CN215078452U

CN215078452U - Health monitoring device applying machine learning

Info

Publication number: CN215078452U
Application number: CN202121151254.5U
Authority: CN
Inventors: 杨勇; 黄淑英
Original assignee: Individual
Current assignee: Individual
Priority date: 2021-05-26
Filing date: 2021-05-26
Publication date: 2021-12-10
Anticipated expiration: 2031-05-26

Abstract

The utility model discloses an use health monitoring device of machine learning of monitoring devices technical field, including portable base, rack, examine test table, buffer unit, pick-up plate, rhythm of the heart sensor, blood pressure sensor, blood oxygen sensor, machine learning module, computer, examine test table by its upset along the articulated department with the rack of drive unit drive, make and examine test table upset to vertical state down, seted up the accommodation space that supplies to examine test table upset ability free pass through in the rack. Through setting up buffer unit, and then can prevent to lead to the fact the influence to the sensor to the pick-up plate overdraft, examine test table upset through setting up the drive unit drive, make and examine test table upset to the vertical state to accomodate in the accommodation space of rack, and then can reduce foreign matter or dust to at least a certain extent and adhere to on the pick-up plate surface and influence the detection, can prevent simultaneously that personnel from missing the collision and lead to the pick-up plate to receive the damage.

Description

Health monitoring device applying machine learning

Technical Field

The utility model relates to a monitoring devices technical field specifically is a health monitoring device who uses machine learning.

Background

With the improvement of living standard, people pay more and more attention to their health. Currently, there are three main forms of routine physical examination: one is that the user places a physical examination instrument by himself, for example, the physical examination instrument may include a sphygmomanometer, a blood glucose meter, etc.; secondly, the user goes to a professional physical examination organization for physical examination; thirdly, the user goes to a regular hospital to perform physical examination.

Through the retrieval, chinese patent No. CN210467338U discloses a health monitoring all-in-one, and this health monitoring all-in-one includes base, health measurement platform and display screen, and health measurement platform is located the base top, and health measurement platform has the measurement mesa, and the display screen is located health measurement bench top, and health monitoring all-in-one still includes: the health sensor is arranged on the measuring table top and used for monitoring body index parameters of the user; the communication unit is used for communicating the server with the health monitoring all-in-one machine; and the control unit is respectively connected with the at least one health sensor and the communication unit and is used for controlling the communication unit to send the body index parameters of the user to the server and controlling the display screen to display the two-dimensional code which is sent by the server and is associated with the body index parameters of the user.

In the above-mentioned health monitoring device in the prior art, when in use, a person to be measured presses a measuring table (referred to as a detection plate in this application) with a hand, and then a plurality of sensors collect health information of the person to be measured, but because the information is collected by adopting a pressing method, when the degree of pressing is too large, the sensor may be damaged and affected, and in addition, when not in use, dust is easily attached to the surface of the measuring table or other foreign matters are attached to the surface of the measuring table, so that the use is affected.

Based on this, the utility model designs an use health monitoring device of machine learning to solve above-mentioned problem.

SUMMERY OF THE UTILITY MODEL

An object of the utility model is to provide a health monitoring device who uses machine learning, with the health monitoring equipment among the prior art who proposes in solving above-mentioned background, when using, the person of being examined presses down measurement mesa (this application is called the pick-up plate) with the hand, and then comes to gather person's health information by a plurality of sensors, but owing to adopt the mode of pressing down to carry out information acquisition, when according to the pressure gauge too big, probably produce damaged influence to the sensor, when not using in addition, measure the mesa surface and adhere to the dust easily or adhere to and influence the problem of using by other foreign matters.

In order to achieve the above object, the utility model provides a following technical scheme: a health monitoring device applying machine learning comprises a cabinet arranged on a movable base, wherein the upper end part of the cabinet is hinged with a detection table, a detection plate is arranged on the detection table through a buffer unit, a heart rate sensor, a blood pressure sensor, a blood oxygen sensor and a machine learning module are integrated on the detection plate, the heart rate sensor, the blood pressure sensor and the blood oxygen sensor are respectively used for detecting and collecting heart rate, blood pressure and blood oxygen data of a detected person, the machine learning module is used for collecting the heart rate, blood pressure and blood oxygen data and forming a database, a computer is arranged on the cabinet and is in communication connection with the machine learning module through a cable or a wireless transmission module, the machine learning module feeds the database back to the computer, the computer is used for displaying and analyzing, and the detection table is driven by a driving unit to turn over along the hinged part of the cabinet, the detection platform is turned downwards to be vertical, and a containing space for the detection platform to freely pass through when the detection platform is turned is formed in the cabinet.

Preferably, the buffer unit comprises a plurality of sliding rods vertically and fixedly connected to the bottom of the detection plate, the sliding rods penetrate out of the bottom of the detection table and can freely slide up and down, limit nuts are sleeved at one ends of the sliding rods penetrating out of the detection table, a clearance groove for the detection plate to freely pass through when moving up and down is formed in the top surface of the detection table, an elastic piece is arranged in the detection table and the clearance groove, and the elastic piece elastically abuts against the detection plate and drives the detection plate to move upwards.

Preferably, the elastic part is a buffer spring sleeved on the sliding rod, and two ends of the buffer spring in the elastic direction elastically abut against the detection plate and the inner bottom wall of the clearance groove respectively.

Preferably, the drive unit includes the activity free bearing who articulates in the rack through the installation pivot, install electric push rod on the activity free bearing, the last drive of electric push rod is connected with the connector, the connector is articulated with detecting the platform bottom surface.

Preferably, adjusting bolt is threaded through by vertical threads on the movable base, and a pressing block is fixedly connected to the lower end of the adjusting bolt.

Compared with the prior art, the beneficial effects of the utility model are that: through setting up buffer unit, and then can prevent to lead to the fact the influence to the sensor to the pick-up plate overdraft, examine test table upset through setting up the drive unit drive, make and examine test table upset to the vertical state to accomodate in the accommodation space of rack, and then can reduce foreign matter or dust to at least a certain extent and adhere to on the pick-up plate surface and influence the detection, can prevent simultaneously that personnel from missing the collision and lead to the pick-up plate to receive the damage.

Drawings

In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.

Fig. 1 is a schematic structural diagram of a health monitoring device using machine learning according to the present invention;

FIG. 2 is an enlarged view of a portion of the structure at A in FIG. 1;

fig. 3 is the frame structure schematic diagram of the connection between the heart rate sensor, the blood pressure sensor, the blood oxygen sensor, the machine learning module and the computer.

In the drawings, the components represented by the respective reference numerals are listed below:

1-cabinet, 2-computer, 3-detection board, 4-detection board, 5-clearance groove, 6-connector, 7-electric push rod, 8-movable hinged support, 9-containing space, 10-movable base, 11-adjusting bolt, 12-press block, 13-heart rate sensor, 14-blood pressure sensor, 15-blood oxygen sensor, 16-machine learning module, 17-buffer spring, 18-sliding rod and 19-limit nut.

Detailed Description

The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by a person of ordinary skill in the art without creative efforts belong to the protection scope of the present invention.

Referring to fig. 1-3, the present invention provides a technical solution: a health monitoring device applying machine learning comprises a movable base 10 provided with a plurality of rollers at the bottom, a cabinet 1 is arranged on the movable base 10, an adjusting bolt 11 penetrates through the vertical thread on the movable base 10, the lower end of the adjusting bolt 11 is fixedly connected with a pressing block 12, after the cabinet 1 is moved in place, the adjusting bolt 11 is rotated, the adjusting bolt 11 is screwed on the movable base 10 to drive the pressing block 12 to move downwards, so that the pressing block 12 is abutted against the placing surface of the rollers on the movable base 10, the movable base 10 is prevented from moving automatically, the upper end part of the cabinet 1 is hinged with a detection table 4, a detection plate 3 is arranged on the detection table 4 through a buffer unit, the buffer unit comprises a plurality of sliding rods 18 vertically fixedly connected to the bottom of the detection plate 3, the sliding rods 18 penetrate through the bottom of the detection table 4 and can freely slide up and down, one end of the sliding rods, which penetrates through the detection table 4, is sleeved with a limit nut 19, the top surface of the detection table 4 is provided with a clearance groove 5 through which the detection plate 3 can freely pass by moving up and down, the detection table 4 and the clearance groove 5 are internally provided with a buffer spring 17, the buffer spring 17 is sleeved on a sliding rod 18, two ends of the buffer spring in the elastic direction respectively elastically abut against the detection plate 3 and the inner bottom wall of the clearance groove 5, the buffer spring 17 elastically abuts against the detection plate 3 and drives the detection plate 3 to move upwards, the detection plate 3 is integrated with a heart rate sensor 13, a blood pressure sensor 14, a blood oxygen sensor 15 and a machine learning module 16 (the structure and the working principle of the machine learning module refer to a real-time health monitoring system based on machine learning of China patent No. CN 201720285162.3), the heart rate sensor 13, the blood pressure sensor 14 and the blood oxygen sensor 15 are respectively used for detecting and collecting the heart rate, the blood pressure and the blood oxygen data of a person to be detected, the machine learning module 16 is used for collecting the heart rate, the blood pressure and the blood oxygen data and forming a database, a computer 2 is arranged on a machine cabinet 1, the computer 2 is in communication connection with a machine learning module 16 through a cable or a wireless transmission module, the machine learning module 16 feeds a database back to the computer 2, the computer 2 performs display analysis, a palm of a tested person is attached to the surface of a detection plate 3 when the tested person performs detection, health information of the tested person is collected by each sensor, when the pressing degree of the detection plate 3 is overlarge, the detection plate 3 is buffered by a buffer spring 17, so that each sensor on the detection plate 3 is prevented from being damaged, a detection table 4 is driven by a driving unit to turn over along a hinge joint part with the machine cabinet 1, so that the detection table 4 turns downwards to be vertical, a containing space 9 for the detection table 4 to freely pass through is arranged in the machine cabinet 1, the driving unit comprises a movable hinge seat 8 hinged in the machine cabinet 1 through an installation pivot, and an electric push rod 7 is arranged on the movable hinge seat 8, the electric push rod 7 is connected with the connector 6 in a driving mode, the connector 6 is hinged to the bottom face of the detection table 4, when the detection plate 3 is stored, the electric push rod 7 is started to drive the connector 6 to move downwards, the detection table 4 is driven to overturn downwards along the hinged position of the cabinet 1, so that the detection table 4 is overturned into the storage space, the detection plate 3 is prevented from being damaged due to artificial collision, and dust or medical matters can be reduced to at least a certain degree and attached to the detection plate 3.

In the description herein, references to the description of "one embodiment," "an example," "a specific example," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and its practical applications, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. The health monitoring device applying machine learning is characterized by comprising a cabinet (1) arranged on a movable base (10), wherein the upper end of the cabinet (1) is hinged with a detection table (4), the detection table (4) is provided with a detection plate (3) through a buffer unit, the detection plate (3) is integrated with a heart rate sensor (13), a blood pressure sensor (14), a blood oxygen sensor (15) and a machine learning module (16), the heart rate sensor (13), the blood pressure sensor (14) and the blood oxygen sensor (15) are respectively used for detecting and collecting the heart rate, the blood pressure and the blood oxygen data of a testee, the machine learning module (16) is used for collecting the heart rate, the blood pressure and the blood oxygen data and forming a database, the cabinet (1) is provided with a computer (2), and the computer (2) is in communication connection with the machine learning module (16) through a cable or a wireless transmission module, the machine learning module (16) feeds back a database to the computer (2), the computer (2) displays and analyzes, the detection table (4) is driven by the driving unit to turn over along the hinged part of the cabinet (1), so that the detection table (4) turns over downwards to be vertical, and a containing space (9) for the detection table (4) to freely pass through in turning over is arranged in the cabinet (1).

2. The health monitoring device applying machine learning as claimed in claim 1, wherein the buffer unit comprises a plurality of sliding rods (18) vertically and fixedly connected to the bottom of the detection plate (3), the sliding rods (18) penetrate through the bottom of the detection table (4) and can freely slide up and down, one end of the sliding rods penetrating through the detection table (4) is sleeved with a limit nut (19), the top surface of the detection table (4) is provided with a clearance groove (5) through which the detection plate (3) can freely move up and down, the detection table (4) and the clearance groove (5) are internally provided with elastic members, and the elastic members elastically support against the detection plate (3) and drive the detection plate (3) to move upwards.

3. The health monitoring device applying machine learning as claimed in claim 2, wherein the elastic member is a buffer spring (17) sleeved on the sliding rod (18), and two ends of the buffer spring (17) in the elastic direction elastically abut against the inner bottom wall of the detection plate (3) and the clearance groove (5) respectively.

4. The health monitoring device applying machine learning as claimed in claim 1, wherein the driving unit comprises a movable hinged support (8) hinged in the cabinet (1) through a mounting pivot, an electric push rod (7) is mounted on the movable hinged support (8), a connector (6) is connected to the electric push rod (7) in a driving manner, and the connector (6) is hinged to the bottom surface of the detection table (4).

5. The health monitoring device for machine learning of claim 1, wherein the movable base (10) is provided with an adjusting bolt (11) through vertical threads, and a pressing block (12) is fixedly connected to the lower end of the adjusting bolt (11).