WO2020184926A1 - Biometric information analysis method - Google Patents

Biometric information analysis method Download PDF

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Publication number
WO2020184926A1
WO2020184926A1 PCT/KR2020/003229 KR2020003229W WO2020184926A1 WO 2020184926 A1 WO2020184926 A1 WO 2020184926A1 KR 2020003229 W KR2020003229 W KR 2020003229W WO 2020184926 A1 WO2020184926 A1 WO 2020184926A1
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Prior art keywords
information
exercise
sensing
server
output
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PCT/KR2020/003229
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French (fr)
Korean (ko)
Inventor
최윤제
김영진
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(주)스포투
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Priority claimed from KR1020190149194A external-priority patent/KR102305591B1/en
Application filed by (주)스포투 filed Critical (주)스포투
Publication of WO2020184926A1 publication Critical patent/WO2020184926A1/en

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/103—Detecting, measuring or recording devices for testing the shape, pattern, colour, size or movement of the body or parts thereof, for diagnostic purposes
    • A61B5/11—Measuring movement of the entire body or parts thereof, e.g. head or hand tremor, mobility of a limb
    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
    • A61B5/316—Modalities, i.e. specific diagnostic methods
    • A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
    • A—HUMAN NECESSITIES
    • A63—SPORTS; GAMES; AMUSEMENTS
    • A63B—APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT
    • A63B24/00—Electric or electronic controls for exercising apparatus of preceding groups; Controlling or monitoring of exercises, sportive games, training or athletic performances
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Definitions

  • the present invention relates to a method of analyzing biometric information, and more particularly, to a method of receiving biometric information sensed by a sensing device, analyzing it, and calculating information including exercise information or performance indicators.
  • biometric information sensing devices As interest in health increases, various types of biometric information sensing devices and biometric information analysis methods are being developed. In addition, as various wearable devices that can be directly worn by users are spreading, devices specialized for healthcare are being developed.
  • the conventional wearable type biometric information sensing device and biometric information analysis method are specialized in outdoor exercise or tactical training analysis due to high dependence on location information including GPS, and multiplayer monitoring is performed with soccer, basketball and It was only limitedly introduced and used in group sports of the same team, and there was a problem that it was not suitable for routine-based personal exercise performed indoors.
  • the conventional biometric information analysis method has a problem in that it provides only a performance index suitable for group sports mainly in a team unit, such as an activity amount, and does not provide a performance index suitable for an individual exercise performed indoors.
  • the problem to be solved by the present invention is not limited to measuring the amount of activity that relies on GPS, and by sensing and analyzing information about the user's respiration, electrocardiogram, oxygen saturation, body temperature, or motion, It is to provide a precise biometric information analysis method that can be actively used in personal exercise.
  • the problem to be solved by the present invention is to calculate accurate exercise information (for example, exercise frequency information) by sensing precise breathing information by a fiber-type breathing sensor and analyzing it by matching it with other biometric information sensed at the same time. This is to provide a method for analyzing biometric information.
  • the problem to be solved by the present invention is to provide a performance index suitable for a routine-based personal exercise performed indoors by analyzing biometric information.
  • the breathing information is sensed by a method in which a fiber-type breathing sensor coated with carbon nanotubes detects a change in the volume of the chest, and breathing Include pattern, breathing frequency, or volume.
  • the exercise information generating step includes the step of generating, by a server, exercise type information by applying a characteristic value for each exercise, and the exercise
  • the star feature value includes a feature value of horizontal, vertical or rotational motion for each type of exercise.
  • the exercise information generation step comprises: constructing a data set based on the received sensing information, and the sensing information and the data set And generating exercise type information by deep learning on the basis of.
  • the data set is constructed by matching one or more of the sensing information and exercise type information.
  • the server in the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the server generates exercise information by comparing a plurality of sensing information for the same time to correspond to time information. It includes the step of.
  • the index calculation step is a step of calculating the performance index by deep learning
  • the performance index is consistency, accuracy, and time required.
  • Count or predicted record and the consistency means the degree of correspondence of each performance action in the repetitive performance of the same exercise action
  • the accuracy means the degree of agreement with the reference action of each performance action
  • the required time Is the total time spent on exercise, the time spent on performing the exercise, or the time taken to take a break during exercise, and the count includes the number of movements, the number of sets, or the number of times that the correct movement was not performed
  • the expected Records include those that mean predicted records calculated based on progression speed and physical strength.
  • the method for analyzing biometric information according to another embodiment of the present invention for solving the above-described problem further includes the step of receiving, by the server, authentication information of the user input from the sensing device.
  • an output information generation step in which a server generates output information based on the sensing information, the exercise information, or the performance index, and the server outputs the output information. It further comprises an output information transmission step of transmitting the information to the sensing device or the client device, the output information includes the information output from the output unit of the sensing device or the client device.
  • the output information includes the sensing information, the exercise information, or change information of an output lighting color corresponding to a change in the performance indicator. Includes that.
  • the output information includes the sensing information, the exercise information, or recommended exercise information generated based on the performance index. do.
  • the client device includes a user client device or a trainer client device, and the output information transmission step is received from a plurality of sensing devices. And transmitting a plurality of the output information corresponding to the sensing information to one trainer client device.
  • the biometric information analysis program according to another embodiment of the present invention for solving the above-described problems is combined with a computer which is hardware, and is stored in a medium to execute any one of the above-described methods.
  • a biometric information analysis server device for solving the above-described problem includes a communication unit that receives sensing information from a sensing device or transmits sensing information, exercise information, or performance indicators to the sensing device or a client device, An analysis unit that generates exercise information based on the sensing information and calculates a performance index based on the sensing information or the exercise information, and a storage unit that stores the sensing information, the exercise information, or the performance index, and the sensing
  • the information includes respiration, electrocardiogram, oxygen saturation, temperature, location or motion information
  • the exercise information includes exercise type, exercise frequency, or exercise intensity information.
  • the user by receiving the output information by lighting from the sensing device for the exercise information or the performance indicator, the user can receive information on the exercise performance even without having a client device (eg, a smartphone) close to each other. It can be obtained easily.
  • a client device eg, a smartphone
  • a plurality of users wear a sensing device and communicate with one trainer client device through a server, so that a trainer can monitor a plurality of users without space constraints in group training, You can provide personalized feedback.
  • FIG. 1 is a diagram showing the configuration of a biometric information sensing device for receiving sensing information according to an embodiment of the present invention.
  • FIG. 2 is a diagram illustrating a configuration of a housing of a biometric information sensing device for receiving sensing information according to an embodiment of the present invention.
  • FIG. 3 is a diagram illustrating a method of analyzing biometric information according to an embodiment of the present invention.
  • FIG. 4 is a diagram illustrating a server according to an embodiment of the present invention.
  • FIG. 5 is a view for explaining an analysis unit according to an embodiment of the present invention.
  • FIG. 6 is a diagram illustrating a graph of time of biometric information sensed at the same time according to an embodiment of the present invention.
  • FIG. 7 is a diagram illustrating a performance index according to an embodiment of the present invention.
  • FIG. 8 is a diagram for explaining a method for analyzing biometric information including generating and transmitting output information according to an embodiment of the present invention.
  • FIG. 9 is a diagram showing a configuration of an output unit of a biometric information sensing device for outputting output information according to an embodiment of the present invention.
  • FIG. 10 is a diagram illustrating a biometric information sensing device for changing and outputting an output lighting color according to an embodiment of the present invention and a wearing state.
  • 11 and 12 are diagrams for explaining a screen for providing biometric information, exercise information, or performance index according to an embodiment of the present invention.
  • FIG. 13 is a diagram illustrating a communication relationship between a biometric information sensing device and a client device according to an embodiment of the present invention.
  • 'biometric information' is information received from a biometric information sensing device, and may include respiration, electrocardiogram, oxygen saturation, body temperature, location, or motion information, but is not limited thereto and includes all information on the user's body. can do.
  • 'exercise information' is information on an exercise performed by a user, which is generated by analyzing received biometric information, and may include exercise type, exercise frequency, exercise intensity, or exercise time information, but is not limited thereto. Can contain all information about the exercise being performed.
  • the'performance indicator' means an index calculated based on biometric information or exercise information in order to provide information on the result of an exercise performed by a user.
  • the term "computer” includes all various devices capable of performing arithmetic processing.
  • computers are not only desktop PCs and notebooks, but also smart phones, tablet PCs, cellular phones, PCS phones, and synchronous/asynchronous systems.
  • a mobile terminal of the International Mobile Telecommunication-2000 (IMT-2000), a Palm Personal Computer (PC), a personal digital assistant (PDA), and the like may also be applicable.
  • IMT-2000 International Mobile Telecommunication-2000
  • PC Palm Personal Computer
  • PDA personal digital assistant
  • a'client device' refers to all devices including a communication function that users can use by installing a program (or application). That is, the client device is a cellular phone, a PCS phone (Personal Communication Service phone), a synchronous/asynchronous mobile terminal of the International Mobile Telecommunication-2000 (IMT-2000), a Palm Personal Computer (Palm Personal Computer), Personal Digital Assistant (PDA), Smart phone, WAP phone (Wireless Application Protocao phone), mobile game console, tablet PC, smart watch, notebook PC, desktop PC, smart camera, smart TV It may include various communication devices such as. Further, the client device does not basically include a communication function, but may include a device capable of performing communication by combining a memory chip having a communication function.
  • FIG. 1 is a diagram showing a configuration of a biometric information sensing device for receiving sensing information according to an embodiment of the present invention
  • FIG. 2 is a biometric information sensing device for receiving sensing information according to an embodiment of the present invention. It is a diagram showing the configuration of the housing.
  • a biometric information sensing device 10 for receiving sensing information includes a housing 100, a wearing part 200 coupled to one or both sides of the housing, and the It may include a fiber-type breathing sensor 300 located on the wearing part.
  • the housing 100 of the biometric information sensing device for receiving sensing information may include a sensor unit 110, a power supply unit 120, or a control unit 130.
  • the sensor unit may include an ECG sensor 112, an oxygen saturation sensor 114, a temperature sensor 116, or a motion sensor 118, and various sensors capable of sensing biometric information may be included without being limited thereto. have.
  • the power supply unit 120 supplies power necessary for driving the biometric information sensing device, and may include a battery, and the controller 130 can control overall operations related to the biometric information sensing device, and each component It is connected to the field and can control the operation of each component.
  • the wearing part 200 is coupled to one side or both sides of the housing, and may be a band that can be worn around the user's chest, more preferably, due to the change in the volume of the chest due to the user's breathing. It may be a band having elasticity so that it can be stretched or contracted accordingly.
  • the wearing unit may further include an adjustment unit capable of adjusting a length according to the user's chest circumference.
  • the fibrous breathing sensor 300 may refer to a breathing sensor that is located on a part of the wearing part 200 and senses the user's breathing information by detecting a change in the volume of the chest caused by the user's breathing.
  • the fiber-type breathing sensor is coated with a carbon nanotube (CNT) on one side of the wearing part in the form of a band surrounding the user's chest, and carbon generated by volume change due to contraction and expansion of the chest during the user's breathing.
  • CNT carbon nanotube
  • the deformation rate of the wearing part to which the carbon nanotube is applied can be sensed, and through this, the respiratory information including the breathing pattern, the number of breaths, or the amount of breathing can be sensed.
  • the change in the volume of the chest may be detected in nano-millimeter units, but is not limited thereto.
  • a step for removing noise from the breathing information may be included.
  • the fiber-type breathing sensor is a step of attaching a polyurethane film as a buffer layer to one side of the wearing part made of an elastic band, applying a carbon nanotube on the polyurethane film It may be manufactured by a method including the step of, thermal curing the carbon nanotube layer, attaching a polyurethane film on the carbon nanotube layer as a protective layer, or forming an electrode terminal.
  • the polyurethane film may be attached by a thermal transfer printing technique, and the carbon nanotubes may be applied by a screen printing technique.
  • the thickness of the applied carbon nanotube layer may be 50 to 300 ⁇ m, and the application shape, length, or area may be variously changed according to the design of the biometric information sensing device.
  • the thermal curing step may include thermal curing using an infrared conveyor dryer, and the polyurethane film attached as the protective layer may include a transparent film or an opaque film,
  • the electrode terminal may include an eyelet, a metal wire, or a copper thin film.
  • the ECG sensor 112 may include one or more ECG electrodes 113 for detecting a user's ECG signal, and the plurality of ECG electrodes are located on both sides of the housing so that they can contact the body at a spaced apart position. It can be located on the wearing part.
  • the ECG electrode may include a conductive silicon ECG electrode.
  • a conversion unit for converting the detected ECG signal into heart rate information may be further included in the housing 100, and the ECG signal may include amplitude data of an ECG QRS waveform.
  • the oxygen saturation sensor 114 may include an optical sensor that senses blood oxygen saturation (SpO2) data using a degree of absorption of a specific wavelength of light, and the body temperature sensor 116 It may include a temperature sensor that measures the body temperature of the user by contacting.
  • the oxygen saturation sensor or the body temperature sensor may be modularized to increase the recognition rate.
  • the motion sensor 116 may include an acceleration sensor, an angular velocity sensor, or a geomagnetic sensor.
  • the angular velocity sensor may mean a gyro sensor, but is not limited thereto, and the motion sensor may sense acceleration information, angular velocity information, or geomagnetic information to generate motion information about the movement of a user wearing the sensing device. .
  • the plurality of sensors for sensing the biometric information may be configured as a module including a detection unit for detecting a biosignal and a conversion unit for converting the detected biosignal into biometric information.
  • biometric information sensing device for receiving sensing information according to an embodiment of the present invention
  • the device for receiving sensing information of the present invention is not limited thereto, and the user's biometric information Includes all sensing devices capable of sensing.
  • the method for analyzing biometric information according to an embodiment of the present invention includes an information receiving step (S100) in which a server receives sensing information, and an exercise information generation in which the server generates exercise information based on the sensing information.
  • Step (S110) an index calculation step (S120) in which a server calculates a performance index based on the sensing information or exercise information, and a storage step (S130) in which the server stores the sensing information, the exercise information or the performance index.
  • S100 information receiving step
  • an exercise information generation in which the server generates exercise information based on the sensing information.
  • Step (S110) an index calculation step (S120) in which a server calculates a performance index based on the sensing information or exercise information
  • a storage step (S130) in which the server stores the sensing information, the exercise information or the performance index.
  • the sensing information means biometric information including respiration, electrocardiogram, oxygen saturation, body temperature, location, or motion information sensed by the sensing device, and the exercise information is an exercise type as a result of analysis by the server based on the sensing information.
  • Exercise frequency or exercise intensity information may be included.
  • the respiration information may include that the above-described carbon nanotube-coated fibrous respiration sensor is sensed by a method of detecting the chest volume change according to the user's breathing, the respiration information , Breathing frequency or volume.
  • the server 20 may include an analysis unit 700, and the analysis unit is an exercise information recognition model that generates exercise information based on biometric information. It may include 720.
  • the server may receive sensing information from the biometric information sensing device and input the received sensing information into an exercise information recognition model to generate exercise information.
  • the exercise information recognition model 720 may be configured not only as a single recognition module, but also may be configured to include a plurality of recognition modules, for example, exercise type information recognition module 722, exercise frequency information A recognition module 724 or an exercise intensity information recognition module 726 may be included.
  • the exercise information generation step (S110) in which the server generates exercise information based on the sensing information may include the step of generating exercise type information by applying the exercise-specific feature value by the server, and
  • the feature value may include a feature value of horizontal, vertical, or rotational motion for each type of motion.
  • motion information including acceleration, angular velocity, or slope of the user sensed by the motion sensor is received and input to the exercise type information recognition module 722, and the exercise type information recognition module includes the input motion information and Specific exercise type information may be generated by matching exercise type information including characteristic values of the matched motion.
  • the exercise information generation step may include a data selection step, a preprocessing step, an auto counting step, a data feature extraction step, or a motion recognition step.
  • the data selection step may mean selecting data to be used for analysis from among various sensing information.
  • the pre-processing step is a step of removing a section that will not be used for analysis (e.g., a preparation section before and after exercise or a finishing section of 10 seconds) from the data to be used for analysis (for example, 3-axis acceleration data).
  • a section that will not be used for analysis e.g., a preparation section before and after exercise or a finishing section of 10 seconds
  • processing data e.g, magnitude
  • removing noise eg, butterworth bandpass filtering
  • it may include a conversion section processing step.
  • the analysis unit may include not only a fixed size, but also various sizes (eg, the length of an exercise motion (1rep) calculated by auto counting).
  • the auto-counting step may mean counting the number of peaks in a certain section for data from which noise has been removed after pre-processing, and the auto-counting step includes a plurality of different types for the same time. Accuracy can be improved by applying to sensing information.
  • the data feature extraction step refers to a step of extracting a feature to be used for motion recognition for data from which noise has been removed after processing (for example, 3-axis acceleration data or angular velocity data), and the data feature is averaged ), Standard Deviation, Correlation, Peak Interval or Peak Amplitude.
  • the motion recognition step is a step of recognizing an action performed by a classification algorithm using the data feature
  • the classification algorithm includes hierarchical clustering (Hierarchical Method), logistic regression, and K-NN (K- nearest neighbors), Decision Tree, Random Forest, support vector machine (SVM), Na ve Bayes, Hidden Markov Models (HMMs), or May include, but is not limited to, artificial neural networks including RNN or CNN.
  • the exercise information generation step (S110) in which the server generates exercise information based on the sensing information is a step of constructing a data set based on the received sensing information, and deep learning based on the sensing information and the data set.
  • it may include the step of generating exercise type information.
  • the data set may include one that is constructed by matching one or more sensing information and exercise type information.
  • the server acquires sensing information sensed when a user exercising by wearing a biometric information sensing device performs a single type of exercise, and may construct a dataset by matching the sensing information with the exercise type information.
  • the dataset can be expressed as follows.
  • the sensed biometric information is body temperature (36.5°C), heart rate (180bpm), respiration (25bpm), oxygen saturation (99.5%), and triaxial values.
  • speed 0.010, 0.001, 0.20
  • 3-axis Euler angle 0.1, 0, 0
  • the exercise information recognition model or the exercise type information recognition module may be trained through a deep learning learning model based on the data set. That is, the exercise information recognition model or exercise type information recognition module is built with a specific deep learning algorithm, and learning is performed by matching specific exercise type information and biometric information sensed during execution of the corresponding exercise information based on the dataset. Can be.
  • the exercise information recognition model, the performance index calculation model or the individual exercise information recognition module, and the performance index recognition module may be formed as an artificial neural network composed of multi-layers.
  • the deep learning algorithm may include a CNN, RNN, LSTM, or GRU scheme, but is not limited thereto.
  • the exercise information generation step in the biometric information analysis method is a step of generating exercise information by comparing a plurality of sensing information for the same time to correspond to time information. It may include. That is, the sensing information may further include information about time, and the time information may mean a time at which the biometric information was sensed. By matching and comparing a plurality of sensing information according to time, the server can calculate precise exercise information or performance index.
  • the server may conveniently and accurately analyze the exercise information by matching the received sensing information with a graph against time as shown in FIG. 6. For example, when a sensing device user performs a'squat' exercise, by comparing the user's breathing information 50 and motion information 60 sensed at the same time in time, including exercise frequency information and exercise amount information Exercise information can be analyzed more precisely.
  • a regular breathing pattern e.g., inhalation during a down motion and exhalation during an up motion
  • a strong load is applied to the muscles (e.g., When it reaches the lowest point in the squat), the bracing point is measured and compared with motion information (e.g., up and down movements) to allow precise analysis of the number of exercise. It is possible to analyze exercise intensity or amount of exercise by using the point that the stopping time is longer.
  • the step of calculating the performance indicator may be performed through a deep learning algorithm.
  • the server 20 may include an analysis unit 700, and the analysis unit may include a performance index calculation model 740.
  • the server may calculate the performance index by inputting the biometric information received from the biometric information sensing device or the exercise information generated by the exercise information recognition model 720 into the performance index calculation model 740.
  • the performance indicator calculation model 740 may be configured not only as a single recognition module, but also may be configured to include a plurality of recognition modules.
  • the performance indicator may include consistency, accuracy, time required, count, or predicted recording, but is not limited thereto.
  • Consistency In Action means the degree of agreement with each other in the repetitive performance of the same motion motion, and the accuracy can mean the degree of agreement with the reference motion of each repetitive action. For example, when the user repeatedly performs the squat 10 times (10 rep), the consistency is the degree of correspondence between each 1 rep movement, and the accuracy may mean the degree of correspondence of each 1 rep movement to the movement of the correct posture.
  • the time required may include a total time spent in exercise, a time spent in performing an exercise motion, or a time taken for a break during exercise.
  • the time required may include a total time spent in exercise, a time spent in performing an exercise motion, or a time taken for a break during exercise.
  • the count may include the number of times the operation is performed, the number of sets, or the number of times that the correct operation is not performed.
  • the predicted record may be calculated based on the user's progressing speed for a specific goal and remaining physical strength. For example, in the case of measuring a time record for a predetermined number of repetitions in CrossFit, an expected record may be calculated based on the user's current pace and remaining physical strength by analyzing sensing information or exercise information.
  • the performance indicators include exercise readiness before exercise, physical stability after exercise, physical fitness of the user, maximum oxygen intake, number of possible movements per minute (RPM), and the section where breathing was stopped due to a strong exercise load during exercise (Bracing Point). , It may include calories burned during exercise or power for movement during exercise, but is not limited thereto.
  • the method for analyzing biometric information may further include receiving authentication information of a user input from the sensing device.
  • the authentication information may include ID, PW, or biometric information including fingerprint, iris, and face recognition.
  • the server may receive sensing information corresponding to the user's authentication information by not only the user's personal sensing device but also a common sensing device provided in the fitness center, The sensing information, exercise information, or performance index may be stored or transmitted to a sensing device or a client device. That is, by authenticating (logging in) the authentication information through a common sensing device, the user can check or manage his or her own biometric information, exercise information, and records through the server, and compare the records through sharing with others.
  • an output information generation step in which a server generates output information based on sensing information, exercise information, or performance index, and a server sensing output information Alternatively, it may further include the step of transmitting the output information transmitted to the client device.
  • the output information is information output from the sensing device or the output unit of the client device, and may include image information or audio information, and may include sensing information, exercise information, or information on performance indicators, but is not limited thereto. .
  • FIG. 9 is a diagram showing a configuration of an output unit of a biometric information sensing device for outputting output information according to an embodiment of the present invention
  • FIG. 10 is a diagram for outputting by changing an output lighting color according to an embodiment of the present invention.
  • a diagram for explaining a biometric information sensing device and a wearing state. 9 and 10, in one embodiment, the biometric information sensing device for receiving and outputting output information from a server may further include an output unit 400 on the front surface of the housing 100, and the output unit 400 may include an illumination unit 410 or a display unit 420.
  • the lighting unit 410 may include a device that changes and outputs an illumination color under the control of a controller in response to changes in biometric information sensed by a sensor or information received from a server or a client device, and includes an LED.
  • a device that changes and outputs an illumination color under the control of a controller in response to changes in biometric information sensed by a sensor or information received from a server or a client device, and includes an LED.
  • the user can easily obtain biometric information or exercise information by changing the lighting color or the presence or absence of lighting without putting a client device (eg, a smartphone) close.
  • a client device eg, a smartphone
  • the trainer can easily recognize the user's biometric information, exercise information, or the presence or absence of injuries, thereby improving the efficiency of exercise. There is an effect that can prevent injury to the user.
  • the user may set the lighting color change reference information
  • the server may receive the setting reference information and generate and transmit output information including the change information of the output lighting color based thereon.
  • a normal range for specific biometric information may be set, and when the biometric information falls within the normal range, green illumination may be output, but when the biometric information is out of the normal range, red illumination may be output.
  • the server When setting reference information is received and the heart rate information received in real time is analyzed, output information that changes to be output as red light when the heart rate falls below 140 may be generated, and transmitted to the sensing device. In this case, there is an effect that the user can easily receive feedback on biometric information or exercise information by the output light color.
  • feedback on the exercise posture may be received by setting to output green light when a user performs an accurate motion for a specific exercise and red light when performing an incorrect motion.
  • the illumination color change reference information may include a plurality of reference information. That is, the user may set a plurality of criteria, and the server may receive the plurality of reference information, generate output information for each reference information, and transmit it to a sensing device or a client device.
  • the server when the user is set to output yellow light when a user detects an injury risk sign at the same time, the server receives the operation accuracy reference information and the injury risk reference information, and generates and transmits output information for each standard.
  • the standard for changing the lighting color is not limited thereto, and a user may set variously.
  • the output unit 400 of the sensing device may include a display unit 420 and the output unit may include both a lighting unit and a display unit.
  • the output information may simultaneously include change information of the output lighting color and sensing information, exercise information, or performance index information about the change information of the output lighting color.
  • the lighting unit when a user exercises by setting the heart rate 140 as the lighting color change reference information, the lighting unit may output illumination of a color corresponding to the reference, and the display unit may display the user's heart rate.
  • the user can simply obtain information (whether or not the heart rate exceeds 140) according to the illumination color, and when the illumination color changes, specific heart rate information can be obtained by checking the display unit.
  • the output information may include recommended exercise information generated based on the sensing information, exercise information, or performance index.
  • the recommended exercise information is generated based on personal biometric information, exercise information, or performance index, and may include accurate posture or breathing method, warm-up exercise information, or finishing exercise information of a specific exercise performed by the user, but is limited thereto. It is not.
  • 11 and 12 are diagrams for explaining a screen for providing biometric information, exercise information, or performance index according to an embodiment of the present invention.
  • the output information may be information output from a client device (eg, a smartphone).
  • the output information may be numerical or graphed information of the user's biometric data, and may include exercise information or performance indicators.
  • Communication includes wired or wireless, for example, Bluetooth communication, Bluetooth Low Energy (BLE) communication, near field communication unit, WLAN (Wi-Fi) communication, Zigbee communication, infrared (IrDA, infrared Data Association) communication, WFD (Wi-Fi Direct) communication, UWB (ultra wideband) communication, Ant+ communication WIFI communication method can be used to communicate, but is not limited thereto.
  • BLE Bluetooth Low Energy
  • Wi-Fi Wi-Fi
  • Zigbee communication infrared (IrDA, infrared Data Association) communication
  • WFD Wi-Fi Direct
  • UWB ultra wideband
  • Ant+ communication WIFI communication method can be used to communicate, but is not limited thereto.
  • the communication relationship may include a plurality of biometric information sensing devices communicating with one trainer client device (FIG. 13(a)) or communicating through a server (FIG. 13(b)).
  • information on a plurality of users performing an exercise in the same place as well as in different places may be transmitted to one trainer.
  • the trainer can monitor a large number of remote people in real time without space constraints.
  • a server device 20 for performing a biometric information analysis method may receive sensing information from a sensing device, or receive sensing information, exercise information, or performance indicator.
  • the communication unit 500 that transmits the sensor to the sensing device 10 or the client device 30, the analysis unit 700 that generates exercise information based on the sensing information and calculates a performance index based on the sensing information or exercise information, and sensing It may include a storage unit 600 for storing information, exercise information or performance index.
  • the biometric information analysis method which is a method according to an embodiment of the present invention described above, may be implemented as a biometric information analysis computer program (or application) to be executed by combining a computer as hardware and stored in a medium.
  • the steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof.
  • the software module includes Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.

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Abstract

A biometric information analysis method is provided. The biometric information analysis method comprises: an information reception step for receiving, by a server, sensing information; an exercise information generation step for generating, by the server, exercise information on the basis of the sensing information; an indicator calculation step for calculating, by the server, a performance indicator on the basis of the sensing information or the exercise information; a storage step for storing, by the server, the sensing information, the exercise information, or the performance indicator; a step for receiving, by the server, authentication information of a user input from a sensing device; an output information generation step for generating, by the server, output information on the basis of the sensing information, the exercise information, or the performance indicator; and an output information transmission step for transmitting, by the server, the output information to the sensing device or a client device, wherein the sensing information is acquired by sensing biometric information comprising respiration, electrocardiogram, oxygen saturation, body temperature, location, or motion information, the exercise information comprises information about an exercise type, the number of exercises, or exercise intensity, and the exercise information generation step or the indicator calculation step uses a deep learning algorithm.

Description

생체정보 분석 방법Biometric information analysis method
본 발명은 생체정보 분석 방법에 관한 것으로, 보다 자세하게는 센싱 장치에 의해 센싱된 생체정보를 수신하고, 이를 분석하여 운동정보 또는 성능지표를 포함하는 정보를 산출하는 방법에 관한 것이다.The present invention relates to a method of analyzing biometric information, and more particularly, to a method of receiving biometric information sensed by a sensing device, analyzing it, and calculating information including exercise information or performance indicators.
건강에 관한 관심이 증가됨에 따라 다양한 종류의 생체정보 센싱 장치 및 생체정보 분석 방법이 개발되고 있다. 또한, 사용자가 직접 착용할 수 있는 다양한 웨어러블 디바이스(wearable device)가 보급되면서, 헬스 케어에 특화된 기기들이 개발되고 있다.As interest in health increases, various types of biometric information sensing devices and biometric information analysis methods are being developed. In addition, as various wearable devices that can be directly worn by users are spreading, devices specialized for healthcare are being developed.
그러나, 종래의 웨어러블 형태의 생체정보 센싱 장치 및 생체정보 분석 방법은, GPS를 포함하는 위치정보에 대한 의존도가 높아 실외운동이나 전술훈련 분석에 특화되어 있으며, 멀티플레이어에 대한 모니터링은 축구, 농구와 같은 팀단위의 단체 스포츠에 제한적으로 도입되어 사용될 뿐, 실내에서 이루어지는 루틴 기반의 개인 운동에 적합하지 않은 문제점이 있었다.However, the conventional wearable type biometric information sensing device and biometric information analysis method are specialized in outdoor exercise or tactical training analysis due to high dependence on location information including GPS, and multiplayer monitoring is performed with soccer, basketball and It was only limitedly introduced and used in group sports of the same team, and there was a problem that it was not suitable for routine-based personal exercise performed indoors.
또한, 종래의 생체정보 분석 방법에 의하면 각 생체정보 센싱결과의 부정확성 및 각 생체정보에 대해 별개로 분석함으로 인하여, 센싱결과에 대해 분석한 운동종류, 운동횟수 또는 운동량을 포함하는 운동정보의 정확성이 떨어지는 문제점이 있었다.In addition, according to the conventional biometric information analysis method, since the inaccuracy of each biometric information sensing result and each biometric information are separately analyzed, the accuracy of the exercise information including the type of exercise, the number of exercise or the amount of exercise analyzed for the sensing result is reduced. There was a falling problem.
또한, 종래의 생체정보 분석 방법은 활동량과 같은 주로 팀단위의 단체스포츠에 적합한 성능지표만 제공하고, 실내에서 이루어지는 개인 운동에 적합한 성능지표를 제공하지 않는 문제점이 있었다.In addition, the conventional biometric information analysis method has a problem in that it provides only a performance index suitable for group sports mainly in a team unit, such as an activity amount, and does not provide a performance index suitable for an individual exercise performed indoors.
본 발명이 해결하고자 하는 과제는 GPS에 의존하는 활동량 측정에 제한되지 않고, 사용자의 호흡, 심전도, 산소포화도, 체온 또는 동작에 관한 정보를 센싱하여 분석함으로써, 단체 스포츠뿐만 아니라 실내에서 이루어지는 루틴 기반의 개인 운동에 있어서 적극 활용될 수 있는 정밀한 생체정보 분석 방법을 제공하는 것이다.The problem to be solved by the present invention is not limited to measuring the amount of activity that relies on GPS, and by sensing and analyzing information about the user's respiration, electrocardiogram, oxygen saturation, body temperature, or motion, It is to provide a precise biometric information analysis method that can be actively used in personal exercise.
또한, 본 발명이 해결하고자 하는 과제는 섬유형 호흡센서에 의해 정밀한 호흡정보를 센싱하고, 동시간에 센싱된 다른 생체정보와 매칭하여 분석함으로써 정확한 운동정보(예를 들어, 운동횟수정보)의 산출이 가능한 생체정보 분석 방법을 제공하는 것이다.In addition, the problem to be solved by the present invention is to calculate accurate exercise information (for example, exercise frequency information) by sensing precise breathing information by a fiber-type breathing sensor and analyzing it by matching it with other biometric information sensed at the same time. This is to provide a method for analyzing biometric information.
또한, 본 발명이 해결하고자 하는 과제는 생체정보를 분석하여 실내에서 이루어지는 루틴 기반의 개인운동에 적합한 성능지표를 제공하는 것이다.In addition, the problem to be solved by the present invention is to provide a performance index suitable for a routine-based personal exercise performed indoors by analyzing biometric information.
본 발명이 해결하고자 하는 과제들은 이상에서 언급된 과제로 제한되지 않으며, 언급되지 않은 또 다른 과제들은 아래의 기재로부터 통상의 기술자에게 명확하게 이해될 수 있을 것이다.The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems that are not mentioned will be clearly understood by those skilled in the art from the following description.
상술한 과제를 해결하기 위한 본 발명의 일 실시예에 따른 생체정보 분석 방법은 서버가 센싱정보를 수신하는 정보수신단계, 서버가 상기 센싱정보를 기초로 운동정보를 생성하는 운동정보생성단계, 서버가 상기 센싱정보 또는 상기 운동정보를 기초로 성능지표를 산출하는 지표산출단계 및 서버가 상기 센싱정보, 운동정보 또는 상기 성능지표를 저장하는 저장단계를 포함하고, 상기 센싱정보는 호흡, 심전도, 산소포화도, 체온, 위치 또는 동작정보를 포함하는 생체정보가 센싱된 것이고, 상기 운동정보는 운동종류, 운동횟수 또는 운동강도 정보를 포함한다.The biometric information analysis method according to an embodiment of the present invention for solving the above-described problem includes an information receiving step in which a server receives sensing information, an exercise information generation step in which the server generates exercise information based on the sensing information, and a server A, an index calculation step of calculating a performance index based on the sensing information or the exercise information, and a storage step of storing the sensing information, exercise information or the performance index by the server, and the sensing information is respiration, electrocardiogram, oxygen Biometric information including saturation, body temperature, location, or motion information is sensed, and the exercise information includes exercise type, exercise frequency, or exercise intensity information.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 호흡정보는 탄소나노튜브가 도포된 섬유형 호흡센서가 흉부의 부피변화를 감지하는 방식에 의해 센싱되고, 호흡패턴, 호흡횟수 또는 호흡량을 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the breathing information is sensed by a method in which a fiber-type breathing sensor coated with carbon nanotubes detects a change in the volume of the chest, and breathing Include pattern, breathing frequency, or volume.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 운동정보 생성단계는, 서버가 운동별 특징값을 적용하여 운동종류정보를 생성하는 단계를 포함하고, 상기 운동별 특징값은 운동종류별 수평, 수직 또는 회전동작의 특징값을 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the exercise information generating step includes the step of generating, by a server, exercise type information by applying a characteristic value for each exercise, and the exercise The star feature value includes a feature value of horizontal, vertical or rotational motion for each type of exercise.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 운동정보생성단계는, 상기 수신한 센싱정보를 기초로 데이터셋을 구축하는 단계 및 상기 센싱정보 및 상기 데이터셋을 기초로 딥러닝에 의하여 운동종류정보를 생성하는 단계를 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the exercise information generation step comprises: constructing a data set based on the received sensing information, and the sensing information and the data set And generating exercise type information by deep learning on the basis of.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 상기 데이터셋은 하나 이상의 상기 센싱정보와 운동종류정보를 매칭하여 구축되는 것을 특징으로 하는 것을 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the data set is constructed by matching one or more of the sensing information and exercise type information.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 운동정보생성단계는, 서버가 동시간에 대한 복수의 센싱정보를 시간정보에 대응하도록 비교하여 운동정보를 생성하는 단계를 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, in the exercise information generation step, the server generates exercise information by comparing a plurality of sensing information for the same time to correspond to time information. It includes the step of.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 상기 지표산출단계는 딥러닝에 의하여 상기 성능지표를 산출하는 단계이고, 상기 성능지표는 일관성, 정확성, 소요시간, 카운트 또는 예상기록을 포함하고, 상기 일관성은 동일한 운동동작의 반복수행에 있어서 각 수행동작의 일치정도를 의미하고, 상기 정확성은 각 수행동작의 기준동작에 대한 일치정도를 의미하고, 상기 소요시간은 운동에 소요한 총 시간, 운동동작 수행에 소요한 시간 또는 운동중 휴식을 취한 시간을 포함하고, 상기 카운트는 동작수행 횟수, 세트수 또는 정확한 동작수행이 이루어지지 않은 횟수를 포함하고, 상기 예상기록은 진행속도 및 체력을 기초로 산출된 예상기록을 의미하는 것을 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the index calculation step is a step of calculating the performance index by deep learning, and the performance index is consistency, accuracy, and time required. , Count or predicted record, and the consistency means the degree of correspondence of each performance action in the repetitive performance of the same exercise action, and the accuracy means the degree of agreement with the reference action of each performance action, and the required time Is the total time spent on exercise, the time spent on performing the exercise, or the time taken to take a break during exercise, and the count includes the number of movements, the number of sets, or the number of times that the correct movement was not performed, and the expected Records include those that mean predicted records calculated based on progression speed and physical strength.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법은 서버가 센싱장치로부터 입력된 사용자의 인증정보를 수신하는 단계를 더 포함한다.The method for analyzing biometric information according to another embodiment of the present invention for solving the above-described problem further includes the step of receiving, by the server, authentication information of the user input from the sensing device.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법은 서버가 상기 센싱정보, 상기 운동정보 또는 상기 성능지표를 기초로 출력정보를 생성하는 출력정보생성단계 및 서버가 상기 출력정보를 센싱장치 또는 클라이언트 장치에 전송하는 출력정보전송단계를 더 포함하고, 상기 출력정보는 센싱장치 또는 클라이언트 장치의 출력부에서 출력되는 정보인 것을 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, an output information generation step in which a server generates output information based on the sensing information, the exercise information, or the performance index, and the server outputs the output information. It further comprises an output information transmission step of transmitting the information to the sensing device or the client device, the output information includes the information output from the output unit of the sensing device or the client device.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 상기 출력정보는 상기 센싱정보, 상기 운동정보 또는 상기 성능지표의 변화에 대응하는 출력조명색의 변경 정보를 포함하는 것을 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the output information includes the sensing information, the exercise information, or change information of an output lighting color corresponding to a change in the performance indicator. Includes that.
상술한 과제를 해결하기 위한 본 발명의 일 실시예에 따른 생체정보 분석 방법에 있어서, 상기 출력정보는 상기 센싱정보, 상기 운동정보 또는 상기 성능지표를 기초로 생성한 추천운동정보를 포함하는 것을 포함한다.In the biometric information analysis method according to an embodiment of the present invention for solving the above-described problem, the output information includes the sensing information, the exercise information, or recommended exercise information generated based on the performance index. do.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 방법에 있어서, 상기 클라이언트 장치는 사용자 클라이언트 장치 또는 트레이너 클라이언트 장치를 포함하고, 상기 출력정보전송단계는 복수의 센싱장치로부터 수신한 상기 센싱정보에 대응하는 복수의 상기 출력정보를 하나의 트레이너 클라이언트 장치에 전송하는 단계를 포함한다.In the biometric information analysis method according to another embodiment of the present invention for solving the above-described problem, the client device includes a user client device or a trainer client device, and the output information transmission step is received from a plurality of sensing devices. And transmitting a plurality of the output information corresponding to the sensing information to one trainer client device.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 프로그램은 하드웨어인 컴퓨터와 결합되어, 상술한 방법 중 어느 하나의 방법을 실행시키기 위해 매체에 저장된다.The biometric information analysis program according to another embodiment of the present invention for solving the above-described problems is combined with a computer which is hardware, and is stored in a medium to execute any one of the above-described methods.
상술한 과제를 해결하기 위한 본 발명의 다른 실시예에 따른 생체정보 분석 서버장치는, 센싱장치로부터 센싱정보를 수신하거나 센싱정보, 운동정보 또는 성능지표를 상기 센싱장치 또는 클라이언트 장치에 전송하는 통신부, 상기 센싱정보를 기초로 운동정보를 생성하고 상기 센싱정보 또는 상기 운동정보를 기초로 성능지표를 산출하는 분석부 및 상기 센싱정보, 상기 운동정보 또는 상기 성능지표를 저장하는 저장부를 포함하고, 상기 센싱정보는 호흡, 심전도, 산소포화도, 온도, 위치 또는 동작정보를 포함하고, 상기 운동정보는 운동종류, 운동횟수 또는 운동강도 정보를 포함한다.A biometric information analysis server device according to another embodiment of the present invention for solving the above-described problem includes a communication unit that receives sensing information from a sensing device or transmits sensing information, exercise information, or performance indicators to the sensing device or a client device, An analysis unit that generates exercise information based on the sensing information and calculates a performance index based on the sensing information or the exercise information, and a storage unit that stores the sensing information, the exercise information, or the performance index, and the sensing The information includes respiration, electrocardiogram, oxygen saturation, temperature, location or motion information, and the exercise information includes exercise type, exercise frequency, or exercise intensity information.
본 발명의 기타 구체적인 사항들은 상세한 설명 및 도면들에 포함되어 있다.Other specific details of the present invention are included in the detailed description and drawings.
상기 본 발명에 의하면, 다양한 생체정보를 수신하여 실내에서 이루어지는 루틴 기반의 개인운동에 적합한 운동정보 및 성능지표를 산출하여 제공할 수 있다.According to the present invention, it is possible to receive various biometric information and calculate and provide exercise information and performance indicators suitable for routine-based personal exercise performed indoors.
또한, 상기 본 발명에 의하면, 동시간에 센싱된 복수의 생체정보를 시간에 맞춰 비교하여 분석함으로써 정확한 운동정보 또는 성능지표를 제공할 수 있다.In addition, according to the present invention, it is possible to provide accurate exercise information or performance index by comparing and analyzing a plurality of biometric information sensed at the same time according to time.
또한, 상기 본 발명에 의하면, 사용자는 운동정보 또는 성능지표에 대하여 센싱 장치로부터 조명에 의한 출력정보를 제공받음으로써, 클라이언트 장치(예를 들어, 스마트폰)를 가까이 두지 않아도 운동수행에 관한 정보를 간편하게 획득할 수 있다.In addition, according to the present invention, by receiving the output information by lighting from the sensing device for the exercise information or the performance indicator, the user can receive information on the exercise performance even without having a client device (eg, a smartphone) close to each other. It can be obtained easily.
또한, 상기 본 발명에 의하면, 복수의 사용자가 센싱 장치를 착용하고, 서버를 통해 하나의 트레이너 클라이언트 장치와 통신함으로써, 그룹훈련에 있어서 공간의 제약 없이 트레이너가 복수의 사용자에 대해 모니터링이 가능하여, 개인 맞춤형 피드백을 제공할 수 있다.In addition, according to the present invention, a plurality of users wear a sensing device and communicate with one trainer client device through a server, so that a trainer can monitor a plurality of users without space constraints in group training, You can provide personalized feedback.
본 발명의 효과들은 이상에서 언급된 효과로 제한되지 않으며, 언급되지 않은 또 다른 효과들은 아래의 기재로부터 통상의 기술자에게 명확하게 이해될 수 있을 것이다.The effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.
도 1은 본 발명의 일 실시예에 따른 센싱정보를 수신하기 위한 생체 정보 센싱 장치의 구성을 도시한 도면이다.1 is a diagram showing the configuration of a biometric information sensing device for receiving sensing information according to an embodiment of the present invention.
도 2는 본 발명의 일 실시예에 따른 센싱정보를 수신하기 위한 생체정보 센싱 장치의 하우징의 구성을 도시한 도면이다.2 is a diagram illustrating a configuration of a housing of a biometric information sensing device for receiving sensing information according to an embodiment of the present invention.
도 3은 본 발명의 일 실시예에 따른 생체정보 분석 방법을 설명하기 위한 도면이다.3 is a diagram illustrating a method of analyzing biometric information according to an embodiment of the present invention.
도 4는 본 발명의 일 실시예에 따른 서버를 설명하기 위한 도면이다.4 is a diagram illustrating a server according to an embodiment of the present invention.
도 5는 본 발명의 일 실시예에 따른 분석부를 설명하기 위한 도면이다.5 is a view for explaining an analysis unit according to an embodiment of the present invention.
도 6은 본 발명의 일 실시예에 따른 동시간에 센싱된 생체정보의 시간에 대한 그래프를 도시한 도면이다.6 is a diagram illustrating a graph of time of biometric information sensed at the same time according to an embodiment of the present invention.
도 7은 본 발명의 일 실시예에 따른 성능지표를 설명하기 위한 도면이다.7 is a diagram illustrating a performance index according to an embodiment of the present invention.
도 8은 본 발명의 일 실시예에 따른 출력정보 생성 및 전송단계를 포함하는 생체정보 분석 방법을 설명하기 위한 도면이다.8 is a diagram for explaining a method for analyzing biometric information including generating and transmitting output information according to an embodiment of the present invention.
도 9는 본 발명의 일 실시예에 따른 출력정보를 출력하기 위한 생체정보 센싱 장치의 출력부의 구성을 도시한 도면이다.9 is a diagram showing a configuration of an output unit of a biometric information sensing device for outputting output information according to an embodiment of the present invention.
도 10은 본 발명의 일 실시예에 따른 출력조명색을 변경하여 출력하기 위한 생체정보 센싱 장치 및 착용 모습을 설명하기 위한 도면이다.FIG. 10 is a diagram illustrating a biometric information sensing device for changing and outputting an output lighting color according to an embodiment of the present invention and a wearing state.
도 11 및 12는 본 발명의 일 실시예에 따른 생체정보, 운동정보 또는 성능지표 제공 화면을 설명하기 위한 도면이다.11 and 12 are diagrams for explaining a screen for providing biometric information, exercise information, or performance index according to an embodiment of the present invention.
도 13은 본 발명의 일 실시예에 따른 생체정보 센싱 장치와 클라이언트 장치의 통신 관계를 도시한 도면이다.13 is a diagram illustrating a communication relationship between a biometric information sensing device and a client device according to an embodiment of the present invention.
본 발명의 이점 및 특징, 그리고 그것들을 달성하는 방법은 첨부되는 도면과 함께 상세하게 후술되어 있는 실시예들을 참조하면 명확해질 것이다. 그러나, 본 발명은 이하에서 개시되는 실시예들에 제한되는 것이 아니라 서로 다른 다양한 형태로 구현될 수 있으며, 단지 본 실시예들은 본 발명의 개시가 완전하도록 하고, 본 발명이 속하는 기술 분야의 통상의 기술자에게 본 발명의 범주를 완전하게 알려주기 위해 제공되는 것이며, 본 발명은 청구항의 범주에 의해 정의될 뿐이다.Advantages and features of the present invention, and a method of achieving them will become apparent with reference to the embodiments described below in detail together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in a variety of different forms, only the present embodiments are intended to complete the disclosure of the present invention, It is provided to fully inform the technician of the scope of the present invention, and the present invention is only defined by the scope of the claims.
본 명세서에서 사용된 용어는 실시예들을 설명하기 위한 것이며 본 발명을 제한하고자 하는 것은 아니다. 본 명세서에서, 단수형은 문구에서 특별히 언급하지 않는 한 복수형도 포함한다. 명세서에서 사용되는 "포함한다(comprises)" 및/또는 "포함하는(comprising)"은 언급된 구성요소 외에 하나 이상의 다른 구성요소의 존재 또는 추가를 배제하지 않는다. 명세서 전체에 걸쳐 동일한 도면 부호는 동일한 구성 요소를 지칭하며, "및/또는"은 언급된 구성요소들의 각각 및 하나 이상의 모든 조합을 포함한다. 비록 "제1", "제2" 등이 다양한 구성요소들을 서술하기 위해서 사용되나, 이들 구성요소들은 이들 용어에 의해 제한되지 않음은 물론이다. 이들 용어들은 단지 하나의 구성요소를 다른 구성요소와 구별하기 위하여 사용하는 것이다. 따라서, 이하에서 언급되는 제1 구성요소는 본 발명의 기술적 사상 내에서 제2 구성요소일 수도 있음은 물론이다.The terms used in the present specification are for describing exemplary embodiments and are not intended to limit the present invention. In this specification, the singular form also includes the plural form unless specifically stated in the phrase. As used in the specification, “comprises” and/or “comprising” do not exclude the presence or addition of one or more other elements other than the mentioned elements. Throughout the specification, the same reference numerals refer to the same elements, and “and/or” includes each and all combinations of one or more of the mentioned elements. Although "first", "second", and the like are used to describe various elements, it goes without saying that these elements are not limited by these terms. These terms are only used to distinguish one component from another component. Therefore, it goes without saying that the first component mentioned below may be the second component within the technical idea of the present invention.
본 명세서에서 '생체정보'는 생체정보 센싱 장치로부터 수신한 정보로써, 호흡, 심전도, 산소포화도, 체온, 위치 또는 동작정보를 포함할 수 있으나, 이에 제한되지 않고 사용자의 신체에 관한 모든 정보를 포함할 수 있다.In the present specification,'biometric information' is information received from a biometric information sensing device, and may include respiration, electrocardiogram, oxygen saturation, body temperature, location, or motion information, but is not limited thereto and includes all information on the user's body. can do.
본 명세서에서 '운동정보'는 수신한 생체정보를 분석하여 생성한 사용자가 수행한 운동에 관한 정보로써, 운동종류, 운동횟수, 운동강도 또는 운동시간 정보를 포함할 수 있으나, 이에 제한되지 않고 사용자가 수행중인 운동에 관한 모든 정보를 포함할 수 있다.In the present specification,'exercise information' is information on an exercise performed by a user, which is generated by analyzing received biometric information, and may include exercise type, exercise frequency, exercise intensity, or exercise time information, but is not limited thereto. Can contain all information about the exercise being performed.
본 명세서에서 '성능지표'는 사용자가 수행한 운동 결과에 관한 정보를 제공하기 위하여 생체정보 또는 운동정보를 기초로 산출한 지표를 의미한다.In the present specification, the'performance indicator' means an index calculated based on biometric information or exercise information in order to provide information on the result of an exercise performed by a user.
본 명세서에서 '컴퓨터'는 연산처리를 수행할 수 있는 다양한 장치들이 모두 포함된다. 예를 들어, 컴퓨터는 데스크 탑 PC, 노트북(Note Book) 뿐만 아니라 스마트폰(Smart phone), 태블릿 PC, 셀룰러폰(Cellular phone), 피씨에스폰(PCS phone; Personal Communication Service phone), 동기식/비동기식 IMT-2000(International Mobile Telecommunication-2000)의 이동 단말기, 팜 PC(Palm Personal Computer), 개인용 디지털 보조기(PDA; Personal Digital Assistant) 등도 해당될 수 있다.In the present specification, the term "computer" includes all various devices capable of performing arithmetic processing. For example, computers are not only desktop PCs and notebooks, but also smart phones, tablet PCs, cellular phones, PCS phones, and synchronous/asynchronous systems. A mobile terminal of the International Mobile Telecommunication-2000 (IMT-2000), a Palm Personal Computer (PC), a personal digital assistant (PDA), and the like may also be applicable.
본 명세서에서 '클라이언트 장치'는 사용자들이 프로그램(또는 어플리케이션)을 설치하여 사용할 수 있는 통신 기능을 포함한 모든 장치를 말한다. 즉, 클라이언트 장치는 셀룰러폰(Cellular phone), 피씨에스폰(PCS phone; Personal Communication Service phone), 동기식/비동기식 IMT-2000(International Mobile Telecommunication-2000)의 이동 단말기, 팜 PC(Palm Personal Computer), 개인용 디지털 보조기(PDA; Personal Digital Assistant), 스마트폰(Smart phone), 왑폰(WAP phone; Wireless Application Protocao phone), 모바일 게임기, 테블릿 PC, 스마트워치, 노트북 PC, 데스크탑 PC, 스마트카메라, 스마트TV 등의 다양한 통신기기들을 포함할 수 있다. 또한, 클라이언트 장치는 기본적으로 통신 기능을 포함하고 있지 않으나 통신기능을 보유한 메모리 칩을 결합하여 통신을 수행할 수 있는 장치를 포함할 수 있다.In this specification, a'client device' refers to all devices including a communication function that users can use by installing a program (or application). That is, the client device is a cellular phone, a PCS phone (Personal Communication Service phone), a synchronous/asynchronous mobile terminal of the International Mobile Telecommunication-2000 (IMT-2000), a Palm Personal Computer (Palm Personal Computer), Personal Digital Assistant (PDA), Smart phone, WAP phone (Wireless Application Protocao phone), mobile game console, tablet PC, smart watch, notebook PC, desktop PC, smart camera, smart TV It may include various communication devices such as. Further, the client device does not basically include a communication function, but may include a device capable of performing communication by combining a memory chip having a communication function.
다른 정의가 없다면, 본 명세서에서 사용되는 모든 용어(기술 및 과학적 용어를 포함)는 본 발명이 속하는 기술분야의 통상의 기술자에게 공통적으로 이해될 수 있는 의미로 사용될 수 있을 것이다. 또한, 일반적으로 사용되는 사전에 정의되어 있는 용어들은 명백하게 특별히 정의되어 있지 않는 한 이상적으로 또는 과도하게 해석되지 않는다.Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used as meanings that can be commonly understood by those of ordinary skill in the art to which the present invention belongs. In addition, terms defined in a commonly used dictionary are not interpreted ideally or excessively unless explicitly defined specifically.
이하, 첨부된 도면을 참조하여 본 발명의 실시예를 상세하게 설명한다.Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings.
도 1은 본 발명의 일 실시예에 따른 센싱정보를 수신하기 위한 생체 정보 센싱 장치의 구성을 도시한 도면이고, 도 2는 본 발명의 일 실시예에 따른 센싱정보를 수신하기 위한 생체정보 센싱 장치의 하우징의 구성을 도시한 도면이다.FIG. 1 is a diagram showing a configuration of a biometric information sensing device for receiving sensing information according to an embodiment of the present invention, and FIG. 2 is a biometric information sensing device for receiving sensing information according to an embodiment of the present invention. It is a diagram showing the configuration of the housing.
도 1을 참조하면, 본 발명의 일 실시예에 따른 센싱정보를 수신하기 위한 생체정보 센싱 장치(10)는, 하우징(100), 상기 하우징의 일측 또는 양측에 결합되는 착용부(200), 상기 착용부에 위치하는 섬유형 호흡센서(300)를 포함할 수 있다.Referring to FIG. 1, a biometric information sensing device 10 for receiving sensing information according to an embodiment of the present invention includes a housing 100, a wearing part 200 coupled to one or both sides of the housing, and the It may include a fiber-type breathing sensor 300 located on the wearing part.
도 2를 참조하면, 본 발명의 일 실시예에 따른 센싱정보를 수신하기 위한 생체정보 센싱 장치의 하우징(100)은 센서부(110), 전원부(120), 또는 제어부(130)를 포함할 수 있고, 센서부는 ECG센서(112), 산소포화도센서(114), 온도센서(116) 또는 동작센서(118)를 포함할 수 있으며, 이로써 한정하지는 않고 생체정보를 센싱할 수 있는 다양한 센서들이 포함될 수 있다.2, the housing 100 of the biometric information sensing device for receiving sensing information according to an embodiment of the present invention may include a sensor unit 110, a power supply unit 120, or a control unit 130. In addition, the sensor unit may include an ECG sensor 112, an oxygen saturation sensor 114, a temperature sensor 116, or a motion sensor 118, and various sensors capable of sensing biometric information may be included without being limited thereto. have.
일 실시예에서, 전원부(120)는 생체정보 센싱 장치의 구동에 필요한 전력을 공급해 주는 것으로 배터리를 포함할 수 있고, 제어부(130)는 생체정보 센싱 장치와 관련된 전반적인 동작들을 제어할 수 있고 각 구성들과 연결되어 각 구성들의 동작을 제어할 수 있다.In one embodiment, the power supply unit 120 supplies power necessary for driving the biometric information sensing device, and may include a battery, and the controller 130 can control overall operations related to the biometric information sensing device, and each component It is connected to the field and can control the operation of each component.
일 실시예에서, 착용부(200)는 하우징의 일측 또는 양측에 결합되는 것으로, 사용자의 가슴에 둘러서 착용할 수 있는 밴드일 수 있으며, 보다 바람직하게는, 사용자의 호흡에 의한 흉부의 부피변화에 따라 늘어나거나 줄어들 수 있도록 신축성을 가지는 밴드일 수 있다. 또한, 착용부는 사용자의 가슴둘레에 따라 길이의 조절이 가능한 조절부를 더 포함할 수 있다.In one embodiment, the wearing part 200 is coupled to one side or both sides of the housing, and may be a band that can be worn around the user's chest, more preferably, due to the change in the volume of the chest due to the user's breathing. It may be a band having elasticity so that it can be stretched or contracted accordingly. In addition, the wearing unit may further include an adjustment unit capable of adjusting a length according to the user's chest circumference.
일 실시예에서, 섬유형 호흡센서(300)는 상기 착용부(200)의 일부에 위치하여 사용자의 호흡에 의한 흉부의 부피변화를 감지하여 사용자의 호흡정보를 센싱하는 호흡센서를 의미할 수 있다. 상기 섬유형 호흡센서는 사용자의 흉부를 둘러싸는 밴드 형태의 착용부의 일면에 탄소나노튜브(Carbon NanoTube, CNT)가 도포되고, 사용자의 호흡시 흉부의 수축, 팽창으로 인한 부피변화에 의해 발생하는 탄소나노튜브의 저항값 변화를 검출하여, 탄소나노튜브를 도포한 착용부의 변형률, 즉 흉부의 부피변화를 감지하고 이를 통해 호흡패턴, 호흡횟수 또는 호흡량을 포함하는 호흡정보를 센싱할 수 있다. 일 실시예에서, 상기 흉부의 부피변화는 나노밀리미터 단위로 감지할 수 있으며, 이에 제한되는 것은 아니다. 또한, 실시예에서, 상기 호흡정보의 노이즈 제거를 위한 단계가 포함될 수 있다.In one embodiment, the fibrous breathing sensor 300 may refer to a breathing sensor that is located on a part of the wearing part 200 and senses the user's breathing information by detecting a change in the volume of the chest caused by the user's breathing. . The fiber-type breathing sensor is coated with a carbon nanotube (CNT) on one side of the wearing part in the form of a band surrounding the user's chest, and carbon generated by volume change due to contraction and expansion of the chest during the user's breathing. By detecting the change in the resistance value of the nanotube, the deformation rate of the wearing part to which the carbon nanotube is applied, that is, the change in the volume of the chest, can be sensed, and through this, the respiratory information including the breathing pattern, the number of breaths, or the amount of breathing can be sensed. In one embodiment, the change in the volume of the chest may be detected in nano-millimeter units, but is not limited thereto. In addition, in an embodiment, a step for removing noise from the breathing information may be included.
일 실시예에서, 상기 섬유형 호흡센서는 신축성을 가지는 밴드로 이루어진 착용부의 일면에 폴리우레탄 필름(Polyurethane adhesive film)을 버퍼층(buffer layer)으로 부착하는 단계, 상기 폴리우레탄 필름 위에 탄소나노튜브를 도포하는 단계, 탄소나노튜브층을 열경화(Thermal curing)하는 단계, 상기 탄소나노튜브층 위에 폴리우레탄 필름을 보호층으로 부착하는 단계 또는 전극 단자를 형성하는 단계를 포함하는 방법에 의해 제조될 수 있다. 실시예에서, 상기 폴리우레탄필름은 열전사(thermal transfer printing) 기법에 의해 부착될 수 있고, 상기 탄소나노튜브는 스크린 인쇄(screen printing) 기법에 의해 도포될 수 있다. 또한, 도포된 상기 탄소나노튜브층의 두께는 50~300μm 일 수 있으며, 도포 모양, 길이 또는 면적은 생체정보 센싱 장치의 디자인에 따라 다양하게 변경될 수 있다. 또한, 상기 열경화 단계는 적외선 컨베이어 건조기(Infrared conveyor dryer)를 사용하여 열경화하는 단계를 포함할 수 있고, 상기 보호층으로 부착하는 폴리우레탄 필름은, 투명 필름 또는 불투명 필름을 포함할 수 있고, 상기 전극 단자는 아일렛(eyelet), 금속전선 또는 구리 박막을 포함할 수 있다.In one embodiment, the fiber-type breathing sensor is a step of attaching a polyurethane film as a buffer layer to one side of the wearing part made of an elastic band, applying a carbon nanotube on the polyurethane film It may be manufactured by a method including the step of, thermal curing the carbon nanotube layer, attaching a polyurethane film on the carbon nanotube layer as a protective layer, or forming an electrode terminal. . In an embodiment, the polyurethane film may be attached by a thermal transfer printing technique, and the carbon nanotubes may be applied by a screen printing technique. In addition, the thickness of the applied carbon nanotube layer may be 50 to 300 μm, and the application shape, length, or area may be variously changed according to the design of the biometric information sensing device. In addition, the thermal curing step may include thermal curing using an infrared conveyor dryer, and the polyurethane film attached as the protective layer may include a transparent film or an opaque film, The electrode terminal may include an eyelet, a metal wire, or a copper thin film.
일 실시예에서, ECG센서(112)는 사용자의 ECG 신호를 검출하는 하나 이상의 ECG전극(113)을 포함할 수 있고, 상기 복수의 ECG전극은 이격된 위치에서 신체에 접촉할 수 있도록 하우징의 양측 착용부에 위치할 수 있다. 또한, 상기 ECG 전극은 전도성 실리콘 ECG 전극을 포함할 수 있다. 또한, 검출된 ECG신호를 심박수 정보로 변환하는 변환부를 상기 하우징(100) 내부에 더 포함할 수 있으며, 상기 ECG 신호는 심전도 QRS 파형의 진폭 데이터를 포함할 수 있다.In one embodiment, the ECG sensor 112 may include one or more ECG electrodes 113 for detecting a user's ECG signal, and the plurality of ECG electrodes are located on both sides of the housing so that they can contact the body at a spaced apart position. It can be located on the wearing part. In addition, the ECG electrode may include a conductive silicon ECG electrode. In addition, a conversion unit for converting the detected ECG signal into heart rate information may be further included in the housing 100, and the ECG signal may include amplitude data of an ECG QRS waveform.
일 실시예에서, 산소포화도센서(114)는 빛의 특정 파장이 흡수되는 정도를 이용해 혈중 산소포화도(SpO2) 데이터를 센싱하는 광학센서를 포함할 수 있고, 체온센서(116)는 사용자의 신체와 접촉하여 사용자의 체온을 측정하는 온도센서를 포함할 수 있다. 또한, 상기 산소포화도센서 또는 체온센서는 인식율을 높이기 위하여 모듈화가 진행될 수 있다.In one embodiment, the oxygen saturation sensor 114 may include an optical sensor that senses blood oxygen saturation (SpO2) data using a degree of absorption of a specific wavelength of light, and the body temperature sensor 116 It may include a temperature sensor that measures the body temperature of the user by contacting. In addition, the oxygen saturation sensor or the body temperature sensor may be modularized to increase the recognition rate.
일 실시예에서, 동작센서(116)는 가속도센서, 각속도센서 또는 지자기센서를 포함할 수 있다. 상기 각속도센서는 자이로센서를 의미할 수 있으나 이에 제한되는 것은 아니며, 상기 동작센서는 가속도정보, 각속도정보 또는 지자기정보를 센싱하여 상기 센싱 장치를 착용한 사용자의 움직임에 대한 동작정보를 생성할 수 있다.In one embodiment, the motion sensor 116 may include an acceleration sensor, an angular velocity sensor, or a geomagnetic sensor. The angular velocity sensor may mean a gyro sensor, but is not limited thereto, and the motion sensor may sense acceleration information, angular velocity information, or geomagnetic information to generate motion information about the movement of a user wearing the sensing device. .
또한, 일 실시예에서, 상기 생체정보를 센싱하는 복수의 센서는, 생체신호를 검출하는 검출부 및 검출된 생체신호를 생체정보로 변환하는 변환부를 포함하는 모듈로 구성될 수 있다.In addition, in an embodiment, the plurality of sensors for sensing the biometric information may be configured as a module including a detection unit for detecting a biosignal and a conversion unit for converting the detected biosignal into biometric information.
이상으로, 본 발명의 일 실시예에 따른 센싱정보를 수신하기 위한 생체정보 센싱 장치의 일 실시예를 설명하였으나, 본 발명의 센싱정보를 수신하기 위한 장치는 이에 제한되는 것은 아니고, 사용자의 생체정보를 센싱할 수 있는 모든 센싱 장치를 포함한다.As described above, an embodiment of a biometric information sensing device for receiving sensing information according to an embodiment of the present invention has been described, but the device for receiving sensing information of the present invention is not limited thereto, and the user's biometric information Includes all sensing devices capable of sensing.
도 3은 본 발명의 일 실시예에 따른 생체정보 분석 방법을 설명하기 위한 도면이다. 도 3을 참조하면, 본 발명의 일 실시예에 따른 생체정보 분석 방법은, 서버가 센싱정보를 수신하는 정보수신단계(S100), 서버가 상기 센싱정보를 기초로 운동정보를 생성하는 운동정보생성단계(S110), 서버가 상기 센싱정보 또는 운동정보를 기초로 성능지표를 산출하는 지표산출단계(S120) 및 서버가 상기 센싱정보, 상기 운동정보 또는 상기 성능지표를 저장하는 저장단계(S130)를 포함할 수 있다.3 is a diagram illustrating a method of analyzing biometric information according to an embodiment of the present invention. Referring to FIG. 3, the method for analyzing biometric information according to an embodiment of the present invention includes an information receiving step (S100) in which a server receives sensing information, and an exercise information generation in which the server generates exercise information based on the sensing information. Step (S110), an index calculation step (S120) in which a server calculates a performance index based on the sensing information or exercise information, and a storage step (S130) in which the server stores the sensing information, the exercise information or the performance index. Can include.
상기 센싱정보는 센싱 장치에 의해 센싱된 호흡, 심전도, 산소포화도, 체온, 위치 또는 동작정보를 포함하는 생체정보를 의미하고, 상기 운동정보는 상기 센싱정보를 기초로 서버에서 분석한 결과로서 운동종류, 운동횟수 또는 운동 강도 정보를 포함할 수 있다.The sensing information means biometric information including respiration, electrocardiogram, oxygen saturation, body temperature, location, or motion information sensed by the sensing device, and the exercise information is an exercise type as a result of analysis by the server based on the sensing information. , Exercise frequency or exercise intensity information may be included.
일 실시예에서, 상기 호흡정보는 전술한 탄소나노튜브가 도포된 섬유형 호흡센서가 사용자의 호흡에 따른 흉부 부피변화를 감지하는 방식에 의해 센싱된 것을 포함할 수 있고, 상기 호흡정보는 호흡패턴, 호흡횟수 또는 호흡량을 포함할 수 있다.In one embodiment, the respiration information may include that the above-described carbon nanotube-coated fibrous respiration sensor is sensed by a method of detecting the chest volume change according to the user's breathing, the respiration information , Breathing frequency or volume.
도 4는 본 발명의 일 실시예에 따른 서버를 설명하기 위한 도면이고, 도 5는 본 발명의 일 실시예에 따른 분석부를 설명하기 위한 도면이다. 도 4 및 도 5를 참조하면, 본 발명의 일 실시예에서, 서버(20)는 분석부(700)를 포함할 수 있고, 상기 분석부는 생체정보를 기초로 운동정보를 생성하는 운동정보 인식모델(720)을 포함할 수 있다. 서버는 생체정보 센싱 장치로부터 센싱정보를 수신하고, 수신한 센싱정보를 운동정보 인식모델에 입력하여 운동정보를 생성할 수 있다. 또한, 상기 운동정보 인식모델(720)은 단일 인식모듈로 구성될 수 있을뿐만 아니라, 복수의 인식모듈을 포함하도록 구성될 수 있으며, 예를 들어, 운동종류정보인식모듈(722), 운동횟수정보 인식모듈(724) 또는 운동강도정보 인식모듈(726)을 포함할 수 있다.4 is a diagram for explaining a server according to an embodiment of the present invention, and FIG. 5 is a view for explaining an analysis unit according to an embodiment of the present invention. 4 and 5, in one embodiment of the present invention, the server 20 may include an analysis unit 700, and the analysis unit is an exercise information recognition model that generates exercise information based on biometric information. It may include 720. The server may receive sensing information from the biometric information sensing device and input the received sensing information into an exercise information recognition model to generate exercise information. In addition, the exercise information recognition model 720 may be configured not only as a single recognition module, but also may be configured to include a plurality of recognition modules, for example, exercise type information recognition module 722, exercise frequency information A recognition module 724 or an exercise intensity information recognition module 726 may be included.
일 실시예에서, 서버가 센싱정보를 기초로 운동정보를 생성하는 운동정보생성단계(S110)는 서버가 운동별특징값을 적용하여 운동종류정보를 생성하는 단계를 포함할 수 있고, 상기 운동별특징값은 운동종류별 수평, 수직 또는 회전동작의 특징값을 포함할 수 있다. 예를 들어, 동작센서에 의해 센싱된 사용자의 가속도, 각속도 또는 기울기를 포함하는 동작정보를 수신하여 운동종류정보 인식모듈(722)에 입력하고, 상기 운동종류정보 인식모듈은 입력된 상기 동작정보와 일치하는 동작의 특징값을 포함하는 운동종류정보를 매칭하여 특정 운동종류정보를 생성할 수 있다.In one embodiment, the exercise information generation step (S110) in which the server generates exercise information based on the sensing information may include the step of generating exercise type information by applying the exercise-specific feature value by the server, and The feature value may include a feature value of horizontal, vertical, or rotational motion for each type of motion. For example, motion information including acceleration, angular velocity, or slope of the user sensed by the motion sensor is received and input to the exercise type information recognition module 722, and the exercise type information recognition module includes the input motion information and Specific exercise type information may be generated by matching exercise type information including characteristic values of the matched motion.
또한, 일 실시예에서, 상기 운동정보생성단계는 데이터선택단계, 전처리단계, 오토카운팅단계, 데이터특징추출단계 또는 동작인식단계를 포함할 수 있다. 상기 데이터선택단계는 다양한 센싱정보 중에서 분석에 활용할 데이터를 선택하는 것을 의미할 수 있다. 또한, 상기 전처리단계는, 분석에 활용할 데이터(예를 들어, 3축가속도 데이터)에서 분석에 사용하지 않을 구간(예를 들어, 운동 전후 준비구간 또는 마무리 구간 10초)을 제거하는 단계, 분석에 사용할 구간에 대해 특정 길이 구간(예를 들어, 2초)을 분석단위로 설정하는 단계, 데이터를 가공(예를 들어, magnitude)하는 단계, 노이즈를 제거(예를 들어, butterworth bandpass filtering)하는 단계 또는 전환구간 처리단계를 포함할 수 있다. 상기 분석단위는 고정된 크기뿐만 아니라, 다양한 크기(예를 들어, 오토카운팅으로 산출된 운동동작(1rep) 길이)를 포함할 수 있다. 또한, 상기 오토카운팅단계는 전처리 가공 후 노이즈가 제거된 데이터에 대해 일정 구간에 대한 피크(peak)의 수를 세는 것을 의미할 수 있으며, 상기 오토카운팅단계는 동시간에 대한 복수의 서로 다른 종류의 센싱정보에 대하여 적용함으로써 정확도를 향상시킬 수 있다. 또한, 상기 데이터특징추출단계는 가공 후 노이즈가 제거된 데이터(예를 들어, 3축가속도 데이터 또는 각속도데이터)에 대해 동작인식에 활용할 특징을 추출하는 단계를 의미하며, 상기 데이터특징은 평균(Mean), 표준편차(Standard Deviation), 상관관계(Correlation), 피크 간격 또는 피크 진폭을 포함할 수 있다. 또한, 상기 동작인식단계는 상기 데이터특징을 활용하여 분류 알고리즘에 의해 수행동작을 인식하는 단계로, 상기 분류 알고리즘은 계층 적 클러스터링(Hierarchical Method), 로지스틱 회귀(logistic regression), K-NN(K-nearest neighbors), 결정 트리(Decision Tree), 랜덤 포레스트(Random Forest), 서포트 벡터 머신(support vector machine, SVM), 나이브 베이즈(Na ve Bayes), 은닉마코프모델(Hidden Markov Models, HMMs), 또는 RNN 또는 CNN을 포함하는 인공신경망(Neural Networks)을 포함할 수 있으나, 이에 제한되지 않는다.In addition, in an embodiment, the exercise information generation step may include a data selection step, a preprocessing step, an auto counting step, a data feature extraction step, or a motion recognition step. The data selection step may mean selecting data to be used for analysis from among various sensing information. In addition, the pre-processing step is a step of removing a section that will not be used for analysis (e.g., a preparation section before and after exercise or a finishing section of 10 seconds) from the data to be used for analysis (for example, 3-axis acceleration data). For the section to be used, setting a specific length section (eg, 2 seconds) as an analysis unit, processing data (eg, magnitude), removing noise (eg, butterworth bandpass filtering) Or it may include a conversion section processing step. The analysis unit may include not only a fixed size, but also various sizes (eg, the length of an exercise motion (1rep) calculated by auto counting). In addition, the auto-counting step may mean counting the number of peaks in a certain section for data from which noise has been removed after pre-processing, and the auto-counting step includes a plurality of different types for the same time. Accuracy can be improved by applying to sensing information. In addition, the data feature extraction step refers to a step of extracting a feature to be used for motion recognition for data from which noise has been removed after processing (for example, 3-axis acceleration data or angular velocity data), and the data feature is averaged ), Standard Deviation, Correlation, Peak Interval or Peak Amplitude. In addition, the motion recognition step is a step of recognizing an action performed by a classification algorithm using the data feature, and the classification algorithm includes hierarchical clustering (Hierarchical Method), logistic regression, and K-NN (K- nearest neighbors), Decision Tree, Random Forest, support vector machine (SVM), Na ve Bayes, Hidden Markov Models (HMMs), or May include, but is not limited to, artificial neural networks including RNN or CNN.
일 실시예에서, 서버가 센싱정보를 기초로 운동정보를 생성하는 운동정보생성단계(S110)는 수신한 센싱정보를 기초로 데이터셋을 구축하는 단계 및 센싱정보 및 데이터셋을 기초로 딥러닝에 의하여 운동종류정보를 생성하는 단계를 포함할 수 있다.In one embodiment, the exercise information generation step (S110) in which the server generates exercise information based on the sensing information is a step of constructing a data set based on the received sensing information, and deep learning based on the sensing information and the data set. Thus, it may include the step of generating exercise type information.
일 실시예에서, 상기 데이터셋은 하나 이상의 센싱정보와 운동종류 정보를 매칭하여 구축되는 것을 포함할 수 있다. 구체적으로, 서버는 생체정보 센싱 장치를 착용하여 운동하는 사용자가 단일 종류의 운동을 수행할 때 센싱된 센싱 정보를 획득하여, 상기 센싱정보와 상기 운동종류정보를 매칭하여 데이터셋을 구축할 수 있으며, 상기 데이터셋은 아래와 같이 표현할 수 있다.In one embodiment, the data set may include one that is constructed by matching one or more sensing information and exercise type information. Specifically, the server acquires sensing information sensed when a user exercising by wearing a biometric information sensing device performs a single type of exercise, and may construct a dataset by matching the sensing information with the exercise type information. , The dataset can be expressed as follows.
Dataset={(x, y)│x=센싱 장치가 센싱한 복수의 생체정보, y=운동종류정보}Dataset={(x, y)│x=multiple biometric information sensed by the sensing device, y=exercise type information}
예를 들어, 센싱 장치를 착용한 사용자가 "push up" 운동을 수행하는 중 센싱된 생체정보가 체온(36.5℃), 심박(180bpm), 호흡(25bpm), 산소포화도(99.5%), 3축가속도(0.010, 0.001, 0.20), 3축오일러앵글(0.1, 0, 0)인 경우, 데이터셋은 아래와 같이 구축될 수 있다.For example, when a user wearing a sensing device performs a “push up” exercise, the sensed biometric information is body temperature (36.5℃), heart rate (180bpm), respiration (25bpm), oxygen saturation (99.5%), and triaxial values. In the case of speed (0.010, 0.001, 0.20) and 3-axis Euler angle (0.1, 0, 0), the dataset can be constructed as follows.
Dataset={36.5℃, 180bpm, 25bpm, 99.5%, (0.010, 0.001, 0.20), (0.1, 0, 0), "push up"}Dataset={36.5℃, 180bpm, 25bpm, 99.5%, (0.010, 0.001, 0.20), (0.1, 0, 0), "push up"}
또한, 일 실시예에서, 상기 운동정보 인식모델 또는 상기 운동종류정보 인식모듈은, 상기 데이터셋을 기초로 딥러닝 학습모델을 통해 트레이닝이 되는 것일 수 있다. 즉, 상기 운동정보 인식모델 또는 운동종류정보 인식모듈은 특정한 딥러닝 알고리즘으로 구축되는 것으로, 상기 데이터셋을 기초로 특정한 운동종류정보와 해당 운동정보 수행 중 센싱된 생체정보를 매칭하여 학습을 수행한 것일 수 있다.In addition, in an embodiment, the exercise information recognition model or the exercise type information recognition module may be trained through a deep learning learning model based on the data set. That is, the exercise information recognition model or exercise type information recognition module is built with a specific deep learning algorithm, and learning is performed by matching specific exercise type information and biometric information sensed during execution of the corresponding exercise information based on the dataset. Can be.
일 실시예에서, 상기 운동정보 인식모델, 성능지표 산출모델 또는 개별 운동정보 인식모듈, 성능지표 인식모듈은 멀티레이어(multi-layer)로 구성된 인공신경망(artificial neural network)으로 형성될 수 있다. 또한, 상기 딥러닝 알고리즘은 CNN, RNN, LSTM 또는 GRU 방식을 포함할 수 있으며, 이에 제한되는 것은 아니다.In an embodiment, the exercise information recognition model, the performance index calculation model or the individual exercise information recognition module, and the performance index recognition module may be formed as an artificial neural network composed of multi-layers. In addition, the deep learning algorithm may include a CNN, RNN, LSTM, or GRU scheme, but is not limited thereto.
도 6은 본 발명의 일 실시예에 따른 동시간에 센싱된 생체정보의 시간에 대한 그래프를 도시한 도면이다. 도 6을 참조하면, 본 발명의 일 실시예에 따른 생체정보 분석 방법에 있어서 운동정보생성단계는, 서버가 동시간에 대한 복수의 센싱정보를 시간정보에 대응하도록 비교하여 운동정보를 생성하는 단계를 포함할 수 있다. 즉, 센싱정보는 시간에 대한 정보를 더 포함할 수 있고, 상기 시간정보는 생체정보가 센싱된 시간을 의미할 수 있다. 서버는 시간에 맞춰 복수의 센싱 정보를 매칭하여 비교함으로써, 정밀한 운동정보 또는 성능지표의 산출이 가능하다.6 is a diagram illustrating a graph of time of biometric information sensed at the same time according to an embodiment of the present invention. Referring to FIG. 6, the exercise information generation step in the biometric information analysis method according to an embodiment of the present invention is a step of generating exercise information by comparing a plurality of sensing information for the same time to correspond to time information. It may include. That is, the sensing information may further include information about time, and the time information may mean a time at which the biometric information was sensed. By matching and comparing a plurality of sensing information according to time, the server can calculate precise exercise information or performance index.
일 실시예에서, 서버는 수신한 센싱정보를 도 6과 같이 시간에 대한 그래프로 매칭하여 간편하고 정확하게 운동정보를 분석할 수 있다. 예를 들어, 센싱 장치 사용자가 '스쿼트' 운동을 하는 경우, 동시간에 센싱되는 사용자의 호흡정보(50)와 동작정보(60)를 시간에 맞춰 비교함으로써, 운동횟수정보, 운동량정보를 포함하는 운동정보를 보다 정밀하게 분석할 수 있다. 구체적인 예로, 스쿼트 운동에 있어서, 동작의 반복수행에 따른 규칙적인 호흡패턴(예를 들어, down 동작시 들숨 및 up 동작시 날숨)이 동반되는 것 또는 근육에 강한 부하가 걸리는 경우(예를 들어, 스쿼트에서 최저점에 도달한 경우) 호흡을 멈추는 지점(bracing point)을 측정하여 동작정보(예를 들어, 상하 움직임)와 비교함으로써 정밀한 운동횟수의 분석이 가능하고, 근육에 강한 부하가 걸릴수록 호흡을 멈추는 시간이 길어지는 점을 이용하여 운동강도 또는 운동량의 분석이 가능하다. 또한, 동시간에 대한 복수의 센싱정보를 획득하여 비교함으로써, 사용자의 운동동작(1rep) 수행을 구분할 수 있고, 운동동작을 수행하는데 소요한 시간과 휴식을 취한 시간을 산출 가능하며, 특정 루틴에 있어서 사용자의 운동동작 수행 횟수를 정밀하게 산출할 수 있다.In an embodiment, the server may conveniently and accurately analyze the exercise information by matching the received sensing information with a graph against time as shown in FIG. 6. For example, when a sensing device user performs a'squat' exercise, by comparing the user's breathing information 50 and motion information 60 sensed at the same time in time, including exercise frequency information and exercise amount information Exercise information can be analyzed more precisely. As a specific example, in the squat exercise, a regular breathing pattern (e.g., inhalation during a down motion and exhalation during an up motion) according to the repeated performance of the motion is accompanied, or when a strong load is applied to the muscles (e.g., When it reaches the lowest point in the squat), the bracing point is measured and compared with motion information (e.g., up and down movements) to allow precise analysis of the number of exercise. It is possible to analyze exercise intensity or amount of exercise by using the point that the stopping time is longer. In addition, by acquiring and comparing a plurality of sensing information for the same time, it is possible to distinguish the user's exercise action (1 rep) performance, and to calculate the time spent performing the exercise action and the time taken to take a break. Therefore, it is possible to accurately calculate the number of times the user performs an exercise operation.
일 실시예에서, 성능지표 산출단계는 딥러닝 알고리즘을 통해 이루어질 수 있다. 도 5를 참조하면, 서버(20)는 분석부(700)를 포함할 수 있고, 상기 분석부는 성능지표 산출모델(740)을 포함할 수 있다. 서버는 생체정보 센싱 장치로부터 수신한 생체정보 또는 운동정보 인식모델(720)에 의해 생성된 운동정보를 성능지표 산출모델(740)에 입력하여 성능지표를 산출할 수 있다. 또한, 상기 성능지표 산출모델(740)은 단일 인식모듈로 구성될 수 있을뿐만 아니라, 복수의 인식모듈을 포함하도록 구성될 수 있으며, 이 경우 일관성 산출모듈(742), 정확성 산출모듈(744), 소요시간 산출모듈(746) 등 복수의 성능지표에 대한 개별 산출모듈을 포함할 수 있다.In an embodiment, the step of calculating the performance indicator may be performed through a deep learning algorithm. Referring to FIG. 5, the server 20 may include an analysis unit 700, and the analysis unit may include a performance index calculation model 740. The server may calculate the performance index by inputting the biometric information received from the biometric information sensing device or the exercise information generated by the exercise information recognition model 720 into the performance index calculation model 740. In addition, the performance indicator calculation model 740 may be configured not only as a single recognition module, but also may be configured to include a plurality of recognition modules. In this case, the consistency calculation module 742, the accuracy calculation module 744, It may include individual calculation modules for a plurality of performance indicators, such as the required time calculation module 746.
도 7은 본 발명의 일 실시예에 따른 성능지표를 설명하기 위한 도면이다. 도 7을 참조하면, 일 실시예에서, 상기 성능지표는 일관성, 정확성, 소요시간, 카운트 또는 예상기록을 포함할 수 있으나 이에 제한되지 않는다.7 is a diagram illustrating a performance index according to an embodiment of the present invention. Referring to FIG. 7, in an embodiment, the performance indicator may include consistency, accuracy, time required, count, or predicted recording, but is not limited thereto.
일관성(Consistency In Action, CIA)은 동일한 운동동작의 반복적 수행에 있어서, 반복수행 동작의 서로에 대한 일치정도를 의미하고, 정확성은 각반복수행 동작의 기준동작에 대한 일치정도를 의미할 수 있다. 예를 들어, 사용자가 스쿼트를 10회(10rep) 반복 수행하는 경우, 일관성은 각 1rep 동작들 간의 일치 정도이고, 정확성은 정확한 자세의 동작에 대한 각 1rep 동작의 일치정도를 의미할 수 있다.Consistency In Action (CIA) means the degree of agreement with each other in the repetitive performance of the same motion motion, and the accuracy can mean the degree of agreement with the reference motion of each repetitive action. For example, when the user repeatedly performs the squat 10 times (10 rep), the consistency is the degree of correspondence between each 1 rep movement, and the accuracy may mean the degree of correspondence of each 1 rep movement to the movement of the correct posture.
소요시간은 운동에 소요한 총 시간, 운동동작 수행에 소요한 시간 또는 운동중 휴식을 취한 시간을 포함할 수 있다. 전술한 바와 같이, 동시간에 대한 복수의 센싱정보를 시간에 따라 분석하여 사용자의 운동동작(1rep)을 구분할 수 있고, 운동시간과 휴식시간을 구분할 수 있다.The time required may include a total time spent in exercise, a time spent in performing an exercise motion, or a time taken for a break during exercise. As described above, by analyzing a plurality of sensing information for the same time according to time, it is possible to classify a user's exercise motion (1 rep), and to classify exercise time and rest time.
카운트는 동작수행 횟수, 세트수 또는 정확한 동작수행이 이루어지지 않은 횟수를 포함할 수 있다. 사용자의 동시간에 대한 복수의 센싱정보를 시간에 따라 분석하여 운동동작 수행 횟수 또는 세트수를 산출할 수 있으며, 또한, 부정확한 자세나 호흡법에 의한 동작의 횟수를 산출할 수 있다.The count may include the number of times the operation is performed, the number of sets, or the number of times that the correct operation is not performed. By analyzing a plurality of sensing information for the same time of the user according to time, the number of times the exercise movement is performed or the number of sets may be calculated, and the number of movements by an incorrect posture or breathing method may be calculated.
또한, 사용자의 기록뿐만 아니라 예상 기록을 성능지표로 산출할 수 있다. 예상 기록은 사용자의 특정 목표에 대한 진행속도, 잔여 체력을 기초로 산출되는 것일 수 있다. 예를 들어, 크로스핏에 있어서 정해진 반복횟수에 대한 시간기록을 측정하는 경우, 센싱정보 또는 운동정보를 분석하여 사용자의 현재 페이스와 잔여 체력을 기초로 예상기록을 산출할 수 있다.In addition, it is possible to calculate not only the user's record but also the expected record as a performance indicator. The predicted record may be calculated based on the user's progressing speed for a specific goal and remaining physical strength. For example, in the case of measuring a time record for a predetermined number of repetitions in CrossFit, an expected record may be calculated based on the user's current pace and remaining physical strength by analyzing sensing information or exercise information.
또한, 상기 성능지표는 운동 전 운동 준비상태, 운동 후 신체 안정상태, 사용자의 체력 상태, 최대산소섭취량, 1분당 가능한 동작 횟수(RPM), 운동 중 강한 운동부하로 호흡을 멈춘 구간(Bracing Point), 운동시 소모된 칼로리 또는 운동 중 움직임에 대한 파워를 포함할 수 있으며, 이에 제한되는 것은 아니다.In addition, the performance indicators include exercise readiness before exercise, physical stability after exercise, physical fitness of the user, maximum oxygen intake, number of possible movements per minute (RPM), and the section where breathing was stopped due to a strong exercise load during exercise (Bracing Point). , It may include calories burned during exercise or power for movement during exercise, but is not limited thereto.
일 실시예에서, 본 발명의 일 실시예에 따른 생체정보 분석 방법은, 센싱 장치로부터 입력된 사용자의 인증정보를 수신하는 단계를 더 포함할 수 있다. 상기 인증정보는 ID, PW 또는 지문, 홍채, 안면인식을 포함하는 생체정보를 포함할 수 있다. 실시예에 의하면, 상기 사용자의 인증정보를 입력받음으로써, 서버는 사용자의 개인 센싱 장치뿐만 아니라 휘트니스 센터에 구비된 공용 센싱 장치에 의해서도 사용자의 상기 인증정보에 대응하여 센싱정보를 수신할 수 있고, 상기 센싱정보, 운동정보 또는 성능지표를 저장하거나 센싱 장치 또는 클라이언트 장치로 전송할 수 있다. 즉, 사용자는 공용 센싱 장치에 의해서도 인증정보를 인증(로그인)함으로써, 서버를 통해 본인의 생체정보, 운동정보, 기록을 확인하거나 관리할 수 있으며, 타인과의 공유를 통해 기록을 비교할 수 있다.In one embodiment, the method for analyzing biometric information according to an embodiment of the present invention may further include receiving authentication information of a user input from the sensing device. The authentication information may include ID, PW, or biometric information including fingerprint, iris, and face recognition. According to an embodiment, by receiving the user's authentication information, the server may receive sensing information corresponding to the user's authentication information by not only the user's personal sensing device but also a common sensing device provided in the fitness center, The sensing information, exercise information, or performance index may be stored or transmitted to a sensing device or a client device. That is, by authenticating (logging in) the authentication information through a common sensing device, the user can check or manage his or her own biometric information, exercise information, and records through the server, and compare the records through sharing with others.
도 8은 본 발명의 일 실시예에 따른 출력정보 생성 및 전송단계를 포함하는 생체정보 분석 방법을 설명하기 위한 도면이다. 도 8을 참조하면, 본 발명의 일 실시예에 따른 생체정보 분석 방법은, 서버가 센싱정보, 운동정보 또는 성능지표를 기초로 출력정보를 생성하는 출력정보생성단계 및 서버가 출력정보를 센싱장치 또는 클라이언트 장치에 전송하는 출력정보전송단계를 더 포함할 수 있다. 상기 출력정보는 센싱 장치 또는 클라이언트 장치의 출력부에서 출력되는 정보로 영상정보 또는 음성정보를 포함할 수 있으며, 센싱정보, 운동정보 또는 성능지표에 관한 정보를 포함할 수 있으나, 이에 제한되는 것은 아니다.8 is a diagram for explaining a method for analyzing biometric information including generating and transmitting output information according to an embodiment of the present invention. Referring to FIG. 8, in the biometric information analysis method according to an embodiment of the present invention, an output information generation step in which a server generates output information based on sensing information, exercise information, or performance index, and a server sensing output information Alternatively, it may further include the step of transmitting the output information transmitted to the client device. The output information is information output from the sensing device or the output unit of the client device, and may include image information or audio information, and may include sensing information, exercise information, or information on performance indicators, but is not limited thereto. .
도 9는 본 발명의 일 실시예에 따른 출력정보를 출력하기 위한 생체 정보 센싱 장치의 출력부의 구성을 도시한 도면이고, 도 10은 본 발명의 일 실시예에 따른 출력조명색을 변경하여 출력하기 위한 생체정보 센싱 장치 및 착용 모습을 설명하기 위한 도면이다. 도 9 및 도 10을 참조하면, 일 실시예에서, 서버로부터 출력정보를 전송받아 출력하기 위한 생체정보 센싱 장치는 하우징(100)의 전면에 출력부(400)를 더 포함할 수 있고, 출력부(400)는 조명부(410) 또는 디스플레이부(420)를 포함할 수 있다.9 is a diagram showing a configuration of an output unit of a biometric information sensing device for outputting output information according to an embodiment of the present invention, and FIG. 10 is a diagram for outputting by changing an output lighting color according to an embodiment of the present invention. A diagram for explaining a biometric information sensing device and a wearing state. 9 and 10, in one embodiment, the biometric information sensing device for receiving and outputting output information from a server may further include an output unit 400 on the front surface of the housing 100, and the output unit 400 may include an illumination unit 410 or a display unit 420.
조명부(410)는 센서에 의해 센싱된 생체정보 또는 서버, 클라이언트 장치로부터 수신한 정보의 변화에 대응하여 제어부의 제어에 의해 조명색을 변경하여 출력하는 장치를 포함할 수 있으며, LED를 포함하여 구성될 수 있으나 이에 제한되는 것은 아니다. 사용자는 상기 조명부의 출력으로 인하여, 클라이언트 장치(예를 들어, 스마트폰)를 가까이 두지 않아도 조명색의 변화 또는 조명의 유무로 생체정보 또는 운동정보를 손쉽게 획득할 수 있는 효과가 있다. 또한, 하나의 트레이너에 대해 복수의 사용자가 함께 훈련하는 그룹훈련(Group Exercise)에 있어서, 트레이너가 사용자의 생체정보, 운동정보 또는 부상징후 유무에 대해 간편하게 인식할 수 있어 운동의 효율성을 향상시키고, 사용자의 부상을 방지할 수 있는 효과가 있다.The lighting unit 410 may include a device that changes and outputs an illumination color under the control of a controller in response to changes in biometric information sensed by a sensor or information received from a server or a client device, and includes an LED. However, it is not limited thereto. Due to the output of the lighting unit, the user can easily obtain biometric information or exercise information by changing the lighting color or the presence or absence of lighting without putting a client device (eg, a smartphone) close. In addition, in a group exercise in which multiple users train together for one trainer, the trainer can easily recognize the user's biometric information, exercise information, or the presence or absence of injuries, thereby improving the efficiency of exercise. There is an effect that can prevent injury to the user.
구체적인 실시예에서, 사용자는 조명색의 변경 기준 정보를 설정할 수 있고, 서버는 상기 설정 기준 정보를 수신하고 이에 기초하여 출력조명색의 변경정보를 포함하는 출력정보를 생성 및 전송할 수 있다. 예를 들어, 특정 생체정보에 대한 정상범위를 설정하고, 상기 생체정보가 정상범위에 속하는 경우에는 녹색 조명을 출력하되, 정상범위를 벗어나는 경우 빨간색 조명을 출력하도록 설정할 수 있다. 구체적으로, 감량을 위한 운동에 있어서 심박수의 유지가 중요한데, 사용자가 심박수에 대해 140 이상이면 녹색, 140 미만이면 빨간색 조명을 출력하도록 설정하고 트레드밀(Treadmill)을 이용해 운동하는 경우에 있어서, 서버는 위 설정 기준 정보를 수신하고, 실시간으로 수신하는 심박수 정보를 분석하여 심박수가 140 미만으로 떨어지는 경우 빨간색 조명으로 출력하도록 변경하는 출력정보를 생성하고, 상기 센싱 장치에 전송할 수 있다. 이 경우, 사용자는 출력되는 조명색에 의해 간편하게 생체정보 또는 운동정보에 대한 피드백을 받을 수 있는 효과가 있다. 또한, 루틴 기반의 개인운동에 있어서, 사용자가 특정 운동에 대해 정확한 동작을 수행하는 경우 녹색 조명을, 부정확한 동작을 수행하는 경우 빨간색 조명을 출력하도록 설정하여 운동 자세에 대한 피드백을 받을 수 있다. 또한, 상기 조명색 변경 기준 정보는 복수의 기준 정보를 포함할 수 있다. 즉, 사용자는 복수의 기준을 설정하고, 서버는 복수의 기준 정보를 수신하여 각 기준 정보에 대한 출력정보를 생성하여 센싱 장치 또는 클라이언트 장치에 전송할 수 있다. 위의 실시예에서, 사용자가 동시에 부상 위험 징후의 발견시 노란색 조명을 출력하도록 설정하는 경우, 서버는 동작의 정확성 기준 정보 및 부상위험 기준 정보를 입력받아 각 기준에 대한출력정보를 생성 및 전송함으로써, 사용자는 간편하게 여러 정보(동작의 정확성, 부상 위험 징후 유무)를 획득할 수 있는 효과가 있다. 한편, 조명색의 변경 기준은 이에 제한되지 않고, 사용자가 다양하게 설정할 수 있다.In a specific embodiment, the user may set the lighting color change reference information, and the server may receive the setting reference information and generate and transmit output information including the change information of the output lighting color based thereon. For example, a normal range for specific biometric information may be set, and when the biometric information falls within the normal range, green illumination may be output, but when the biometric information is out of the normal range, red illumination may be output. Specifically, it is important to maintain the heart rate in exercise for weight loss.When the user sets the heart rate to output green when the heart rate is above 140 and red when it is less than 140, and when exercising using a treadmill, the server When setting reference information is received and the heart rate information received in real time is analyzed, output information that changes to be output as red light when the heart rate falls below 140 may be generated, and transmitted to the sensing device. In this case, there is an effect that the user can easily receive feedback on biometric information or exercise information by the output light color. In addition, in the routine-based personal exercise, feedback on the exercise posture may be received by setting to output green light when a user performs an accurate motion for a specific exercise and red light when performing an incorrect motion. In addition, the illumination color change reference information may include a plurality of reference information. That is, the user may set a plurality of criteria, and the server may receive the plurality of reference information, generate output information for each reference information, and transmit it to a sensing device or a client device. In the above embodiment, when the user is set to output yellow light when a user detects an injury risk sign at the same time, the server receives the operation accuracy reference information and the injury risk reference information, and generates and transmits output information for each standard. , There is an effect that users can easily obtain various information (accuracy of movement, presence of signs of injury risk). Meanwhile, the standard for changing the lighting color is not limited thereto, and a user may set variously.
일 실시예에서, 센싱 장치의 출력부(400)는 디스플레이부(420)를 포함할 수 있고 출력부는 조명부 및 디스플레이부를 모두 포함할 수 있다. 또한, 출력정보는 출력조명색의 변경정보 및 상기 출력조명색의 변경정보에 관한 센싱정보, 운동정보 또는 성능지표 정보를 동시에 포함할 수 있다. 예를 들어, 전술한 실시예에서 사용자가 심박수 140을 조명색 변경 기준 정보로 설정하여 운동하는 경우, 조명부는 상기 기준에 대응하는 색의 조명을 출력하고, 디스플레이부는 사용자의 심박수를 디스플레이할 수 있다. 이 경우, 사용자는 조명색에 의하여 간단히 정보(심박수의 140 초과 여부)를 획득할 수 있고, 조명색이 변경되는 경우 디스플레이부를 확인함으로써 구체적인 심박수 정보를 획득할 수 있다.In one embodiment, the output unit 400 of the sensing device may include a display unit 420 and the output unit may include both a lighting unit and a display unit. In addition, the output information may simultaneously include change information of the output lighting color and sensing information, exercise information, or performance index information about the change information of the output lighting color. For example, in the above-described embodiment, when a user exercises by setting the heart rate 140 as the lighting color change reference information, the lighting unit may output illumination of a color corresponding to the reference, and the display unit may display the user's heart rate. In this case, the user can simply obtain information (whether or not the heart rate exceeds 140) according to the illumination color, and when the illumination color changes, specific heart rate information can be obtained by checking the display unit.
일 실시예에서, 상기 출력정보는 상기 센싱정보, 운동정보 또는 성능지표를 기초로 생성한 추천운동정보를 포함할 수 있다. 상기 추천운동정보는 개인의 생체정보, 운동정보 또는 성능지표를 기초로 생성한 것으로, 사용자가 수행한 특정 운동의 정확한 자세 또는 호흡법, 준비운동정보 또는 마무리운동정보를 포함할 수 있으나, 이에 제한되는 것은 아니다.In an embodiment, the output information may include recommended exercise information generated based on the sensing information, exercise information, or performance index. The recommended exercise information is generated based on personal biometric information, exercise information, or performance index, and may include accurate posture or breathing method, warm-up exercise information, or finishing exercise information of a specific exercise performed by the user, but is limited thereto. It is not.
도 11 및 12는 본 발명의 일 실시예에 따른 생체정보, 운동정보 또는 성능지표 제공 화면을 설명하기 위한 도면이다. 도 11 및 12를 참조하면, 상기 출력정보는 클라이언트 장치(예를 들어, 스마트폰)에서 출력하는 정보일 수 있다. 출력정보는 사용자의 생체정보 데이터의 수치화 또는 그래프화한 정보일 수 있고, 운동정보 또는 성능지표를 포함할 수 있다.11 and 12 are diagrams for explaining a screen for providing biometric information, exercise information, or performance index according to an embodiment of the present invention. 11 and 12, the output information may be information output from a client device (eg, a smartphone). The output information may be numerical or graphed information of the user's biometric data, and may include exercise information or performance indicators.
도 13은 본 발명의 일 실시예에 따른 생체정보 센싱 장치(10)와 서버(20) 또는 클라이언트 장치(30)의 통신 관계를 도시한 도면이다. 통신은 유선 또는 무선을 포함하며, 예를 들어, 블루투스(bluetooth) 통신, BLE(Bluetooth Low Energy) 통신, 근거리 무선 통신(Near Field Communication unit), WLAN(와이파이) 통신, 지그비(Zigbee) 통신, 적외선(IrDA, infrared Data Association) 통신, WFD(Wi-Fi Direct) 통신, UWB(ultra wideband) 통신, Ant+ 통신 WIFI 통신 방법을 이용하여 통신할 수 있으나, 이에 제한되지 않는다.13 is a diagram showing a communication relationship between the biometric information sensing device 10 and the server 20 or the client device 30 according to an embodiment of the present invention. Communication includes wired or wireless, for example, Bluetooth communication, Bluetooth Low Energy (BLE) communication, near field communication unit, WLAN (Wi-Fi) communication, Zigbee communication, infrared (IrDA, infrared Data Association) communication, WFD (Wi-Fi Direct) communication, UWB (ultra wideband) communication, Ant+ communication WIFI communication method can be used to communicate, but is not limited thereto.
도 13을 참조하면, 통신 관계는 복수의 생체정보 센싱 장치가 하나의 트레이너 클라이언트 장치와 통신(도 13(a))하거나, 서버를 통하여 통신(도 13(b))하는 것을 포함할 수 있다. 본 실시예에 의하면, 한 트레이너에게 동일한 장소는 물론 각자 다른 장소에서 운동을 수행하는 복수의 사용자에 대한 정보가 전송될 수 있고, 트레이너는 한 장소의 다수에 대한 정보뿐만 아니라 원거리에 있는 복수의 사용자 개인의 생체정보 또는 운동정보를 분석하여 개인 맞춤형 피드백 또는 추천운동정보를 제공할 수 있는 효과가 있다. 즉, 트레이너는 공간에 제약 없이 원거리에 있는 다수의 사람들을 실시간으로 모니터링할 수 있다.Referring to FIG. 13, the communication relationship may include a plurality of biometric information sensing devices communicating with one trainer client device (FIG. 13(a)) or communicating through a server (FIG. 13(b)). According to the present embodiment, information on a plurality of users performing an exercise in the same place as well as in different places may be transmitted to one trainer. There is an effect of providing personalized feedback or recommended exercise information by analyzing personal biometric information or exercise information. In other words, the trainer can monitor a large number of remote people in real time without space constraints.
일 실시예에서, 도 5를 참조하면, 본 발명의 일 실시예에 따른 생체정보 분석 방법을 수행하기 위한 서버장치(20)는, 센싱장치로부터 센싱정보를 수신하거나 센싱정보, 운동정보 또는 성능지표를 센싱장치(10) 또는 클라이언트 장치(30)에 전송하는 통신부(500), 센싱정보를 기초로 운동정보를 생성하고 센싱정보 또는 운동정보를 기초로 성능지표를 산출하는 분석부(700) 및 센싱정보, 운동정보 또는 성능지표를 저장하는 저장부(600)를 포함할 수 있다.In one embodiment, referring to FIG. 5, a server device 20 for performing a biometric information analysis method according to an embodiment of the present invention may receive sensing information from a sensing device, or receive sensing information, exercise information, or performance indicator. The communication unit 500 that transmits the sensor to the sensing device 10 or the client device 30, the analysis unit 700 that generates exercise information based on the sensing information and calculates a performance index based on the sensing information or exercise information, and sensing It may include a storage unit 600 for storing information, exercise information or performance index.
이상에서 상술한 본 발명의 일 실시예에 따른 방법인, 생체정보 분석 방법은 하드웨어인 컴퓨터가 결합되어 실행되기 위해 생체정보 분석 컴퓨터 프로그램(또는 어플리케이션)으로 구현되어 매체에 저장될 수 있다.The biometric information analysis method, which is a method according to an embodiment of the present invention described above, may be implemented as a biometric information analysis computer program (or application) to be executed by combining a computer as hardware and stored in a medium.
본 발명의 실시예와 관련하여 설명된 방법 또는 알고리즘의 단계들은 하드웨어로 직접 구현되거나, 하드웨어에 의해 실행되는 소프트웨어 모듈로 구현되거나, 또는 이들의 결합에 의해 구현될 수 있다. 소프트웨어 모듈은 RAM(Random Access Memory), ROM(Read Only Memory), EPROM(Erasable Programmable ROM), EEPROM(Electrically Erasable Programmable ROM), 플래시 메모리(Flash Memory), 하드 디스크, 착탈형 디스크, CD-ROM, 또는 본 발명이 속하는 기술 분야에서 잘 알려진 임의의 형태의 컴퓨터 판독가능 기록매체에 상주할 수도 있다.The steps of a method or algorithm described in connection with an embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or a combination thereof. The software module includes Random Access Memory (RAM), Read Only Memory (ROM), Erasable Programmable ROM (EPROM), Electrically Erasable Programmable ROM (EEPROM), Flash Memory, hard disk, removable disk, CD-ROM, or It may reside on any type of computer-readable recording medium well known in the art to which the present invention pertains.
이상, 첨부된 도면을 참조로 하여 본 발명의 실시예를 설명하였지만, 본 발명이 속하는 기술분야의 통상의 기술자는 본 발명이 그 기술적 사상이나 필수적인 특징을 변경하지 않고서 다른 구체적인 형태로 실시될 수 있다는 것을 이해할 수 있을 것이다. 그러므로, 이상에서 기술한 실시예들은 모든 면에서 예시적인 것이며, 제한적이 아닌 것으로 이해해야만 한다.In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those of ordinary skill in the art to which the present invention pertains can be implemented in other specific forms without changing the technical spirit or essential features. You can understand. Therefore, the embodiments described above are illustrative in all respects, and should be understood as non-limiting.

Claims (14)

  1. 서버가 센싱정보를 수신하는 정보수신단계;An information receiving step of the server receiving sensing information;
    서버가 상기 센싱정보를 기초로 운동정보를 생성하는 운동정보생성단계;An exercise information generation step in which the server generates exercise information based on the sensing information;
    서버가 상기 센싱정보 또는 상기 운동정보를 기초로 성능지표를 산출하는 지표산출단계; 및An index calculation step in which a server calculates a performance index based on the sensing information or the exercise information; And
    서버가 상기 센싱정보, 운동정보 또는 상기 성능지표를 저장하는 저장단계;A storage step in which the server stores the sensing information, the exercise information, or the performance index;
    를 포함하고,Including,
    상기 센싱정보는 호흡, 심전도, 산소포화도, 체온, 위치 또는 동작정보를 포함하는 생체정보가 센싱된 것이고,The sensing information is biometric information including respiration, electrocardiogram, oxygen saturation, body temperature, location, or motion information is sensed,
    상기 운동정보는 운동종류, 운동횟수 또는 운동강도 정보를 포함하는, 생체 정보 분석 방법.The exercise information includes exercise type, exercise frequency, or exercise intensity information.
  2. 제1항에 있어서,The method of claim 1,
    상기 호흡정보는 탄소나노튜브가 도포된 섬유형 호흡센서가 흉부의 부피변화를 감지하는 방식에 의해 센싱되고, 호흡패턴, 호흡횟수 또는 호흡량을 포함하는, 생체정보 분석 방법.The breathing information is sensed by a method in which a fiber-type breathing sensor coated with a carbon nanotube detects a change in the volume of the chest, and includes a breathing pattern, a breathing frequency, or a breathing volume.
  3. 제1항에 있어서,The method of claim 1,
    상기 운동정보 생성단계는,The exercise information generating step,
    서버가 운동별 특징값을 적용하여 운동종류정보를 생성하는 단계를 포함하고,The server includes the step of generating exercise type information by applying the characteristic values for each exercise,
    상기 운동별 특징값은 운동종류별 수평, 수직 또는 회전동작의 특징값을 포함하는, 생체정보 분석 방법.The characteristic value for each exercise includes a characteristic value of a horizontal, vertical or rotational motion for each exercise type.
  4. 제1항에 있어서,The method of claim 1,
    상기 운동정보생성단계는,The exercise information generation step,
    상기 수신한 센싱정보를 기초로 데이터셋을 구축하는 단계; 및Building a data set based on the received sensing information; And
    상기 센싱정보 및 상기 데이터셋을 기초로 딥러닝에 의하여 운동종류정보를 생성하는 단계를 포함하는, 생체정보 분석 방법.And generating exercise type information by deep learning based on the sensing information and the data set.
  5. 제4항에 있어서,The method of claim 4,
    상기 데이터셋은 하나 이상의 상기 센싱정보와 운동종류정보를 매칭하여 구축되는 것을 특징으로 하는, 생체정보 분석 방법.The data set is constructed by matching one or more of the sensing information and exercise type information.
  6. 제1항에 있어서,The method of claim 1,
    상기 운동정보생성단계는,The exercise information generation step,
    서버가 동시간에 대한 복수의 센싱정보를 시간정보에 대응하도록 비교하여 운동정보를 생성하는 단계를 포함하는, 생체정보 분석 방법.Comprising the step of generating exercise information by comparing the plurality of sensing information for the same time to correspond to the time information, biometric information analysis method.
  7. 제1항에 있어서,The method of claim 1,
    상기 지표산출단계는 딥러닝에 의하여 상기 성능지표를 산출하는 단계이고,The index calculation step is a step of calculating the performance index by deep learning,
    상기 성능지표는 일관성, 정확성, 소요시간, 카운트 또는 예상기록을 포함하고,The performance indicators include consistency, accuracy, time required, count or expected record,
    상기 일관성은 동일한 운동동작의 반복수행에 있어서 각 수행동작의 일치정도를 의미하고,The consistency refers to the degree of correspondence of each performance movement in the repeated performance of the same movement movement,
    상기 정확성은 각 수행동작의 기준동작에 대한 일치정도를 의미하고,The accuracy refers to the degree of agreement with the reference operation of each performed operation,
    상기 소요시간은 운동에 소요한 총 시간, 운동동작 수행에 소요한 시간 또는 운동 중 휴식을 취한 시간을 포함하고,The time required includes the total time spent on exercise, time spent on performing exercise movements, or time taken for rest during exercise,
    상기 카운트는 동작수행 횟수, 세트수 또는 정확한 동작수행이 이루어지지 않은 횟수를 포함하고,The count includes the number of times the operation was performed, the number of sets, or the number of times that the correct operation was not performed,
    상기 예상기록은 진행속도 및 체력을 기초로 산출된 예상기록을 의미하는, 생체정보 분석 방법.The predicted record means an expected record calculated based on a progression speed and physical strength.
  8. 제1항에 있어서,The method of claim 1,
    서버가 센싱장치로부터 입력된 사용자의 인증정보를 수신하는 단계를 더 포함하는, 생체정보 분석 방법.The method of analyzing biometric information further comprising the step of receiving, by the server, authentication information of the user input from the sensing device.
  9. 제1항에 있어서,The method of claim 1,
    서버가 상기 센싱정보, 상기 운동정보 또는 상기 성능지표를 기초로 출력정보를 생성하는 출력정보생성단계; 및An output information generation step in which a server generates output information based on the sensing information, the exercise information, or the performance index; And
    서버가 상기 출력정보를 센싱장치 또는 클라이언트 장치에 전송하는 출력정보전송단계;를 더 포함하고,The server further comprises an output information transmission step of transmitting the output information to a sensing device or a client device,
    상기 출력정보는 센싱장치 또는 클라이언트 장치의 출력부에서 출력되는 정보인, 생체정보 분석 방법.The output information is information output from a sensing device or an output unit of a client device.
  10. 제9항에 있어서,The method of claim 9,
    상기 출력정보는 상기 센싱정보, 상기 운동정보 또는 상기 성능지표의 변화에 대응하는 출력조명색의 변경 정보를 포함하는, 생체정보 분석 방법.The output information includes the sensing information, the exercise information, or change information of an output lighting color corresponding to a change in the performance indicator.
  11. 제9항에 있어서,The method of claim 9,
    상기 출력정보는 상기 센싱정보, 상기 운동정보 또는 상기 성능지표를 기초로 생성한 추천운동정보를 포함하는, 생체정보 분석 방법.The output information includes the sensing information, the exercise information, or recommended exercise information generated based on the performance indicator.
  12. 제9항에 있어서,The method of claim 9,
    상기 클라이언트 장치는 사용자 클라이언트 장치 또는 트레이너 클라이언트장치를 포함하고,The client device includes a user client device or a trainer client device,
    상기 출력정보전송단계는 복수의 센싱장치로부터 수신한 상기 센싱정보에 대응하는 복수의 상기 출력정보를 하나의 트레이너 클라이언트 장치에 전송하는 단계를 포함하는, 생체정보 분석 방법.The transmitting of the output information includes transmitting a plurality of the output information corresponding to the sensing information received from a plurality of sensing devices to one trainer client device.
  13. 하드웨어인 컴퓨터와 결합되어, 제1항 내지 제12항 중 어느 한 항의 방법을 실행시키기 위하여 매체에 저장된, 생체정보 분석 프로그램.A biometric information analysis program combined with a computer as hardware and stored in a medium to execute the method of any one of claims 1 to 12.
  14. 센싱장치로부터 센싱정보를 수신하거나 센싱정보, 운동정보 또는 성능지표를 상기 센싱장치 또는 클라이언트 장치에 전송하는 통신부;A communication unit for receiving sensing information from a sensing device or transmitting sensing information, exercise information, or performance index to the sensing device or a client device;
    상기 센싱정보를 기초로 운동정보를 생성하고 상기 센싱정보 또는 상기 운동 정보를 기초로 성능지표를 산출하는 분석부; 및An analysis unit for generating exercise information based on the sensing information and calculating a performance index based on the sensing information or the exercise information; And
    상기 센싱정보, 상기 운동정보 또는 상기 성능지표를 저장하는 저장부;를 포함하고,Including; a storage unit for storing the sensing information, the exercise information or the performance index,
    상기 센싱정보는 호흡, 심전도, 산소포화도, 온도, 위치 또는 동작정보를 포함하고,The sensing information includes respiration, electrocardiogram, oxygen saturation, temperature, location or motion information,
    상기 운동정보는 운동종류, 운동횟수 또는 운동강도 정보를 포함하는, 생체 정보 분석 서버장치.The exercise information includes exercise type, exercise frequency, or exercise intensity information.
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