KR20210055189A - Joint torque measuring system using multi-channel electromyographic signal and method thereof - Google Patents

Abstract

Provided are a joint torque measuring system using a multi-channel EMG signal and a method thereof. The system comprises: an EMG measuring device for obtaining EMG measurement data from a user using at least one channel, and calculating joint predicted torque data for the EMG measurement data in real time based on EMG standard data; and a management server for generating the EMG standard data by learning the EMG measurement data and the joint predicted torque data corresponding to the EMG measurement data.

Description

Joint torque measurement system and method using multi-channel EMG signals {JOINT TORQUE MEASURING SYSTEM USING MULTI-CHANNEL ELECTROMYOGRAPHIC SIGNAL AND METHOD THEREOF}

The present invention relates to a joint torque measurement system and method using a multi-channel EMG signal capable of predicting a user's joint torque from EMG measurement data acquired from a user by learning EMG standard data.

In general, robots are used in various fields such as home, military, and industrial robots, and in particular, the demand for muscle strength robots is gradually increasing.

The above-described muscle strength robot is a wearable robot, which is operated while being worn on the user's body, and amplifies the physical force of the wearer by operating an actuator linked to the frame based on the user's motion intention.

These wearable robots are used in steel or heavy industries to handle high loads that are difficult to handle with human force, or to handle irregular repetitive tasks that are difficult to perform with programmed machine motions.

In addition, it prevents the decrease in work productivity due to the decrease in production manpower caused by the 3D avoidance phenomenon, and also protects workers from dangerous work environments.

The user can provide the required torque by using a biometric signal that can be measured when a specific muscle contracts, such as an electromyogram (EMG) signal.

However, since the user's muscle fatigue cannot be objectified, the torque value for the EMG signal may not be accurate due to noise.

Accordingly, it may not be possible to provide the proper torque required by the user according to the user's muscle condition.

[Patent Document 1] Korean Patent Publication 10-2015-0131554 (2015.11.25)

The problem to be solved by the present invention is to provide a joint torque measurement system and method using a multi-channel EMG signal capable of predicting joint torque from EMG measurement data acquired from a user by learning EMG standard data.

The problem to be solved by the present invention is to provide a joint torque measurement system and method using a multi-channel EMG signal capable of predicting an accurate joint torque in consideration of the surface EMG included in the EMG measurement data.

The problem to be solved by the present invention is to provide a joint torque measurement system and method using a multi-channel EMG signal capable of predicting joint torque from EMG measurement data by generating and learning EMG standard data using deep learning. .

The problem to be solved by the present invention is to provide a system and method for measuring joint torque using a multi-channel EMG signal using a separate smart device capable of communicating with an EMG measuring device.

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.

A joint torque measurement system using a multi-channel EMG signal according to an embodiment of the present invention for solving the above-described problem acquires EMG measurement data from a user using at least one or more channels, and based on EMG standard data An EMG measuring device for calculating joint predicted torque data for the EMG measurement data in real time; And a management server for generating the EMG standard data by learning the EMG measurement data and the joint predicted torque data corresponding to the EMG measurement data. It may include.

In one embodiment of the present invention, the EMG measuring apparatus includes: an EMG measuring unit that directly contacts a user's body to obtain the EMG measurement data from the user; And a joint torque calculator configured to calculate the estimated joint torque data in real time by analyzing the EMG measurement data by simultaneously considering the time domain and the frequency domain based on the EMG standard data. It may include including.

In one embodiment of the present invention, the joint torque calculation unit comprises: data separation means for separating the EMG measurement data into the time domain and the frequency domain; Data analysis means for analyzing the EMG measurement data divided into the time domain and the frequency domain; And data calculating means for calculating the joint predicted torque data for the analyzed EMG data in real time. It may include.

In an embodiment of the present invention, the data analysis means analyzes the EMG measurement data to generate RMS (Root Mean Square) data for the time domain, and converts the time domain signal to the frequency domain signal. By transforming, Fast Fourier Transform (FFT) data may be generated.

In an embodiment of the present invention, the data calculation means may learn based on the EMG standard data and calculate the joint predicted torque data for the EMG measurement data in real time from the Fast Fourier Transform (FFT) data. have.

In an embodiment of the present invention, the EMG measurement data may include a surface EMG signal generated by multiple channels.

In an embodiment of the present invention, the joint torque calculation unit may calculate the joint estimated torque data in real time by considering the surface EMG signal by simultaneously considering the time domain and the frequency domain.

In an embodiment of the present invention, the management server receives joint measurement torque data for the EMG measurement data obtained from the EMG measurement device, learns the joint measurement torque data, and is numerically optimized. A management control unit for generating a; It may include.

In an embodiment of the present invention, the management control unit may calculate the joint predicted torque data by using the EMG measurement data obtained from the EMG measurement device based on the EMG standard data.

In an embodiment of the present invention, the management server may update the EMG standard data in real time in response to the EMG measurement data and the joint predicted torque data.

In one embodiment of the present invention, when receiving the EMG measurement data from the EMG measurement device and receiving the EMG standard data from the management server, the EMG standard data is learned and the EMG measurement data is used. A user terminal for calculating joint predicted torque data; It may further include.

In addition, a joint torque measurement method using a multi-channel EMG signal according to another embodiment of the present invention for solving the above-described problem includes: calculating, by an EMG measurement device, EMG measurement data; Calculating, in real time, joint predicted torque data from the EMG measurement data by learning, by the EMG standard data, by the EMG measurement device; And receiving, by the management server, the joint predicted torque data and updating it in real time. Including, wherein the step of calculating the joint predicted torque data, the EMG measuring device analyzes the EMG measurement data in consideration of a time domain and a frequency domain simultaneously based on the EMG standard data to calculate the joint predicted torque data. It can be calculated in real time.

In an embodiment of the present invention, the calculating of the joint estimated torque data may include: separating, by the EMG measurement device, the EMG measurement data into the time domain and the frequency domain; Analyzing, by the EMG measuring device, the EMG measurement data separated into the time domain and the frequency domain; And calculating, in real time, the joint predicted torque data for the analyzed EMG measurement data by the EMG measurement device. It may include.

In an embodiment of the present invention, the analyzing of the EMG data includes: analyzing the EMG data to generate RMS (Root Mean Square) data for the time domain; And converting the time domain signal into the frequency domain signal to generate Fast Fourier Transform (FFT) data. It may include.

In an embodiment of the present invention, the calculating of the estimated joint torque data includes the EMG measuring device considering the surface EMG signal included in the EMG measurement data by simultaneously considering the time domain and the frequency domain. Joint estimated torque data can be calculated in real time.

In an embodiment of the present invention, the management server receives joint measurement torque data for the EMG measurement data obtained from the EMG measurement device, learns the joint measurement torque data, and is numerically optimized. Can be created.

In an embodiment of the present invention, when the management server receives the EMG measurement data from the EMG measurement device, the step of learning the EMG standard data and calculating the joint predicted torque data using the EMG measurement data ; It may further include.

In an embodiment of the present invention, when a user terminal receives the EMG data from the EMG measurement device and receives the EMG standard data from the management server, it learns the EMG standard data to obtain the EMG measurement data. Calculating the joint estimated torque data by using; It may further include.

In one embodiment of the present invention, the step of updating the EMG standard data in real time in response to the EMG measurement data and the joint predicted torque data; It may include.

A program according to an embodiment of the present invention is combined with a computer that is hardware, and is stored in a computer-readable recording medium to perform a joint torque measurement method using the multi-channel EMG signal.

Other specific details of the present invention are included in the detailed description and drawings.

According to the present invention, by learning EMG standard data and predicting and providing a customized joint torque according to a user's EMG signal, it is possible to give a user a sense of reliability.

According to the present invention, by predicting and providing a customized joint torque to the user, it is possible to improve the user's ability to perform tasks.

According to the present invention, by predicting and providing a customized joint torque to a user, it is possible to prevent a safety accident by preventing unnecessary muscle power consumption by the user during work.

According to the present invention, accurate results can be obtained at low cost by predicting and providing accurate joint torque using only the user's EMG signal based on the learned standard data without using a sensor for recognizing a separate bio-signal. have.

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.

1 is a conceptual diagram illustrating a joint torque measurement system using a multi-channel EMG signal according to an embodiment of the present invention.
FIG. 2 is a diagram illustrating a detailed configuration of a joint torque measurement system using a multi-channel EMG signal shown in FIG. 1.
3 is a view for explaining a joint torque measurement method using a multi-channel EMG signal according to an embodiment of the present invention.
4 is a diagram for explaining the EMG measurement data shown in FIG. 3.
5 is a view for explaining joint measurement torque data and joint predicted torque data according to an embodiment of the present invention.
6 is a detailed flowchart for explaining the step of calculating the estimated joint torque data shown in FIG. 3.
7 is a diagram for explaining a step of analyzing the EMG data shown in FIG. 6.
8 is a view for explaining a joint torque measurement method using a multi-channel EMG signal according to another embodiment of the present invention.
9 is a view for explaining a joint torque measurement system using a multi-channel EMG signal according to another embodiment of the present invention.
10 is a view for explaining a joint torque measurement method using a multi-channel EMG signal according to another embodiment of the present invention.

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. It is provided to fully inform the skilled person of the scope of the present invention, and the present invention is only defined by the scope of the claims.

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 herein, “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.

Unless otherwise defined, all terms (including technical and scientific terms) used in the present specification may be used with 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.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

1 is a conceptual diagram illustrating a joint torque measurement system using a multi-channel EMG signal according to an embodiment of the present invention, and FIG. 2 is a detailed configuration of a joint torque measurement system using a multi-channel EMG signal shown in FIG. 1. It is a drawing for explanation.

1 and 2, a joint torque measurement system 1 using a multi-channel EMG signal according to an embodiment of the present invention may include an EMG measuring device 10 and a management server 20.

Here, the EMG measuring device 10 and the management server 20 are synchronized in real time using a wireless communication network to transmit and receive data. The wireless communication network can support various telecommunication methods, for example, Wireless LAN (WLAN), Digital Living Network Alliance (DLNA), Wireless Broadband: Wibro, and Wimax (World Interoperability for Microwave Access: Wimax). ), Global System for Mobile communication (GSM), Code Division Multi Access (CDMA), Code Division Multi Access 2000 (CDMA2000), Enhanced Voice-Data Optimized or Enhanced Voice-Data Only (EV-DO), Wideband CDMA (WCDMA) , High Speed Downlink Packet Access (HSDPA), High Speed Uplink Packet Access (HSUPA), IEEE 802.16, Long Term Evolution (LTE), Long Term Evolution-Advanced (LTEA), Broadband Wireless Mobile Communication Service (Wireless Mobile) Broadband Service: Various communication methods such as WMBS), BLE (Bluetooth Low Energy), Zigbee, RF (Radio Frequency), LoRa (Long Range), etc. may be applied, but are not limited thereto. The method may be applied. Alternatively, the EMG measuring apparatus 10 and the management server 20 may transmit and receive data through a wired communication method.

The EMG measuring device 10 is a portable EMG measuring device 10, which can be operated using an application program or application in the present disclosure, and such an application program is an external server or an external server through wireless communication. It can be downloaded from the management server 20. The EMG measuring apparatus 10 may be formed of an electronic device that is attached to a part of the user's body using a plurality of channels to obtain an electrode waveform capable of grasping the user's muscle mass. The electronic device is not limited thereto, and the electronic device is a wearable device capable of acquiring an electrode waveform from a body, for example, a body-worn type (eg, a head-worn device, a bracelet, an anklet, a necklace, a ring, a watch, etc.), It may be at least one of an integrated clothing type, a body attachment type, or a living body implant type.

As shown in FIG. 2, the EMG measuring apparatus 10 may include an EMG measuring unit 12 and a joint torque calculating unit 14.

The EMG measuring unit 12 is a stimulation means that directly contacts the user's body, and may be a device configured with eight channels in the present embodiment, but is not limited thereto. For example, the EMG measurement unit 12 may be configured with at least three channels.

The EMG measurement unit 12 may contact the user's skin under the control of the control module 148 of the joint torque calculation unit 14 to provide electric stimulation to obtain EMG measurement data for the electric stimulation from the user. The attachment position of the EMG measurement unit 12 is described as both arms in the present embodiment, but the present invention is not limited thereto, and may be any part of the body as long as it is a position capable of acquiring data reacted by electrical stimulation.

In contrast, the EMG measurement unit 12 is a proximity sensor (not shown) that is included on the inner surface of the EMG measurement unit 12 in contact with the user's skin and detects a change in the distance between the user's skin and the EMG measurement unit 12 ) Can be included. For example, the proximity sensor detects between the user's skin and the EMG measurement unit 12 when the EMG measurement unit 12 is driven to check whether the patch of the EMG measurement unit 12 is well attached to the user’s skin. Electricity generated by the electromyography unit 12 may be energized to accurately obtain an electrical signal of the heart in response to the user's heartbeat.

In addition, the EMG measurement unit 12 is a temperature sensor (not shown) included on the inner surface of the EMG measurement unit 12 in contact with the user's skin to detect a temperature change between the user's skin and the EMG measurement unit 12 It may include. For example, by detecting an increase in temperature between the patch and the skin of the EMG measurement unit 12, it is possible to prevent damage to the user’s skin due to an excessive increase in temperature. Through this, the user can use the EMG measurement device 10 Can be used safely.

The joint torque calculation unit 14 may include a communication module 140, a display module 142, a storage module 144, a power module 146, and a control module 148.

The communication module 140 may receive EMG standard data from the management server 20 and transmit the estimated joint torque data to the management server 20.

According to an embodiment, the communication module 140 may receive joint predicted torque data from the management server 20 when transmitting the EMG measurement data to the management server 20.

Here, the EMG measurement data may be data obtained through the EMG measurement unit 12. The joint predicted torque data may be information for predicting joint torque using EMG measurement data based on EMG standard data. The EMG standard data may be numerically optimized information by learning joint measurement torque data, which is an actual measurement value for the EMG measurement data. Here, the EMG standard data may be updated in real time in response to the joint measurement torque data and the joint predicted torque data.

The display module 142 is a means for visually and aurally displaying the current operating state of the EMG measuring device 10, and is capable of displaying symbols, letters, numbers, etc. on the screen according to the operating state. It may include a lamp that outputs or a speaker that outputs audio.

The storage module 144 may store data transmitted and received through the communication module 140 and data supporting various functions of the EMG measuring apparatus 10.

The storage module 144 may store a plurality of application programs or applications driven by the EMG measurement apparatus 10, data for the operation of the EMG measurement apparatus 10, and commands. At least some of these application programs may be downloaded from an external server through wireless communication.

The power module 146 may receive external power and internal power under the control of the control module 148 to supply power to each of the components included in the EMG measuring apparatus 10. The power module 146 includes a battery (not shown), and the remaining amount of the battery can be visually checked. The battery may be charged by connecting a 220V commercial power source or USB to a laptop or computer. In addition, the battery part is a cell phone battery, and it can be charged with a cell phone battery charger by using a 3.7V lithium-ion battery, which is the most economical and efficient secondary battery. Alternatively, the battery may be a built-in battery or a replaceable battery.

When receiving the EMG data from the EMG measurement unit 12 by operating the EMG measurement unit 12 by the user's manual operation, the control module 148 learns the EMG standard data received from the management server 20 Joint estimated torque data can be calculated and output.

Specifically, the control module 148 may analyze the EMG measurement data using the EMG standard data and calculate the joint predicted torque data in real time in consideration of the surface EMG.

The control module 148 may include a data separation unit 1480, a data analysis unit 1482, and a data calculation unit 1484.

The data separation means 1480 may separate the EMG measurement data into a time domain and a frequency domain. In this case, the EMG measurement data may include a surface EMG signal generated by multiple channels.

The data analysis means 1482 may analyze EMG measurement data divided into a time domain and a frequency domain.

Specifically, the data analysis means 1482 analyzes the EMG measurement data to generate RMS (Root Mean Square) data for the time domain, converts the time domain signal into a frequency domain signal, and converts the FFT (Fast Fourier Transform) data. Can be created.

The data calculation means 1484 may calculate in real time joint predictive torque data from which the surface EMG signal is excluded from the analyzed EMG measurement data.

Specifically, the data calculation means 1484 may learn based on the EMG standard data and calculate joint predicted torque data for EMG measurement data in real time from Fast Fourier Transform (FFT) data.

In this case, the control module 148 may visually and/or audibly display the EMG result data to the user through the display module 122.

According to an embodiment, the control module 148 may transmit EMG measurement data to the management server 20 and receive joint predicted torque data from the management server 20.

The EMG measuring apparatus 10 having such a structure learns the EMG standard data generated using deep learning, so that the surface EMG signal is simultaneously considered for the EMG measurement data based on the EMG standard data. Considering this, it is possible to calculate the precise joint estimated torque data in real time. At this time, the joint predicted torque data is provided by predicting and providing a customized joint torque according to the user's EMG measurement data, thereby preventing safety accidents as well as a sense of trust for the user.

The management server 20 may include a communication unit 22, a database unit 24, a monitoring unit 26, and a management control unit 28.

The communication unit 22 may transmit standard EMG data to the EMG measurement apparatus 10 and receive joint predicted torque data from the EMG measurement apparatus 10.

According to an embodiment, when receiving the EMG measurement data from the EMG measurement apparatus 10, the communication unit 22 may transmit the estimated joint torque data to the EMG measurement apparatus 10.

According to an embodiment, the communication unit 22 may receive joint measurement torque data, which is an actual measurement value for the EMG measurement data, from the EMG measurement device 10.

The database unit 24 may store data transmitted/received to and from the EMG measuring apparatus 10 through a wireless communication network. In this case, the EMG standard data may be updated and stored in real time in response to joint measurement torque data or joint predicted torque data.

The database unit 24 may store data supporting various functions of the management server 20. The database unit 24 may store a plurality of application programs (application programs or applications) running in the management server 20, data for the operation of the management server 20, and commands. At least some of these application programs may be downloaded from an external server through wireless communication.

Meanwhile, joint measurement torque data, which are actual measurement values corresponding to the EMG measurement data used in the present embodiment, stored in the database unit 24, predicted joint torque data, EMG standard data, etc., which are expected in response to the EMG measurement data, are stored. I can. In this case, the EMG measurement data, joint measurement torque data, joint predictive torque data, and EMG standard data may be implemented in the form of a mapping table, but are not limited thereto.

The monitoring unit 26 screens the operation state of the EMG measuring device 10, the operation state of the management server 20, and data transmitted/received between the EMG measuring device 10 and the management server 20 by user manipulation. It can be monitored through. That is, by checking the state of use of the EMG measuring device 10 in real time, it is possible to provide a more reliable feeling to the user by making it convenient for the user to use.

The management and control unit 28 may learn EMG measurement data and joint measurement torque data using deep learning to generate numerically optimized EMG standard data. Here, the EMG standard data may be data obtained by matching EMG measurement data and joint measurement torque data corresponding thereto.

Although it has been described that deep learning is used in the present embodiment, it is not limited thereto, and machine learning techniques such as a random forest and a support vector machine may be used.

According to an embodiment, when the EMG measurement data is received from the EMG measurement device 10, the management control unit 28 may extract joint measurement torque data corresponding to the EMG measurement data to calculate the estimated joint torque data.

The management server 20 may be implemented by a hardware circuit (eg, a CMOS-based logic circuit), firmware, software, or a combination thereof. For example, it may be implemented using transistors, logic gates, and electronic circuits in the form of various electrical structures.

The operation of the joint torque measurement system 1 using a multi-channel EMG signal according to an embodiment of the present invention having such a structure is as follows. 3 is a view for explaining a joint torque measurement method using a multi-channel EMG signal according to an embodiment of the present invention, FIG. 4 is a view for explaining the EMG measurement data shown in FIG. 3, and FIG. 5 is a diagram of the present invention. A diagram for explaining joint measurement torque data and joint predicted torque data according to an embodiment, and FIG. 6 is a detailed flowchart illustrating a step of calculating the joint predicted torque data shown in FIG. 3. 7 is a view for explaining the step of analyzing the EMG data shown in FIG. 6, FIG. 7(a) is a diagram showing generated RMS (Root Mean Square) data, and FIG. 7(b) It is a diagram showing Fast Fourier Transform (FFT) data.

First, as shown in FIG. 3, the management server 20 may generate EMG standard data (S100).

Specifically, the management server 20 may generate numerically optimized EMG standard data by learning joint measurement torque data, which is an actual measurement value for EMG measurement data acquired from a plurality of EMG measurement devices 10.

In this case, the management server 20 may learn EMG measurement data and joint measurement torque data using deep learning, but is not limited thereto.

Next, the EMG measuring apparatus 10 may receive EMG standard data generated from the management server 20 (S110).

Next, the EMG measurement apparatus 10 may generate EMG measurement data by obtaining EMG from a user who wants to measure EMG (S120).

For example, referring to FIG. 4, the EMG measurement apparatus 10 may generate an 8-channel EMG measurement signal. At this time, the EMG measuring device 10 may generate an EMG signal for a weight of about 7 to 9 Kg, but is not limited thereto.

Meanwhile, in the present embodiment, the step S110 of receiving the EMG standard data may be performed after the step S120 of generating the EMG measurement data. However, the present invention is not limited thereto and may be performed simultaneously.

Next, the EMG measurement apparatus 10 may learn EMG standard data, analyze the EMG measurement data, and calculate joint predicted torque data (S130). That is, the EMG measurement device 10 learns joint measurement torque data, which is an actual measurement value for the EMG measurement data included in the EMG standard data, and extracts the joint measurement torque data included in the EMG measurement data to calculate the joint predicted torque data. can do. As shown in FIG. 5, it can be seen that the calculated joint estimated torque data and the measured joint measurement torque data are substantially the same. Therefore, by learning the EMG standard data and predicting and providing a customized joint torque according to the user's EMG signal, it is possible to give a user a sense of trust.

Specifically, referring to FIG. 6, the data separation means 1480 of the control module 148 of the EMG measurement apparatus 10 may divide the EMG measurement data into a time domain and a frequency domain (S200). In this case, the EMG measurement data may include a surface EMG signal generated by multiple channels.

Next, the data analysis means 1482 of the control module 148 of the EMG measurement apparatus 10 may analyze the EMG measurement data divided into a time domain and a frequency domain (S210).

Specifically, the data analysis means 1482 analyzes the EMG measurement data and extracts features for a time domain as shown in FIG. 7(a) to generate RMS (Root Mean Square) data, and FIG. 7(b) As shown in FIG. 1, fast Fourier transform (FFT) data may be generated by converting a signal in a time domain into a signal in a frequency domain.

Next, the data calculation means 1484 of the control module 148 of the EMG measuring apparatus 10 may calculate joint predicted torque data from the analyzed EMG measurement data, excluding the surface EMG signal, in real time (S220).

Specifically, the data calculation means 1484 may learn based on the EMG standard data and calculate joint predicted torque data for EMG measurement data in real time from Fast Fourier Transform (FFT) data.

Next, the EMG measuring apparatus 10 may visually or audibly output the calculated joint estimated torque data so that the user can check it (S140).

Next, the management server 20 may update the EMG standard data in real time by using the estimated muscle joint torque data received from the EMG measuring device 10 (S150).

8 is a view for explaining a joint torque measurement method using a multi-channel EMG signal according to another embodiment of the present invention. In this case, since the method performed in FIG. 8 may be the same as the method performed in FIGS. 3 and 6, a detailed description thereof will be omitted.

First, as shown in FIG. 8, the management server 20 may generate EMG standard data (S300).

Next, the EMG measuring apparatus 10 may generate EMG measurement data by obtaining EMG from a user who wants to measure EMG (S310).

Next, the management server 20 may receive EMG measurement data from the EMG measurement device 10 (S320).

Next, the management server 20 may learn the EMG standard data and analyze the EMG measurement data to calculate joint predicted torque data (S330).

Specifically, the management server 20 learns joint measurement torque data, which is an actual measurement value for the EMG measurement data included in the EMG standard data, and extracts the joint measurement torque data included in the EMG measurement data to calculate the estimated joint torque data. can do.

Next, the EMG measurement apparatus 10 may receive joint predicted torque data from the management server 20 and output the EMG result data visually or aurally so that the user can check it (S340).

Finally, the management server 20 may update the EMG standard data in real time using the joint estimated torque data (S350).

9 is a view for explaining a joint torque measurement system 2 using a multi-channel EMG signal according to another embodiment of the present invention.

Referring to FIG. 9, a joint torque measurement system 2 using a multi-channel EMG signal according to another embodiment of the present invention may include a user terminal 30.

Except for the user terminal 30 shown in FIG. 9, it may have the same characteristics as the joint torque measurement system 1 using the multi-channel EMG signals shown in FIGS. 1 and 2.

In FIG. 9 below, detailed descriptions of content overlapping with those described in FIGS. 1 and 2 may be omitted, and different points may be mainly described. Accordingly, components that perform the same functions as those of the joint torque measurement system 2 using a multi-channel EMG signal shown in FIG. 9 are denoted by the same reference numerals as in FIGS.

First, the user terminal 30 can control the operation of the EMG measuring device 10 in the user terminal 30 by using an application program (application program or application), such an application program through wireless communication. It can be downloaded from an external server or management server (20).

The user terminal 30 is synchronized in real time with the EMG measuring device 10 and the management server 20 using a wireless communication network to transmit and receive data.

The user terminal 30 may include a signal transmission/reception unit 32, a memory unit 34, a display unit 36, and a terminal control unit 38.

The signal transmitting and receiving unit 32 may receive EMG standard data from the management server 20 and may receive EMG measurement data from the EMG measurement device 10. In addition, the signal transmitting and receiving unit 32 may transmit the estimated joint torque data to the EMG measuring device 10 and the management server 20.

The memory unit 34 may store data transmitted and received between the EMG measuring apparatus 10 and the management server 20 through a wireless communication network.

The display unit 36 may monitor data transmitted/received between the EMG measuring device 10, the management server 20, and the user terminal 30 through a screen. By visually and aurally outputting the operating state of the EMG measuring device 10, the user can easily check. Accordingly, since the user can control the EMG measurement apparatus 10 through the user terminal 30 without directly manipulating the EMG measurement apparatus 10, the convenience of use can be further improved.

The terminal control unit 38 may learn the EMG standard data received from the management server 20, analyze the EMG measurement data received from the EMG measurement device 10, and calculate the joint predicted torque data.

According to an embodiment, the terminal control unit 38 may receive the estimated joint torque data from the EMG measurement device 10 and transmit it to the management server 20 when the estimated joint torque data is calculated by the EMG measuring device 10. .

According to an embodiment, when the joint predicted torque data is calculated by the management server 20, the terminal control unit 38 may receive joint predicted torque data from the management server 20 and transmit it to the EMG measuring apparatus 10.

Such a user terminal 30 may include various portable electronic communication devices that support communication with the EMG measuring device 10 and the management server 20. For example, as a separate smart device, a smart phone, a personal digital assistant (PDA), a tablet, a wearable device, for example, a smartwatch, a glass terminal (Including Smart Glass), HMD (Head Mounted Display), etc.) and various Internet of Things (IoT) terminals, but are not limited thereto.

The operation of the joint torque measurement system 2 using a multi-channel EMG signal according to another embodiment of the present invention having such a structure is as follows. 10 is a view for explaining a joint torque measurement method using a multi-channel EMG signal according to another embodiment of the present invention

In FIG. 10, a case in which EMG result data is generated by the user terminal 30 is limited and described. In this case, since the method performed in FIG. 9 may be the same as the method performed in FIGS. 3 and 6, a detailed description thereof will be omitted.

First, as shown in FIG. 10, the management server 20 may generate EMG standard data (S400), and the EMG measurement device 10 may generate EMG measurement data (S410).

Next, the user terminal 30 may receive the EMG standard data generated from the management server 20 and receive the EMG measurement data from the EMG measurement device 10 (S420).

Next, the user terminal 30 may learn the EMG standard data received from the management server 20, analyze the EMG measurement data received from the EMG measurement device 10, and calculate the joint predicted torque data (S430). ).

According to an embodiment, the user terminal 30 receives the estimated joint torque data from the EMG measuring device 10 and transmits it to the management server 20 when the estimated joint torque data is calculated by the EMG measuring device 10, or When the estimated joint torque data is calculated by the management server 20, the estimated joint torque data may be received from the management server 20 and transmitted to the EMG measuring apparatus 10.

Next, the EMG measurement apparatus 10 may receive joint predicted torque data from the user terminal 30 and output the EMG result data visually or aurally so that the user can check it (S440).

Finally, the management server 20 may update the EMG standard data in real time by using the joint predicted torque data received from the user terminal 30 (S450).

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. Software modules include 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 invention pertains.

In the above, embodiments of the present invention have been described with reference to the accompanying drawings, but those skilled 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.

10: EMG measuring device
20: management server
30: user terminal

Claims (20)

An EMG measurement device for obtaining EMG measurement data from a user using at least one channel and calculating joint predicted torque data for the EMG measurement data in real time based on EMG standard data; And
A management server for generating the EMG standard data by learning the EMG measurement data and the joint predicted torque data corresponding to the EMG measurement data; Containing, joint torque measurement system using a multi-channel EMG signal. The method of claim 1,
The EMG measuring device,
An EMG measurement unit that directly contacts the user's body to obtain the EMG measurement data from the user; And
A joint torque calculation unit for calculating the joint estimated torque data in real time by analyzing the EMG measurement data by simultaneously considering the time domain and the frequency domain based on the EMG standard data; Containing, joint torque measurement system using a multi-channel EMG signal. The method of claim 2,
The joint torque calculation unit,
Data separation means for separating the EMG measurement data into the time domain and the frequency domain;
Data analysis means for analyzing the EMG measurement data divided into the time domain and the frequency domain; And
A data calculation means for calculating the joint predicted torque data for the analyzed EMG data in real time; Containing, joint torque measurement system using a multi-channel EMG signal. The method of claim 3
The data analysis means,
Analyzing the EMG measurement data to generate RMS (Root Mean Square) data for the time domain, converting the time domain signal to the frequency domain signal to generate Fast Fourier Transform (FFT) data, multi-channel Joint torque measurement system using EMG signals. The method of claim 4,
The data calculation means,
A joint torque measurement system using a multi-channel EMG signal for learning based on the EMG standard data and calculating the joint predicted torque data for the EMG measurement data in real time from the Fast Fourier Transform (FFT) data. According to claim 2
The EMG measurement data includes a surface EMG signal generated by multiple channels, a joint torque measurement system using a multi-channel EMG signal. The method of claim 6,
The joint torque calculation unit,
A joint torque measurement system using a multi-channel EMG signal for calculating the joint estimated torque data in real time by considering the surface EMG signal in consideration of the time domain and the frequency domain at the same time. The method of claim 1,
The management server,
A management control unit receiving joint measurement torque data for the EMG measurement data obtained from the EMG measurement device, learning the joint measurement torque data, and generating the numerically optimized EMG standard data; Containing, joint torque measurement system using a multi-channel EMG signal. The method of claim 8,
The management control unit,
A joint torque measurement system using a multi-channel EMG signal for calculating the joint predicted torque data using EMG measurement data obtained from the EMG measurement device based on the EMG standard data. The method of claim 1,
The management server,
A joint torque measurement system using a multi-channel EMG signal for updating the EMG standard data in real time in response to the EMG measurement data and the joint predicted torque data. The method of claim 1,
When receiving the EMG measurement data from the EMG measurement device and the EMG standard data from the management server, a user terminal that learns the EMG standard data and calculates the joint predicted torque data using the EMG measurement data ; A joint torque measurement system using a multi-channel EMG signal further comprising a. Calculating, by the electromyography apparatus, electromyogram measurement data;
Calculating, in real time, joint predicted torque data from the EMG measurement data by learning, by the EMG standard data, by the EMG measurement device; And
Receiving, by a management server, the predicted joint torque data and updating it in real time; Including,
The step of calculating the joint estimated torque data,
A joint torque measurement method using a multi-channel EMG signal in which the EMG measurement device analyzes the EMG measurement data by simultaneously considering the time domain and the frequency domain based on the EMG standard data and calculates the joint predicted torque data in real time. . The method of claim 12,
The step of calculating the joint estimated torque data,
Separating, by the EMG measurement device, the EMG measurement data into the time domain and the frequency domain;
Analyzing, by the EMG measuring device, the EMG measurement data separated into the time domain and the frequency domain; And
Calculating the joint predicted torque data for the analyzed EMG data in real time by the EMG measuring device; Containing, joint torque measurement method using a multi-channel EMG signal. The method of claim 13,
Analyzing the EMG measurement data,
Analyzing the EMG data to generate RMS (Root Mean Square) data for the time domain; And
Generating Fast Fourier Transform (FFT) data by converting the time domain signal into the frequency domain signal; Containing, joint torque measurement method using a multi-channel EMG signal. The method of claim 12,
The step of calculating the joint estimated torque data,
A joint torque measurement method using a multi-channel EMG signal in which the EMG measuring device calculates the joint predicted torque data in real time by considering a surface EMG signal included in the EMG measurement data by simultaneously considering the time domain and the frequency domain . The method of claim 12,
The management server,
A joint torque measurement method using a multi-channel EMG signal for receiving joint measurement torque data for the EMG measurement data obtained from the EMG measurement device and learning the joint measurement torque data to generate the numerically optimized EMG standard data . The method of claim 12,
When the management server receives the EMG measurement data from the EMG measurement device, learning the EMG standard data and calculating the joint predicted torque data using the EMG measurement data; A method for measuring joint torque using a multi-channel EMG signal further comprising a. The method of claim 12,
When the user terminal receives the EMG measurement data from the EMG measurement device and the EMG standard data from the management server, it learns the EMG standard data and calculates the joint predicted torque data using the EMG measurement data. The step of doing; A method for measuring joint torque using a multi-channel EMG signal further comprising a. The method of claim 12,
Updating the EMG standard data in real time in response to the EMG measurement data and the joint predicted torque data; Containing, joint torque measurement method using a multi-channel EMG signal. A computer program that is combined with a computer as hardware and stored in a recording medium readable by a computer to perform the method of claim 12.

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