calculated thigh pitch range of the affected side with the thigh pitch range of the reference affected side, and sets a value smaller than or equal to the reference thigh pitch range of the affected side as True, and a value larger than the reference thigh pitch range of the affected side as False. Referring to FIG. 5, when data classification is performed by the spatial symmetry analysis module (3131) with the reference spatial symmetry ratio set to 0.812, data classification is performed by the sound-side swing phase analysis module (3132) for data smaller than or equal to 0.812, and at this time, data classification can be performed by the sound-side thigh pitch analysis module (3134) for data greater than the reference sound-side swing phase ratio of 0.376.

[0095] The sound side shin pitch analysis module (3135) is configured to calculate the sound side shin pitch range, compare the calculated sound side shin pitch range with the reference sound side shin pitch range, and determine as true if the sound side shin pitch range is less than or equal to the reference sound side shin pitch range, and determine as false if the sound side shin pitch range is greater than the reference sound side shin pitch range. Preferably, the sound side shin pitch analysis module (3135) can classify data based on data classified as true by the affected side thigh pitch analysis module (3134), as illustrated in FIG. 5, and can also classify data based on data classified as true by the sound side swing phase analysis module (3132) among data classified as false by the spatial symmetry analysis module (3131).

[0096] The above-mentioned healthy thigh pitch analysis module (3136) is a configuration that calculates the thigh pitch range of the healthy side, compares the calculated thigh pitch range of the healthy side with the thigh pitch range of the reference healthy side, and sets a value that is less than or equal to the thigh pitch range of the reference healthy side as True, and a value that is greater than the thigh pitch range of the reference healthy side as False. Referring to FIG. 5, the healthy thigh pitch analysis module (3136) may be configured to classify data after the affected thigh pitch analysis module (3134), and more preferably, the healthy thigh pitch analysis module (3136) may be configured to perform a classification task on data classified as False by the affected thigh pitch analysis module (3134) among data classified as True by the spatial symmetry analysis module (3131) and data classified as False by the healthy swing phase analysis module (3132).

[0097] The above gait judgment module (315) is connected to the data analysis module (313) and is configured to output a class when the Gini coefficient is less than or equal to a reference coefficient value, preferably, it refers to a configuration that outputs a class when the Gini coefficient is 0. The present invention configures the gait judgment module (315) so that a class when the Gini coefficient is 0 is output in the process of data branching by the data analysis module (313), thereby determining whether the user's gait state is normal, and if abnormal, accurately determining whether it is a knee problem or an ankle problem. In Fig. 5, class A means normal gait, class B means abnormal gait with a knee problem, and class C means abnormal gait with a ankle problem.

[0098] FIG. 6 is a diagram illustrating a data analysis code. Referring to FIG. 6, data classification by the spatial symmetry analysis module (3131) is performed first for the first data sample, and as illustrated in FIG. 6, the reference spatial symmetry ratio may be set to 0.81. Thereafter, data classification by the sound-side swing phase analysis module (3132) may be coded to be performed, and the reference swing phase ratio may be set to 0.38. Next, data classification by the spatial symmetry analysis module (3131) is performed again, and at this time, the reference spatial symmetry ratio may be set to 0.28. Thereafter, data classification is performed by the affected-side shank pitch analysis module (3133) with the reference affected-side shank pitch range being 37.14. Through this, data less than or equal to 37.14 are derived as Class C and judged as abnormal gait with an ankle problem, and other data greater than 37.14 are derived as Class B and judged as abnormal gait with an knee problem. In this way, when data analysis code such as that in Fig. 6 is written, highly accurate gait judgment becomes possible, as shown in the diagram illustrating data analysis verification in Fig. 7.

[0099] The above self-questionnaire (33) refers to a configuration that generates questionnaire data and transmits it to the user terminal side, and receives and analyzes the user terminal side's response data to the questionnaire data. The present invention not only generates objective data by the physical function evaluation unit (31), but also enables the collection of subjective data such as discomfort and pain currently felt by the patient by the self-questionnaire (33), so that the objective data and subjective data are transmitted to the medical staff as one, allowing accurate diagnosis and treatment to be performed even without visiting a hospital, etc.

[0100] The above daily record unit (35) refers to a configuration that generates alarm data and transmits it to the user terminal side, and receives and analyzes the user terminal side's record data regarding the alarm data. According to the above daily record unit (35), alarm data such as meal alarms, medication alarms, and exercise alarms can be provided to the user terminal side, and by receiving record data from the user terminal side regarding the provided alarm data, the user's daily life such as whether or not he or she has eaten, taken his or her medication, and exercised can be systematically recorded and managed.

[0101] The above-mentioned non-face-to-face treatment unit (37) is connected to the above-mentioned physical function evaluation unit, the above-mentioned self-questionnaire unit, and the above-mentioned daily record unit, and refers to a configuration that transmits user data to the medical staff terminal side and receives treatment data from the medical staff terminal side. By means of the above-mentioned non-face-to-face treatment unit (37), patients with brain diseases who have difficulty visiting the hospital in person due to inconvenience in movement can not only receive prescriptions for medication non-face-to-face, but also receive examination and treatment remotely that would be available when visiting an actual hospital.

[0102] The above-mentioned customized prescription unit (39) refers to a configuration that provides a customized prescription to the user terminal based on the treatment data received by the non-face-to-face treatment unit (37). When the medical staff terminal inputs the treatment data, the customized prescription unit (39) provides the user terminal with a customized prescription based on the input treatment data, thereby enabling the user's daily life to be cared for in real time.

[0103] The detailed description above is illustrative of the present invention. Furthermore, the above description illustrates and describes preferred embodiments of the present invention, and the present invention can be used in various other combinations, modifications, and environments. In other words, changes or modifications are possible within the scope of the inventive concept disclosed in this specification, the scope equivalent to the written disclosure, and / or the scope of technology or knowledge in the art. The written embodiments illustrate the best possible state for implementing the technical idea of ​​the present invention, and various modifications required for specific application fields and uses of the present invention are also possible. Therefore, the detailed description of the invention above is not intended to limit the present invention to the disclosed embodiments. Furthermore, the appended claims should be construed to include other embodiments.

Claims

1. A body measurement unit that measures body data related to body functions, Includes a server unit that receives and analyzes the above body data, A non-face-to-face medical treatment assistance system, characterized in that the server unit includes a physical function evaluation unit that determines the user's condition through analysis of the physical data.

2. In paragraph 1, A non-face-to-face medical treatment assistance system, characterized in that the above-mentioned physical function evaluation unit includes a data reception module that receives the physical data from the physical measurement unit, and a data analysis module that is connected to the data reception module and analyzes the received physical data to evaluate the user's gait.

3. In paragraph 2, The above data analysis module is a non-face-to-face medical treatment assistance system characterized in that it classifies data in a direction in which the data dispersion is reduced and evaluates the user's gait as normal gait and abnormal gait.

4. In paragraph 3, A non-face-to-face medical treatment assistance system characterized in that the data analysis module includes a spatial symmetry analysis module that calculates a spatial symmetry ratio (α / β) by dividing the knee angle (α) of the affected side by the knee angle (β) of the healthy side, and compares the calculated spatial symmetry ratio (α / β) with a reference spatial symmetry ratio, and determines as true a ratio that is less than or equal to the reference spatial symmetry ratio, and as false a ratio that is greater than the reference spatial symmetry ratio.

5. In paragraph 3, A non-face-to-face medical treatment assistance system characterized in that the data analysis module includes a healthy side swing phase analysis module that calculates the swing phase ratio of the healthy side, compares the calculated swing phase ratio of the healthy side with a reference swing phase ratio, and sets a swing phase ratio smaller than or equal to the reference swing phase ratio as true, and a swing phase ratio larger than the reference swing phase ratio as false.

6. In paragraph 3, The above data analysis module is a non-face-to-face medical treatment assistance system characterized in that it includes an affected-side shin pitch analysis module that calculates a shin pitch range of the affected side, compares the calculated shin pitch range of the affected side with a shin pitch range of the reference affected side, and sets a value smaller than or equal to the shin pitch range of the reference affected side as True, and a value larger than the shin pitch range of the reference affected side as False.

7. In paragraph 3, A non-face-to-face medical treatment assistance system characterized in that the data analysis module includes an affected-side thigh pitch analysis module that calculates a thigh pitch range of the affected side, compares the calculated thigh pitch range of the affected side with the thigh pitch range of the reference affected side, and sets a value smaller than or equal to the thigh pitch range of the reference affected side as True, and a value larger than the thigh pitch range of the reference affected side as False.

8. In paragraph 3, A non-face-to-face medical treatment assistance system characterized in that the data analysis module includes a healthy side shin pitch analysis module that calculates a shin pitch range of the healthy side, compares the calculated shin pitch range of the healthy side with a shin pitch range of the reference healthy side, and sets a value smaller than or equal to the shin pitch range of the reference healthy side as True, and a value larger than the shin pitch range of the reference healthy side as False.

9. In paragraph 3, A non-face-to-face medical treatment assistance system characterized in that the data analysis module includes a healthy thigh pitch analysis module that calculates a thigh pitch range of the healthy side, compares the calculated thigh pitch range of the healthy side with the thigh pitch range of the reference healthy side, and sets a value smaller than or equal to the thigh pitch range of the reference healthy side as True, and a value larger than the thigh pitch range of the reference healthy side as False.

10. In paragraph 3, A non-face-to-face medical treatment assistance system, characterized in that the above physical function evaluation unit includes a gait judgment module that is connected to the data analysis module and outputs a class when the Gini coefficient is 0.

11. In paragraph 1, A non-face-to-face medical treatment assistance system, characterized in that the above measurement unit includes a first tracker located on the user's shin side and a second tracker located on the user's thigh side.

12. In any one of paragraphs 1 to 11, A non-face-to-face medical treatment assistance system characterized in that the server unit includes a self-questionnaire unit that generates questionnaire data and transmits it to a user terminal side, and receives and analyzes response data from the user terminal side to the questionnaire data.

13. In paragraph 12, A non-face-to-face medical treatment assistance system, characterized in that the server unit includes a daily record unit that generates alarm data and transmits it to the user terminal side, and receives and analyzes the user terminal side's record data regarding the alarm data.

14. In paragraph 13, A non-face-to-face medical treatment assistance system characterized in that the server unit includes a non-face-to-face medical treatment unit that is connected to the physical function evaluation unit, the self-questionnaire unit, and the daily record unit, and transmits user data to a medical staff terminal side and receives medical treatment data from the medical staff terminal side.

15. In paragraph 14, A non-face-to-face medical treatment assistance system, characterized in that the server unit includes a customized prescription unit that provides a customized prescription to the user terminal side based on the medical data received in the non-face-to-face medical treatment unit.

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