KR102427263B1 - Customized physical therapy device using big data and the control method thereof - Google Patents

Abstract

A customized physical therapy apparatus using big data and a method for controlling the same are provided. A customized physical therapy apparatus using big data according to an embodiment of the present invention is a health information input unit that receives a user's health information, and collects health state information using a plurality of sensors for measuring the user's health state. A state measurement unit, a user analysis unit that classifies the health type of the user by matching the health information received from the user and the health state collected from the plurality of sensors with pre-stored big data, and a physical health type corresponding to the user's health type It may include a control unit that sets treatment conditions and controls to perform physical therapy.

Description

Customized physical therapy device using big data and its control method

The present invention relates to a customized physical therapy apparatus using big data and a method for controlling the same, and more particularly, to a customized physical therapy apparatus using big data for diagnosing a user's health type using big data and performing customized physical therapy, and It's about how to control it.

Physical therapy uses heat, ice, air, light, electricity, electromagnetic waves, ultrasound, mechanical force, gravity, etc. to relieve pain, promote tissue healing, and improve body movement. It refers to the procedure you want to obtain.

In order to receive physical therapy for pain in each part of the user's body, it is common to visit a hospital, a clinic, a treatment device store, etc. However, users have different age groups, genders, physical conditions, and the like, and it is very difficult to perform customized physical therapy according to their different conditions.

In the past, although it was provided to provide optimized physical therapy to each patient, such as a patient-customized complex physical therapy system (Korea Patent No. 10-2053448), the optimized physical therapy There was a problem in that the satisfaction of customized physical therapy was lowered because the treatment conditions could not be set automatically.

Therefore, there is a need for research on a customized physical therapy device that can accurately identify a patient's individual health condition and provide physical therapy that matches the patient's condition.

Korean Patent Registration No. 10-2053448

It is an object of the present invention to identify a user's health type in big data based on his/her health information input by the user and health status data measured at the site, so that physical therapy that matches the patient's health condition can be performed. To provide a customized physical therapy device using

In addition, when there are a plurality of health types in big data that are matched based on the patient's health information and health status data, the health type is selected using the confidence error value, using big data more suitable for the characteristics of the user. To provide customized physical therapy devices.

A customized physical therapy apparatus using big data according to an embodiment of the present invention is a health information input unit that receives a user's health information, and collects health state information using a plurality of sensors for measuring the user's health state. A state measurement unit, a user analysis unit that classifies the health type of the user by matching the health information received from the user and the health state collected from the plurality of sensors with pre-stored big data, and a physical health type corresponding to the user's health type It may include a control unit that sets treatment conditions and controls to perform physical therapy.

The health information input unit of the customized physical therapy device using big data according to an embodiment of the present invention, the user's height, weight, age, allergy history, exercise history within a preset period, drinking history within a preset period, family history, Receives health information including disease diagnosis history, pain region, and specific information, and the health state measurement unit uses a body temperature sensor, a pulse sensor, a blood pressure sensor, a body fat sensor, and a vision system to obtain the user's body temperature, pulse, and blood pressure Health status including , body fat, facial color, and skin condition can be measured.

The health information input unit of the customized physical therapy device using big data according to an embodiment of the present invention, the user analysis unit, compares the input health information and the measured health state with pre-stored big data to derive a health type , when matching with a plurality of health types among health types of big data stored in advance, a health type having a small confidence error value may be selected as the health type of the user.

The reliability error value of the customized physical therapy apparatus using big data according to an embodiment of the present invention is calculated according to the following [Equation 1] and [Equation 2], and the calculated reliability error value is low based on You can select one of a number of matching health types.

[Equation 1]

where V is the confidence error value, A _n is the measured value of the nth device, B _n is the standard value for the health type in big data corresponding to the measured value of the nth device, and R _n is the error rate of the nth device

[Equation 2]

Here, R _n is the error rate of the nth equipment, P _n is the number of measurements of the nth equipment, S _n is the number of measurement errors of the nth equipment, W _n is the weight for the nth equipment

In the customized physical therapy apparatus using big data according to an embodiment of the present invention, a health type in the pre-stored big data is assigned a disease-related score expressing association with a plurality of diseases, and among a plurality of matched health types, the A predetermined weight may be given when calculating a confidence error value for a health type having a high disease-related score for a disease in the diagnosis history input by the user.

According to the present invention, by identifying the user's health type in big data based on his/her health information input by the user and health status data measured on site, big data that can be used for physical therapy in accordance with the patient's health condition can be obtained. It is possible to provide a customized physical therapy device using the

In addition, when there are a plurality of health types in big data that are matched based on the patient's health information and health status data, the health type is selected using the confidence error value, using big data more suitable for the characteristics of the user. We can provide customized physical therapy devices.

1 is a block diagram illustrating a customized physical therapy apparatus using big data according to an embodiment of the present invention.
2 is a diagram illustrating a process of deriving a health type based on health information and health status and performing customized physical therapy in response thereto according to an embodiment of the present invention.

Hereinafter, specific embodiments of the present invention will be described in detail with reference to the drawings. However, the spirit of the present invention is not limited to the presented embodiments, and those skilled in the art who understand the spirit of the present invention may add, change, delete, etc. other elements within the scope of the same spirit, through addition, change, deletion, etc. Other embodiments included within the scope of the present invention may be easily proposed, but these will also be included within the scope of the present invention.

In addition, components having the same function within the scope of the same idea shown in the drawings of each embodiment will be described using the same reference numerals.

Hereinafter, an embodiment of the present invention will be described with reference to FIGS. 1 and 2 .

1 is a block diagram illustrating a customized physical therapy apparatus using big data according to an embodiment of the present invention, and FIG. 2 is a health type derived based on health information and health status according to an embodiment of the present invention, It is a diagram showing a process of performing customized physical therapy in response to this.

Referring to FIG. 1 , the customized physical therapy apparatus 100 using big data may include a health information input unit 110 , a health state measurement unit 120 , a user analysis unit 130 , and a control unit 140 .

The health information input unit 110 may receive health information of a user (patient). In this case, the health information of the user may be directly input from the user who has visited the site for physical therapy, or the health information may be collected by calling the information entered in the medical database and utilizing it.

In this case, the received health information may include the user's height, weight, age, allergy history, exercise history within a preset period, drinking history within a preset period, family history, disease diagnosis history, pain site and specific information. have.

For example, user 'Gildong Hong' is 180cm tall, weighs 75kg, 45 years old, has a crustacean allergy, exercised once a week for 1 hour for the past 3 months, drank 1 bottle of soju once a week for the past 3 months, has a family history of thyroid cancer from her parents, arrhythmia It is possible to receive basic user health information by inputting diagnosis history, back pain, hypertension, and the like.

The health state measurement unit 120 may collect health state information by using a plurality of sensors to measure the user's health state. That is, since the information input by the user may be different from the current state, when the user visits the site for physical therapy, the user's health state information may be collected using a plurality of sensors.

In this case, the health status information can measure the user's body temperature, pulse, blood pressure, body fat, facial color, and skin condition using a body temperature sensor, a pulse sensor, a blood pressure sensor, a body fat sensor, and a vision system. .

For example, the user's body temperature measured in the field may be measured as 36.7 ° C., the pulse rate is 65 beats/minute, the blood pressure is 80 to 120, the body fat is 25%, the face color is redness grade 2, and the skin condition is a lack of moisture.

The user analysis unit 130 may classify the health type of the user by matching the health information received from the user and the health status collected from the plurality of sensors with pre-stored big data.

That is, the health type of the user can be recommended by matching the health information and health type most similar to the user's health information in big data. It may be quantified, and a health type having the highest numerical value may be recommended as the health type of the user.

In this case, when a plurality of health types having the same numerical value exist, the confidence error value may be used to select and recommend one of the plurality of health types.

The reliability error value will be described in more detail below.

The confidence error value may be calculated according to [Equation 1] and [Equation 2], and one of a plurality of matched health types may be selected based on the calculated confidence error value being low.

[Equation 1]

Here, V is the confidence error value, A _n is the measured value of the nth device, B _n is the standard value for the health type in big data corresponding to the measured value of the nth device, and R _n is the error rate of the nth device .

For example, when two health types in big data are matched (ex. 40s back pain type, 40s obese patient type) based on the user's health information and health status, the confidence error value of each health type can be calculated. can

First, the confidence error value for the back pain type in the 40s is the measured value of each body temperature sensor, pulse sensor, blood pressure sensor, etc. that measured the above health status, and the body temperature, pulse, and blood pressure assigned as attribute values to the back pain type in the 40s. Based on the error value and the error rate of each sensor, the reliability error value of the type of back pain in the 40s can be calculated according to Equation 1 above.

Similarly, the confidence error value for the type of obese person in their 40s is the measured value of each body temperature sensor, pulse sensor, blood pressure sensor, etc. that measured the above health status, and the error of body temperature, pulse, and blood pressure assigned as attribute values to the type of obese person in their 40s. Based on the value and the error rate of each sensor, the reliability error value of the type of obese person in their 40s can be calculated according to Equation 1 above.

Accordingly, a type having a smaller confidence error value of each health type may be finally determined as the user's health type, and the physical therapy apparatus may be controlled to provide optimal physical therapy conditions corresponding to the finally determined health type. .

On the other hand, the above-mentioned error rate R _n of the n-th equipment may be calculated according to the following [Equation 2].

[Equation 2]

Here, R _n is the error rate of the n-th equipment, P _n is the number of measurements of the n-th equipment, S _n is the number of measurement errors of the n-th equipment, and W _n is the weight for the n-th equipment.

In more detail, the first device (sensor), the body temperature sensor, shows 50 measurement errors when operating 1000 times. The error rate of the body temperature sensor for determining the reliability error value of the type of back pain in the 40s is 7.5%. Also, if the blood pressure sensor, which is the second device, shows a measurement error of 30 times when operated 1000 times, the error rate for the second device is 3%.

If the error rate for the type of obese person in their 40s is calculated in this way, the body temperature sensor, the first device (sensor), shows 50 measurement errors when operating 1000 times. If all of them are 1 and the weight of the blood pressure sensor is 1.5, the error rate of the body temperature sensor for determining the reliability error value of the type of obese person in their 40s is 5%. In addition, since the blood pressure sensor, which is the second device, shows a measurement error of 30 times when operated 1000 times, the error rate for the second device is 3%*1.5=4.5%.

Therefore, since the confidence error value is calculated by the above method and the health type having a small value is the user's final health type, the physical therapy apparatus of the present invention can be controlled to set the optimal physical therapy condition corresponding thereto.

Accordingly, the controller 140 may control to set physical therapy conditions and perform physical therapy in response to the user's health type.

Meanwhile, in the method of controlling a physical therapy apparatus using big data of the present invention, the process may be performed as in the embodiment of FIG. 3 .

Referring to FIG. 3 , in step S110 , the user's health information may be input. In this case, the device for input may be one of a terminal provided in the field, a user's mobile device, and a web system, and may collect the user's basic government from a medical database of an external server.

In step S120, the user's health condition may be measured through a sensor provided in the field, and in step S130, the health type of the user defined in the big data may be classified based on the health information and health condition.

In step S140 , when there are multiple health types matched to the user, a confidence error value may be calculated, and a health type having a small confidence error value may be selected as the final health type.

In the subsequent step (S150), physical therapy may be performed by setting physical therapy conditions corresponding to the determined health type.

As described above, according to an embodiment of the present invention, by identifying a user's health type in big data based on his/her health information input by the user and health condition data measured at the field, a physical fitness corresponding to the patient's health condition Treatment may be made.

In addition, when there are a plurality of health types in big data that are matched based on the patient's health information and health status data, the health type is selected using the confidence error value, using big data more suitable for the characteristics of the user. Personalized physical therapy can be provided.

In the above, the configuration and features of the present invention have been described based on the embodiments according to the present invention, but the present invention is not limited thereto, and it is understood that various changes or modifications can be made within the spirit and scope of the present invention. It is intended that such changes or modifications will be apparent to those skilled in the art, and therefore fall within the scope of the appended claims.

110: health information input unit
120: health status measurement unit
130: user analysis unit
140: control unit

Claims (5)

Health information input unit for receiving the user's health information;
a health state measurement unit for collecting health state information by using a plurality of sensors to measure the health state of the user;
a user analysis unit for classifying the health type of the user by matching the health information received from the user and the health status collected from the plurality of sensors with pre-stored big data; and
A control unit for setting physical therapy conditions in response to the user's health type and controlling to perform physical therapy,
The health information input unit,
Health information including the user's height, weight, age, allergy history, exercise history within a preset period, drinking history within a preset period, family history, disease diagnosis history, pain area and specific information is received,
The health status measurement unit,
Measures the user's health status including body temperature, pulse, blood pressure, body fat, face color, and skin condition using a body temperature sensor, pulse sensor, blood pressure sensor, body fat sensor, and vision system,
The user analysis unit,
A health type is derived by comparing the input health information and the measured health status with pre-stored big data, but when matching with multiple health types among health types of pre-stored big data, a health type with a small confidence error value is selected. Select the user's health type,
The confidence error value is
It is calculated according to the following [Equation 1] and [Equation 2], and selects one of a plurality of health types matched based on the calculated reliability error value being low,
[Equation 1]

where V is the confidence error value, A _n is the measured value of the nth device, B _n is the standard value for the health type in big data corresponding to the measured value of the nth device, R _n is the error rate of the nth device, ,
[Equation 2]

Here, R _n is the error rate of the nth equipment, P _n is the number of measurements of the nth equipment, S _n is the number of measurement errors of the nth equipment, W _n is the weight for the nth equipment,
The health type in the pre-stored big data is assigned a disease-related score expressing association with a number of diseases,
A customized physical therapy apparatus using big data, characterized in that a predetermined weight is given when calculating a confidence error value for a health type having a high disease-related score for a disease in the diagnosis history input by the user among a plurality of matched health types . delete delete delete delete

Images