KR101478220B1 - Gloves unit for diagnosis health state ans system for diagnosis health state using the same - Google Patents

Abstract

A health condition diagnosis glove unit and a health state diagnosis system using the same are disclosed. A glove unit for diagnosing a health condition according to an embodiment of the present invention includes gloves having at least one finger accommodating portion, a living body signal measuring device formed on the glove and formed on the finger accommodating portion for measuring a user's living body signal, A body condition analyzing device for receiving the user's bio-signal measurement data from the signal measuring device, analyzing the received bio-signal measurement data to generate heartbeat analysis data, and calculating a user's stress index based on the heartbeat analysis data .

Description

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a glove unit for diagnosing a health condition,

BACKGROUND OF THE INVENTION 1. Field of the Invention [0001] The present invention relates to a health state diagnosis technique, and more particularly, to a glove unit for health state diagnosis and a health state diagnosis system using the same.

Modern people are under stress because of their heavy work and competition. Stress is regarded as a source of panacea, and it is recognized as a unique area of doctors to judge the degree of stress and the degree of stress. However, physicians are not able to provide objective criteria for the degree of stress. Therefore, a diagnostic system that indirectly grasps the degree of stress through the mental and physical responses of the testee has been proposed. However, the method of measuring the stress index through the self test of the physical condition and the behavior of the testee, It is not only difficult to make accurate judgment and measurement, but also has a problem that the reliability of the result is low.

Korean Patent Publication No. 10-1999-0073602 (Oct. 05, 1999)

An embodiment of the present invention is to provide a glove unit for diagnosing a health condition and a health condition diagnosis system using the same, which can easily diagnose a user's health state at any time through gloves.

An embodiment of the present invention is to provide a glove unit for diagnosing a health condition that can accurately calculate a stress index of a user and a health condition diagnosis system using the glove unit.

A glove unit for diagnosing a health condition according to an embodiment of the present invention includes: a glove having at least one finger receiving portion; A biometric signal measuring device formed on the finger receiving part and measuring a user's biometric signal; And a controller configured to receive the bio-signal measurement data of the user from the bio-signal measuring device, analyze the received bio-signal measurement data to generate heartbeat analysis data, And a physical condition analyzing device for calculating a user's stress index.

The health state diagnosis system according to an embodiment of the present invention may include: the health state diagnosis glove unit; And a portable terminal for receiving at least one of the user's bio-signal measurement data, heartbeat analysis data, stress index, appropriate respiratory rate, and respiratory control induction guidance information from the health state diagnostic glove unit and displaying the received information on the screen.

According to the embodiment of the present invention, when the user wears gloves, the user's bio-signal is measured and analyzed to analyze at least one of heartbeat analysis data, stress index, appropriate breathing rate, It is possible to easily diagnose his / her health condition at any time according to the convenience of the user. Further, by applying the stress index of the user to the fuzzy theory based on expert knowledge and experience, it is possible to provide more accurate and reliable stress index information to the user.

1 illustrates a glove unit for a health condition diagnosis according to an embodiment of the present invention.
2 is a block diagram of a bio-signal measurement apparatus according to an embodiment of the present invention.
3 is a block diagram of a physical condition analyzing apparatus according to an embodiment of the present invention.
4 is a view illustrating a state in which heart rate analysis data is displayed on a screen by a physical condition analyzing apparatus according to an embodiment of the present invention.
5 is a view illustrating a state where a user's stress index is displayed on a screen by a physical condition analyzing apparatus according to an embodiment of the present invention.
FIG. 6 is a view illustrating a breathing control induction guide information displayed on a screen by a physical condition analyzing apparatus according to an embodiment of the present invention. FIG.
7 is a diagram showing a configuration of a stress index calculating unit according to an embodiment of the present invention.
8 is a diagram illustrating a state in which the fuzzing unit according to an embodiment of the present invention converts high frequency heart data to belonging to input membership functions.
9 is a diagram illustrating a state in which the fuzzification unit according to an embodiment of the present invention converts low frequency heart data to belonging to input membership functions.
FIG. 10 illustrates a fuzzy rule according to an embodiment of the present invention. FIG.
11 illustrates various embodiments in which the fuzzy inference unit of the present invention deduces the membership of an output membership function for a fuzzy rule;
12 is a view showing a state in which the non-fuzzy part of the present invention finds an area less than the deduced value of the belonging membership of the output membership function of the fuzzy rule in the output membership function inferred to belong to the fuzzy rule.
13 is a diagram showing a state in which the non-fuzzy unit of the present invention finds the center of gravity by superimposing the area obtained by the corresponding output membership function and each area for the 25 fuzzy rules.
FIG. 14 illustrates a configuration of a health condition diagnosis system according to an embodiment of the present invention. FIG.

Hereinafter, the glove unit for diagnosing health condition according to the present invention and the health condition diagnosis system using the same will be described in detail with reference to FIG. 1 to FIG. However, this is an exemplary embodiment only and the present invention is not limited thereto.

In the following description, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear. The following terms are defined in consideration of the functions of the present invention, and may be changed according to the intention or custom of the user, the operator, and the like. Therefore, the definition should be based on the contents throughout this specification.

The technical idea of the present invention is determined by the claims, and the following embodiments are merely a means for efficiently describing the technical idea of the present invention to a person having ordinary skill in the art to which the present invention belongs.

1 is a view illustrating a glove unit for diagnosing a health condition according to an embodiment of the present invention.

Referring to FIG. 1, the health condition diagnostic glove unit 100 includes a glove 102, a bio-signal measuring apparatus 104, and a physical condition analyzing apparatus 106.

The glove 102 includes a finger receiving portion 102-1 capable of receiving a finger. Although the glove 102 has been described as having all of the five finger receiving portions 102-1 here, the present invention is not limited thereto. The finger holding portion 102-1 may include at least one finger receiving portion 102-1. The gloves 102 may be made of, for example, a light shielding material. However, it is not necessary that the entire glove 102 is made of a light shielding material, and only the finger holding portion 102-1 where the bio-signal measuring device 104 is formed can be made of a light shielding material.

The bio-signal measuring device 104 may be formed in the finger receiving portion 102-1 in the glove 102. [ The living body signal measuring device 104 measures the user's living body signal and transmits the measured living body signal measuring data to the physical condition analyzing device 106. Here, the bio-signal may be at least one of, for example, a heart rate variability (HRV) of a user, a blood volume change due to contraction and relaxation of a blood vessel, and a change in blood oxygen saturation according to a heartbeat. At this time, the bio-signal measuring device 104 may transmit the bio-signal measurement data to the physical condition analyzing device 106 through wire communication or wireless communication. Although the bio-signal measuring apparatus 104 and the physical condition analyzing apparatus 106 are shown as being connected by wire, the present invention is not limited thereto. The bio-signal measuring apparatus 104 and the physical condition analyzing apparatus 106 may be connected to a wireless communication network It is possible.

The physical condition analyzing apparatus 106 may be formed on one side of the glove 102 (e.g., the back of the hand). And receives the bio-signal measurement data from the bio- The physical condition analyzing apparatus 106 analyzes the received bio-signal measurement data to generate heartbeat analysis data. Here, the heart rate analysis data includes at least one of heart rate, activity of sympathetic nerves, and activity of parasympathetic nerves. The physical condition analyzing apparatus 106 can calculate the user's stress index using the heartbeat analysis data. The physical condition analyzing apparatus 106 can calculate the appropriate breathing number of the user based on the stress index. The physical condition analyzing apparatus 106 can induce the user's breath control so that the user's breathing number reaches the calculated correct breathing count. The physical condition analyzing apparatus 106 may display at least one of heartbeat analysis data, stress index, appropriate respiratory rate, and respiratory control induction guidance information on the screen.

Here, it has been described that the heart rate, the activity of the sympathetic nerve, the activity of the parasympathetic nerve, the stress index, the optimal respiratory rate, and the like are generated using the user's bio-signal measurement data. However, the present invention is not limited to this, State diagnostic information (e. G., A minimum blood pressure, a systolic blood pressure, an autonomic balance, an immune level, a fatigue index, etc.).

According to the embodiment of the present invention, if the user wears the glove unit 100 for health state diagnosis, the user's bio-signal is measured and analyzed to obtain heart rate analysis data, a stress index, an appropriate breath rate, By displaying at least one of the information on the screen, it is possible to easily diagnose his / her health condition at any time according to the convenience of the user.

2 is a block diagram of a biological signal measurement apparatus according to an embodiment of the present invention.

2, the bio-signal measuring device 104 includes a housing part 111, a light generating part 113, and a light receiving part 115. [

The housing part 111 may be formed to enclose a finger in the finger receiving part 102-1. The housing part 111 may be made of a plastic material, but is not limited thereto, and may be made of various other materials. At this time, the finger receiving portion 102-1 may be made of a light shielding material. In this case, it is possible to prevent the external direct sunlight from interfering with the light generated in the light generating part 113 by interrupting the penetration into the housing part 111. As a result, it becomes possible to increase the accuracy of the bio-signal measurement through the bio-signal measuring device 104. That is, it is possible to provide more reliable diagnosis information by increasing the accuracy from the bio-signal measurement step, which is the basis of the user's health diagnosis.

The light generating part 113 may be formed on one side of the housing part 111 to generate light. Light generated by the light generating unit 113 passes through the user's finger and reaches the other side of the housing unit 111. [ The light generating unit 113 may include a first light generating unit 113-1 and a second light generating unit 113-2. At this time, the first light generating part 113-1 generates light of an infrared wavelength (for example, 940 nm), and the second light generating part 113-2 generates light of a visible light wavelength (for example, 660 nm ) Can be generated. Here, the first light generating portion 113-1 and the second light generating portion 113-2 are separately formed. However, the present invention is not limited thereto, and the first light generating portion 113-1 and the second light generating portion 113-2 may be formed separately, The generating portion 113-2 may be integrally formed.

The light receiving portion 115 is formed on the other side of the housing portion 111, and the light generating portion 113 receives the light transmitted through the user's finger. At this time, according to the heartbeat of the user, the absorption rate of the light of the infrared wavelength generated by the first light generating portion 113-1 and the light of the visible light wavelength generated by the second light generating portion 113-1 are different in the finger Thereby changing the transmittance of light transmitted through the user's finger. The change in the transmittance of light according to the heartbeat of the user of the light receiving unit 115 can be output as a photocurrent signal.

The bio-signal measuring device 104 may transmit the bio-signal measurement data, which is obtained by amplifying the photocurrent signal output from the light-receiving unit 115 and removing noise, to the physical condition analyzer 106. However, the present invention is not limited thereto, and the bio-signal measuring device 104 may transmit the photocurrent signal itself output from the light-receiving unit 115 to the physical condition analyzer 106 as bio-signal measurement data. At this time, the bio-signal measuring apparatus 104 may transmit the bio-signal measurement data periodically (or in real time) to the physical condition analyzing apparatus 106. Here, the bio-signal measuring device 104 measures the user's bio-signal using the optical sensor. However, the present invention is not limited thereto, and the user's bio-signal may be measured by various other methods.

FIG. 3 is a block diagram of a physical condition analyzing apparatus according to an embodiment of the present invention. Referring to FIG.

3, the physical condition analyzing apparatus 106 includes a communication unit 121, a heartbeat analyzing unit 123, a stress index calculating unit 125, a respiration control managing unit 127, a display unit 129, (131).

The communication unit 121 receives the biometric signal measurement data transmitted by the biometric signal measurement device 104. [ At this time, the communication unit 121 can receive the bio-signal measurement data periodically (or in real time) from the bio-signal measurement apparatus 104. [

The heartbeat analyzer 123 analyzes the bio-signal measurement data received by the communication unit 121 to generate heartbeat analysis data. For example, the heart rate analyzer 123 can calculate the heart rate by extracting peak data due to contraction and relaxation of the blood vessel due to heartbeat from the received bio-signal measurement data. Then, the heart rate analyzer 123 can calculate the sympathetic activity and the parasympathetic activity by analyzing the frequency of the received bio-signal measurement data. When the bio-signal measurement data is analyzed by frequency domain, the frequency range of 0.2 to 0.4 Hz shows the activity of the parasympathetic nerve, and the frequency range of 0.032 to 0.2 Hz shows the activity of the sympathetic nerve. Hereinafter, the activity of the parasympathetic nerve is referred to as high frequency heart data, and the activity of the sympathetic nerve is referred to as low frequency heart data.

The stress index calculating unit 125 calculates the user's stress index using the heartbeat analysis data generated by the heartbeat analyzing unit 123. [ At this time, the stress index calculating unit 125 may calculate the user's stress index by substituting the heartbeat analysis data into the fuzzy logic. In this case, fuzzy rules based on expert knowledge and experience can be applied to produce a more accurate and reliable stress index. A detailed description thereof will be described later.

The breath control management unit 127 determines the appropriate breathing number of the user based on the stress index calculated by the stress index calculating unit 125. [ However, the present invention is not limited to this, and the respiratory control management unit 127 may determine the appropriate respiratory rate of the user based on the heart rate generated by the heart rate analyzer 123, The number of breaths can also be determined.

The breath control management unit 127 may induce the breath control of the user so that the breath of the user reaches an appropriate breath rate. For example, the breath control management unit 127 may provide the user with breath control guidance information so that the user's breath reaches the appropriate breath rate. At this time, the breath control induction guidance information may include information on the time of inhalation and exhalation.

The display unit 129 can display the heartbeat analysis data (e.g., heart rate, sympathetic activity, parasympathetic activity, etc.) generated by the heartbeat analyzer 123 on the screen have. The display unit 129 can display the stress index of the user calculated by the stress index calculating unit 125 on the screen as shown in FIG. The display unit 129 can display the breath control guidance information of the breath control management unit 127 on the screen as shown in FIG.

The storage unit 131 may store the bio-signal measurement data received by the communication unit 121. The storage unit 131 may store heartbeat analysis data (for example, heart rate, sympathetic activity, parasympathetic activity, etc.) generated by the heart rate analyzer 123. The storage unit 131 may store the stress index of the user calculated by the stress index calculating unit 125. The storage unit 131 may store the breath control guidance information of the breath control manager 127.

7 is a diagram illustrating a configuration of a stress index calculating unit according to an embodiment of the present invention. Here, the stress index calculating unit 125 calculates a user's stress index using fuzzy logic.

7, the stress index calculating section 125 includes a fuzzification section 141, a fuzzy rule section 143, a fuzzy inference section 145, and a fuzzification section 147.

The fuzzification unit 141 transforms the input heartbeat analysis data to the membership for predetermined input membership functions. At this time, the fuzzification unit 141 may convert the high frequency heart data HF and the low frequency heart data LF inputted from the heart rate analyzing unit 123 to the belonging to the predetermined input membership functions. Here, the input membership function is, for example, very small (VS), small (S), midium (M), large (B), very large There can be a function of level. At this time, each input membership function may be set so that its range overlap with at least one other level input membership function.

8 is a diagram illustrating a state in which a fuzzing unit according to an embodiment of the present invention converts high frequency heart data to belonging to input membership functions.

Referring to FIG. 8, five input membership functions of very small (VS), small (S), medium (M), large (B), and very large (VB) When the membership degree of each input membership function is taken as the y-axis value, it can be set as a function of a triangular shape. However, the input membership function is not limited to a function of a triangular shape, but may be set to various other shapes (e.g., a rectangle or a trapezoid) depending on the knowledge and experience of the expert. At this time, each input membership function can be set to overlap with the neighbor input membership function.

When the high frequency heart data HF is input from the heart rate analyzer 123, the fuzzifier 141 converts the input high frequency heart data to a predetermined speed for the input membership functions. At this time, the high-frequency cardiac data can be substituted with a value between 0 and 10 and inputted.

For example, when the high-frequency cardiac data HF having a value of 1.5 is input, the membership degree of the input membership function, which is very small (VS) of the high-frequency cardiac data, is 0.4 and the input membership function The membership degree of the input membership function of the intermediate (M), the large (B), and the very large (VB) is 0, respectively. At this time, the fuzzification unit 141 calculates the angular velocities V (V), V (V), V (V), V The membership degrees of the input membership functions are set to 0.4, 0.6, 0, 0, and 0, respectively. Thus, one high frequency cardiac data (HF) has a membership degree for each of the five input membership functions.

9 is a diagram illustrating a state in which the fuzzification unit according to an embodiment of the present invention converts low frequency heart data to belonging to input membership functions.

9, an input membership function of five levels, i.e., very small (VS), small (S), medium (M), large (B), and very large (VB) When the membership degree of each input membership function is taken as the y-axis value, it can be set as a function of a triangular shape. However, the input membership function is not limited to a function of a triangular shape, but may be set to various other shapes (e.g., a rectangle or a trapezoid). At this time, each input membership function can be set to overlap with the neighbor input membership function.

Here, when the low frequency heart data LF is inputted from the heart rate analyzing unit 123, the fuzzifying unit 141 converts the input low frequency heart data to the belonging speed for predetermined input membership functions. At this time, the low frequency cardiac data can be input with a value between 0 and 10.

For example, when low-frequency heart data LF having a value of 4.5 is input, the membership of a very small input (VS) of the low-frequency heart data becomes 0, and the input membership function The membership degree of the input membership function of intermediate (M) is 0.7, and the membership degrees of the input membership functions of large (B) and very large (VB) are 0, respectively. At this time, the fuzzification unit 141 sets the values of the small (VS), the small (S), the medium (M), the large (B) and the very large (VB) for the low frequency cardiac data (LF) The membership degrees of the input membership functions are set to 0, 0.3, 0.7, 0, and 0, respectively. Thus, one low-frequency heart data (LF) has a membership degree for each of the five input membership functions.

The fuzzy rule unit 143 sets a fuzzy rule according to an instruction of an administrator. The fuzzy rule sets the linguistic relationship between input and output. For example, the IF input is A and the THEN output is B. The fuzzy rule may be a set of relations with a preset output membership function when the membership of each input membership function of high frequency heart data (HF) and low frequency heart data (LF) is input.

10 is a diagram illustrating a fuzzy rule according to an embodiment of the present invention. In this case, the fuzzy rule sets the relationship with the preset output membership function when the membership degrees of the input membership functions of the high frequency heart data (HF) and the low frequency heart data (LF) are input.

Referring to FIG. 10, the output membership function may include, for example, the degree of stress is stressed (S), tense (T), normal (N), calm (C) Relaxed: R).

Here, the fuzzy rule can be considered in some cases: 1) IF high frequency cardiac data (HF) is very small (VS) and low frequency cardiac data (LF) is very small (VS), THEN output membership function is stressed ). ≪ / RTI > 2) IF high frequency heart data (HF) is medium (M) and low frequency heart data (LF) is small (VS), THEN output membership function can be set to T (T). 3) IF high frequency cardiac data (HF) is small (S) and low frequency heart data (LF) is large (B), THEN output membership function is normal (N). 4) IF high frequency cardiac data (HF) is large (B) and low frequency heart data (LF) is large (B), THEN output membership function is calmness (C). 5) IF high frequency cardiac data (HF) is very large (VB) and low frequency cardiac data (LF) is large (B), THEN output membership function can be set to comfort (R). At this time, the fuzzy rule can be set as expert knowledge and experience.

Here, the membership degree of the input membership function of the high-frequency cardiac data (HF) and the membership degree of the low-frequency heart (HF) are calculated using the AND operation between the belonging membership of the high-frequency cardiac data (HF) and the membership degree of the low- But the present invention is not limited to this. The membership of the input membership function of the high-frequency cardiac data HF and the membership of the input membership function of the low-frequency cardiac data LF The higher membership degree of the input membership function of the high frequency cardiac data (HF) and the membership degree of the input membership function of the low frequency cardiac data (LF) may be selected and operated using the OR operation.

The fuzzy inference unit 145 substitutes the membership degrees of the respective input membership functions of the high frequency heart data HF and the low frequency heart data LF into the fuzzy rule set by the fuzzy rule unit 143, That is, the degree of belonging). At this time, the fuzzy inference unit 145 applies the membership degree of each input membership function of the high frequency heart data HF and the low frequency heart data LF to all the predetermined fuzzy rules, .

If the fuzzy rule is set as shown in FIG. 10, one high frequency cardiac data (HF) has a membership degree for five input membership functions, and one low frequency cardiac data (LF) has five membership membership functions Since there is a membership degree, a total of 25 fuzzy rules are set. Therefore, membership of each input membership function of high frequency cardiac data (HF) and low frequency cardiac data (LF) is applied to all 25 preset fuzzy rules to infer the membership of the output membership function for each fuzzy rule.

11 is a diagram showing various embodiments in which the fuzzy inference unit of the present invention deduces the membership of the output membership function for the fuzzy rule. Here, the output membership function of five levels, i.e., stressed, tense, normal, calm, and relaxed, has a stress index as an x-axis value, and each output membership function Can be pre-set as a function of a triangle shape when the membership degree of the triangle is defined as a y value. However, the output membership function is not limited to a function of a triangular shape, but may be set to various other shapes (for example, a rectangle or a trapezoid). At this time, each output membership function can be set to overlap the neighboring output membership function.

Referring to FIG. 11A, the fuzzy rule includes IF high frequency cardiac data HF is very small VS and low frequency heart data LF is very small VS, THEN output membership function is stressed S, And the membership degree of the input membership function of very small value (VS) of the high frequency cardiac data HF is 0.4 and the membership degree of the input membership function of very small value (VS) of the low frequency cardiac data LF is zero, The output membership function for the fuzzy rule is stressed and the membership degree of the membership membership function of the low frequency cardiac data LF and the membership degree of the input membership function of the high frequency cardiac data HF It is deduced to be 0, which is the smaller of the membership degrees (i.e., 0).

11 (b), the fuzzy rule is set to IF high frequency cardiac data HF is very small VS and low frequency heart data LF is is small S, THEN output membership function is comfort And the membership degree of the input membership function, which is very small (VS) of the high frequency cardiac data (HF) is 0.4 and the membership degree of the input membership function is small (VS) of the low frequency cardiac data LF is 0.3, The membership membership function of the low frequency cardiac data LF is comfort (R) and the membership degree of the membership membership function of the low frequency cardiac data LF (i.e., , 0.3), which is the smallest, 0.3.

11 (c), the fuzzy rule is set to IF high-frequency cardiac data HF is very small VS and low-frequency heart data LF is intermediate M, THEN output membership function is comfort R And the membership degree of the input membership function, which is very small (VS) of the high frequency cardiac data (HF) is 0.4 and the membership degree of the input membership function of the middle (M) of the low frequency cardiac data LF is 0.7, The membership membership function of the low frequency cardiac data LF is comfort (R) and the membership degree of the membership membership function of the low frequency cardiac data LF (i.e., , 0.7), which is 0.4, which is the smallest.

The de-fuzzification unit 147 de-fuzes the result of the inference of the fuzzy inference unit 145 to calculate the user's stress index. Specifically, the defuzzification unit 147 obtains an area of the output membership function of the corresponding fuzzy rule that is inferred to belong to the corresponding fuzzy rule to be less than the inferred value of the belonging member of the output membership function for each fuzzy rule. Next, the defuzzification unit 147 calculates the stress index of the user by superimposing the area obtained in the corresponding output membership function for each fuzzy rule, and then calculating the center of gravity from the overlapping area.

FIG. 12 is a diagram showing a state in which the non-fuzzy part of the present invention finds an area less than the deduced value of the membership of the output membership function of the corresponding fuzzy rule in the output membership function inferred to belong to the fuzzy rule.

Referring to FIG. 12A, when the output membership function for the fuzzy rule in FIG. 11A is stressed and its membership degree is inferred to be zero, Is obtained from an output membership function called Stressed. In this case, since the belonging degree of the corresponding output membership function is 0, its area also becomes zero.

Referring to FIG. 12B, when the output membership function for the corresponding fuzzy rule is relaxed and its membership degree is estimated to be 0.3 in FIG. 11B, the defuzzification unit 147 In the output membership function called Relaxed, the area with the membership of 0.3 or less is obtained.

Referring to FIG. 12C, when the output membership function for the corresponding fuzzy rule in FIG. 11C is relaxed and its membership degree is inferred to be 0.4, the defuzzification unit 147 In the output membership function called Relaxed, an area having a membership degree of 0.4 or less is obtained.

13 is a diagram showing a state in which the non-fuzzy unit of the present invention finds the center of gravity by superimposing the area obtained by the corresponding output membership function and the respective areas for the 25 fuzzy rules.

Referring to FIG. 13, the non-fuzzy logic unit 147 determines whether the output membership function derived from the fuzzy rule for each of the 25 fuzzy rules belongs to the output membership function of the corresponding fuzzy rule, The area below the value is obtained. Next, the non-fuzzification unit 147 calculates the stress index of the user by superimposing the areas of the output membership functions obtained from the 25 fuzzy rules, and then calculating the center of gravity of the overlapping area. Here, the center of gravity of the overlapping area is 5.76, which is the user's stress index.

FIG. 14 is a diagram illustrating a configuration of a health condition diagnosis system according to an embodiment of the present invention.

Referring to FIG. 14, the health condition diagnosis system 200 includes a health condition diagnosis glove unit 100 and a portable terminal 180.

The health condition diagnosis glove unit 100 can measure the user's vital signs. The health condition diagnosis glove unit 100 can generate heartbeat analysis data by analyzing the measured bio-signals. The health condition diagnostic glove unit 100 can calculate the user's stress index using heartbeat analysis data. The health state diagnostic glove unit 100 may generate the breath control instruction information for calculating the user's appropriate breathing number based on the calculated stress index and inducing the breath control of the user to reach the appropriate breathing number. The health condition diagnosis glove unit 100 may transmit at least one of bio-signal measurement data, heartbeat analysis data, stress index, appropriate respiratory rate, and respiratory control guidance information to the portable terminal 180.

The portable terminal 180 receives the bio-signal measurement data, the heart rate analysis data, the stress index, the appropriate breath rate, and the breath control guidance information transmitted by the glove unit for health condition diagnosis 100, And provide it to the user. Here, when only the bio-signal measurement data is received from the health condition diagnosis glove unit 100, the portable terminal 180 uses the received bio-signal measurement data to calculate heartbeat analysis data, a stress index, a proper respiratory rate, Guidance guide information, and display it on the screen.

In this case, the living body signal of the patient is measured using the glove unit 100 for a health condition diagnosis in a hospital, a public health center, a nurse's office, etc., and based on this, at least one of the patient's stress index, appropriate respiratory rate, And transmit it to the portable terminal 180 of the patient and display it on the screen.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is clearly understood that the same is by way of illustration and example only and is not to be taken by way of limitation, I will understand. Therefore, the scope of the present invention should not be limited to the above-described embodiments, but should be determined by equivalents to the appended claims, as well as the appended claims.

100: Health condition diagnosis glove unit 102: Glove
102-1: finger receiving unit 104: biological signal measuring device
106: physical condition analyzing device 111: housing part
113: light generating unit 113-1: first light generating unit
113-2: second light generating unit 115: light receiving unit
121: communication unit 123: heartbeat analysis unit
125: Stress Index Calculator 127: Respiration Control Manager
129: Display section 131:
141: purge section 143: purge rule section
145: Fuzzy reasoning part 147: Non-fuzzy part
180: portable terminal 200: health condition diagnosis system

Claims (9)

A glove having at least one finger receiving portion;
A biometric signal measuring device formed on the finger receiving part and measuring a user's biometric signal; And
The user's bio-signal measurement data is received from the bio-signal measurement device, and the received bio-signal measurement data is analyzed to generate heartbeat analysis data, and based on the heartbeat analysis data, And a body condition analyzing device for calculating a stress index of the subject,
The physical condition analyzing apparatus includes:
A communication unit for receiving the bio-signal measurement data of the user from the bio-signal measurement device;
A heartbeat analyzer for analyzing bio-signal measurement data received by the communication unit to generate heartbeat analysis data; And
And a stress index calculating unit for calculating the stress index of the user by substituting the heartbeat analysis data into fuzzy logic,
The stress index calculating unit may calculate,
A fuzzy logic unit for transforming the heartbeat analysis data into memberships for predetermined input membership functions;
A fuzzy rule unit for setting a membership between the input membership functions of the heartbeat analysis data and predetermined output membership functions as a fuzzy rule;
A fuzzy inference unit for applying a membership level of each input membership function of the heartbeat analysis data to each fuzzy rule to infer the membership of the output membership function for each fuzzy rule; And
And a defuzzification unit for defuzzifying the result of inference of the fuzzy inference unit to calculate the stress index of the user.
The method according to claim 1,
The glove,
Wherein the finger receiving portion in which the bio-signal measuring device is formed is made of a light shielding material.
delete delete The method according to claim 1,
Wherein the fuzzy-
Among the heartbeat analysis data, the high frequency heart data and the low frequency heart data are inputted to each of the input membership functions having very small (Small), small (small), midium, large, and very large A glove unit for the diagnosis of health condition, which converts the road belonging to the group.
6. The method of claim 5,
The fuzzy rule unit includes:
The membership of each of the input membership functions of the high-frequency heart data and the low-frequency heart data, and the respective output values set by the stresses set as Stressed, Tense, Normal, Calm, and Relaxed A glove unit for health status diagnosis, in which the relationship between membership functions is set as a fuzzy rule.
The method according to claim 1,
The non-
In the output membership function deduced to belong to each fuzzy rule, an area less than the inferred value of the membership of the output membership function of the fuzzy rule is obtained, the area obtained for each fuzzy rule is overlapped, and the center of gravity of the overlapping area And calculating the stress index of the user.
The method according to claim 1,
The physical condition analyzing apparatus includes:
Further comprising a respiration control manager configured to determine an appropriate respiratory rate of the user based on the calculated stress index of the user and to generate respiration control induction guidance information such that the respiration of the user reaches the appropriate respiratory rate, Diagnostic armored unit.
8. A glove unit for diagnosing a health condition according to any one of claims 1, 2, and 5 to 8; And
And a portable terminal for receiving at least one of the user's bio-signal measurement data, heartbeat analysis data, stress index, appropriate respiratory rate, and respiratory control induction guidance information from the health status diagnosis glove unit and displaying the received information on the screen. Status diagnostic system.

Images