KR20040011612A - System And Method For Human Interface Using Biological Signals - Google Patents

Abstract

PURPOSE: A system and a method for a human interface using a biometric signal are provided to presume the biometric information by measuring, processing, and analyzing the biometric signal, and to adjust an application system according to an adjusting signal by transforming the biometric information into the adjusting signal. CONSTITUTION: A biometric signal measuring module(10) detects and amplifies the biometric signal such as an electromyography signal and an electroencephalography signal of a human body. A biometric signal processing module(20) receives, preprocesses, and collects the amplified biometric signal. A biometric signal transforming module(30) extracts a characteristic point by receiving and transforming the collected biometric signal. A biometric signal analysis module(40) presumes the biometric information by using the characteristic point. A biometric information transforming module(50) transforms the biometric information into the adjusting signal for an object device.

Description

System and Method for Human Interface Using Biological Signals

The present invention relates to a human interface system and method using a bio-signal, and more particularly, to estimate bio-information by measuring, analyzing and processing a bio-signal in a human-interface device, and adjusting the bio-signal by combining the bio-signal information in the adjustment target device. A human interface system and method using a biosignal to adjust an application system.

In general, the conventional human interface system uses a mechanical method, an optical method and an electromagnetic method.

However, since the mechanical method uses indirect interfaces such as a keyboard, a mouse, and a joystick, it does not give a user's friendliness from a psychological point of view, and it is bound because mechanical parts must be added. There is a disadvantage due to the structure, the operational discomfort due to the unbalance of the weight.

In the case of the optical method, a complex system such as a camera and an image processing system must be constructed and space-limited.

In addition, in the case of the electromagnetic method, the measuring device is expensive and has many limitations in installation and use.

Therefore, the conventional human interface system has many problems as described above because it uses a mechanical method, an optical method and an electromagnetic method.

In order to solve the problems described above, an object of the present invention is to estimate the biometric information by measuring, processing and analyzing the biosignal by implementing a human interface device using the biosignal.

Another object of the present invention is to adjust the application system according to the adjustment signal by converting the estimated biometric information into a coordinate signal.

1 is a conceptual diagram of a human interface system using a bio-signal according to an embodiment of the present invention.

FIG. 2 is a detailed block diagram of the human interface device of FIG. 1; FIG.

3 is a detailed block diagram of the adjustment target device in FIG. 1;

4 is a flow chart illustrating a human interface method using a bio-signal according to an embodiment of the present invention.

FIG. 5 is a flowchart illustrating an operation process of a human interface device of FIG. 4.

6 is a flowchart illustrating an operation process of an adjustment target device in FIG. 4.

Explanation of symbols on the main parts of the drawings

1: human interface device 2: adjustment target device

10: biosignal measurement module 20: biosignal processing module

30: biosignal conversion module 40: biosignal analysis module

50: biometric information conversion module 60: input interface module

70: application system

In order to solve the above object, the human interface system using the bio-signal of the present invention measures the bio-signal from the human body and then processes, converts and analyzes the bio-signal to obtain bio-information and combines the bio-information. A human interface device converting the adjustment signal; And receiving an adjustment signal from the human interface device, the adjustment target device being adjusted according to the corresponding adjustment signal.

The human interface method using the biosignal of the present invention measures a biosignal from a human body, processes, converts, and analyzes the biosignal to obtain biometric information, converts the biometric information into a coordinated signal, and then delivers the biosignal information. Process; Receiving the adjustment signal and adjusting the application system according to the adjustment signal.

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

First, a human interface system using a biosignal according to an embodiment of the present invention will be described with reference to FIG. 1.

1 is a conceptual diagram of a human interface system using a biosignal according to an embodiment of the present invention.

The human interface system according to an embodiment of the present invention measures the electromyography signal, the muscle impedance signal and the EEG signal which are the bio signals from the human body, and then processes, converts and analyzes the corresponding bio signals to determine the force information, the position information, and the determination information. Obtains and combines the corresponding biometric information into a command, command and control signal, which is an adjustment signal, and receives the command, command, and control signal, which is an adjustment signal, from the human interface device 1. And an adjustment target device 2 which is adjusted in accordance with an instruction, command and control signal which is an adjustment signal. In this case, the biosignal is composed of an EMG signal, an impedance signal, and an EEG signal, and thus, the biosignal is composed of force information, location information, and determination information. Accordingly, the biometric information may be configured to exclude the determination information.

Here, referring to the biosignal, in the present invention, a biosignal, which is a purely informational signal that is not generated by human movement as a signal generated by a human, is used as an input of the human interface device 1. Signals that can be used as bio signals include voluntary signals such as electromyography due to potential difference generated by impulse transmission of human nerve tissue during muscle movement, safety related to eye muscle movement, and involuntary and voluntary. One brain wave, and a living body impedance.

The biological signal has a high signal-to-noise ratio due to a lot of noise from a signal point of view, and is difficult to process, but various input signals can be obtained by simply attaching electrodes to the human body.

The EMG can estimate the force by frequency analysis of dislocation generation, but it is difficult to measure the absolute position because depolarization occurs during free movement. In addition, the EEG is widely used for estimating conscious exercise or estimating rational judgment.

Hereinafter, the human interface device 1 will be described in detail with reference to FIG. 2.

FIG. 2 is a detailed block diagram of the human interface device 1 in FIG. 1.

The human interface device 1 includes a biosignal measurement module 10 that detects and amplifies a biosignal such as an EMG signal, an impedance signal, and an EEG signal from a human body, and a biosignal signal amplified by the biosignal measurement module 10. A biosignal processing module 20 to receive and preprocess and collect the biosignal processing module 20, a biosignal conversion module 30 to receive and convert a biosignal collected from the biosignal processing module 20 to extract a feature point, and the biosignal conversion The biosignal analysis module 40 estimating biometric information by using the feature points extracted by the module 30 and the biosignal information estimated from the biosignal analysis module 40 are received to the adjustment target device 2. And a biometric information converting module 50 for converting into an adjustment signal which is an instruction, command and control signal. In this case, the biosignal may be configured to exclude the EEG signal.

Here, the bio-signal measuring module 10 includes an EMG measurement electrode 111 for measuring an EMG signal from muscles of the human body and an EMG amplifier 112 for amplifying the EMG signal measured from the EMG measurement electrode 111. A standard signal is obtained from the EMG detector 110, the muscle impedance measuring electrode 121 for measuring the muscle impedance signal from the muscles of the human body, the standard signal electrode 122 for generating a high frequency standard signal, and the standard signal electrode 122. Near-impedance detection unit 120 having an input electrode 123 for receiving the received signal by measuring the change in the standard impedance compared to the near-impedance signal of the near-impedance measurement electrode 121 received by the degree of change, the brain of the human body EEG measurement electrode 131 for measuring the EEG signal from the EEG and the EEG amplifier 132 for amplifying the EEG signal measured from the EEG measurement electrode 131 A comparison is made including the EEG detector 130. In this case, the biosignal measurement module 10 may be configured to exclude the EEG detector 130.

In addition, the biosignal processing module 20 receives the amplified EMG signal from the EMG amplifier 112 and removes the noise of the EMG signal and filters to obtain a band frequency. The EMG processor 210 includes an EMG collector 212 configured to receive and collect filtered EMG signals from the preprocessor 211, and receive an I impedance signal measured from the input electrode 123 to a corresponding EMP signal. Near impedance, which includes a near impedance preprocessor 221 for removing noise and filtering the bandwidth to obtain a band frequency, and a near impedance collector 222 for receiving and collecting the filtered near impedance signal from the near impedance preprocessor 221. Receiving the amplified signal from the processor 220, the EEG amplifier 132 amplified noise for the EEG signal And comprises a brain wave processing unit 330 comprising a brain wave collector 232 for collection by receiving the filtered brain waves from the brain waves preprocessor 231 and the EEG preprocessor 231 for filtering to obtain the band frequency. In this case, the biosignal processing module 20 may be made except for the EEG processor 330.

In addition, the bio-signal conversion module 30 is an EMG A / D (Analog / Digital) converter 311 and the EMG A / D converter for converting an analog EMG signal received from the EMG collector 212 into a digital EMG signal. The EMG converter 312 and the EMG converter 312 convert the temporal EMG signal into a frequency EMG signal by converting a time EMG signal into a frequency EMG signal by using a fast Fourier transform and an average squared value calculation. EMG converter 310 having an EMG feature point extractor 313 for extracting a feature point by the converted EMG signal received from the) and an analog EMG signal received from the EMP impedance collector 222 as a digital EMP signal Digital EMG received from converting near-impedance A / D converter 322 and said near-impedance A / D converter 322 The change in the signal level is transmitted from the near impedance transducer 322 and the near impedance transducer 322 which converts the time near impedance signal into a frequency near impedance signal by the fast Fourier transform and the mean squared square value calculation and converts it into a muscle movement displacement. A near-impedance converter 320 having a near-impedance feature point extractor 323 for extracting feature points by the received converted near-impedance signal, and converting an analog brain wave signal received from the brain wave collector 230 into a digital brain wave signal Potential difference of brain by converting time EEG signal into frequency EEG signal by fast Fourier transform and mean squared square calculation of the level of digital EEG signal received from EEG A / D converter 331 and EEG A / D converter 331 The EEG transducer 332 converts the displacement and the EEG signal received from the EEG transducer 332 It includes an EEG converter 330 having an EEG feature point extractor 333 for extracting the mark. In this case, the biosignal conversion module 30 may be configured to exclude the EEG converter 330.

In addition, the biosignal analysis module 40 may include a force information estimator 410 for estimating muscle force information using the EMG feature point extracted by the EMG feature point extractor 313, and the EMP extractor 323. A location information estimator 420 for estimating the position information of the muscle using the muscle impedance feature point extracted by Equation 2) and an EEG feature point extracted by the EEG feature point extractor 333 to estimate determination information of the brain. The determination information estimation unit 430 is included. In this case, the biosignal analysis module 40 may be configured to exclude the determination information estimator 430.

Hereinafter, the adjustment target device 2 will be described in detail with reference to FIG. 3.

FIG. 3 is a detailed block diagram of the adjustment target device 1 in FIG. 1.

The adjustment target device 2 includes an input interface module 60 that receives an instruction, a command and a control signal, which are adjustment signals from the biometric information conversion module 50, an instruction that is an adjustment signal from the input interlace module 60, And a plurality of application systems 70 that receive commands and control signals and adjust according to corresponding adjustment signals.

Here, the input interface module 60 receives an indication signal from the biometric information conversion module 50 and transmits the indication signal to the application system 70 to indicate and adjust the corresponding application system 7, and the A command unit 620 for receiving a command signal from the biometric information conversion module 50 and transmitting the command signal to the application system 70 to control the application system 70, and a control signal from the biometric information conversion module 50. The control unit 60 receives and transmits the control to the application system 70 and controls the application system 7. Accordingly, the application system 70 receives an indication signal, a command signal, and a control signal, which are adjustment signals, from the indicating unit 610, the command unit 620, and the control unit 630, and is adjusted according to the corresponding adjustment signal. Here, the application system 70 may be not only the graphics system 710 and the robot system 720, but also home appliances such as televisions, audio, lights, and the like, indicating devices of computers and game machines. In particular, the application system 70 may be applied to virtual reality, or may be applied to assistive devices of the disabled.

Hereinafter, a human interface method according to an embodiment of the present invention will be described with reference to FIG. 4. Although the human interface method of the present invention is described when the biosignal is composed of an EMG signal, an impedance signal, and an EEG signal, the biosignal may be configured except for an EEG signal in the human interface method of the present invention.

First, the human interface device 1 measures EMG signals, muscle impedance signals, and EEG signals, which are biological signals, from a human body, and then processes, converts, and analyzes the corresponding biological signals to obtain force information, position information, and determination information, which are biometric information. The biometric information is combined to be converted into an indication, command and control signal which is an adjustment signal and transmitted to the adjustment target device 2 (S401).

Accordingly, the adjustment target device 2 receives the instruction, command, and control signal, which is an adjustment signal, from the human interface device 1, and adjusts the application system 70 according to the instruction, command, and control signal, which is the corresponding adjustment signal. (S402).

Hereinafter, an operation process of the human interface device 1 will be described with reference to FIG. 5.

FIG. 5 is a flowchart illustrating an operation process of a human interface device of FIG. 4.

First, in the human interface device 1, the biosignal measurement module 10 detects and amplifies biosignals such as EMG signals, muscle impedance signals, and brain wave signals from the human body, and then transfers the biosignals to the biosignal processing module 20. (S501). That is, the biosignal measurement module 10 detects the EMG signal from the EMG measurement electrode 111 of the EMG detector 110, amplifies it from the EMG amplifier 112, and the N impedance electrode of the EMG detector 120. Detecting the near impedance signal from the 121 and the standard signal electrode 122 and input it to the input electrode 123, the EEG amplifier 132 by detecting the EEG signal from the EEG measuring electrode 131 of the EEG detector 130 Amplify it.

Thus, the biosignal processing module 20 receives the biosignal signal amplified from the biosignal measurement module 10, collects the biosignal, preprocesses the collected biosignal, and transfers the biosignal to the biosignal conversion module 30 (S502). That is, the biosignal processing module 20 removes the noise of the EMG signal from the EMG preprocessor 211 of the EMG processor 210 and filters the EMG collector 212 after filtering to obtain a band frequency. The near-impedance preprocessor 221 of the near-impedance processor 220 removes the noise for the near-impedance signal and filters it to obtain a band frequency, and then collects it from the near-impedance collector 222, and the brain wave of the EEG processor 230. The preprocessor 231 removes the noise on the EEG signal and filters it to obtain a band frequency, and then collects it in the EEG collector 232.

Accordingly, the biosignal conversion module 30 receives the biosignal collected from the biosignal processing module 20, converts the extracted biomarkers, extracts a feature point, and then transfers the extracted feature points to the biosignal analysis module 40 (S503). That is, the biosignal conversion module 30 converts the analog EMG signal into a digital EMG signal by the EMG converter 311 of the EMG converter 310 and then converts the time EMG signal into a frequency EMG signal by the EMG converter 312. Extract the feature point from the EMG extractor 313 by converting it into a signal, and convert the analog IMP signal into a digital IMP signal from the IMP converter 321 of the IMP converter 320 and then the IMP converter. At 322, the time-impedance signal is converted into a frequency-impedance signal to extract a feature point from the near-impedance feature point extractor 323, and the EEG A / D converter 331 of the EEG converter 330 converts the analog EEG signal into a digital signal. After converting to an EEG signal, the EEG converter 332 converts the time EEG signal into a frequency EEG signal to extract the feature points from the EEG feature point extractor 333 Recognize a turn.

In addition, the biosignal analysis module 40 receives the feature point extracted from the biosignal conversion module 30, estimates biometric information using the feature point, and then transfers the biometric information to the biometric information conversion module 50 (S504). . That is, the biosignal analysis module 40 estimates muscle force information using the EMG feature point in the force information estimator 410, and position information of the muscle using the muscle impedance feature point in the position information estimator 420. The estimation information estimator 430 estimates the determination information of the brain using the EEG feature point.

Accordingly, the biometric information conversion module 50 receives the biometric information estimated from the biosignal analysis module 40, combines the biometric information, and converts the biometric information into an adjustment signal which is an instruction, command, and control signal for the adjustment target device 2. It transfers to the input interface module 60 of the said adjustment object apparatus 2 (S505).

In general, mutual interference should be excluded in order to simultaneously measure the EMG signal and the IMP signal. For this purpose, the present invention separates the standard frequency for EMG measurement from the EMG signal on the frequency spectrum. Minimize interference. And, in order to prevent the charge charge of the muscle by the standard signal of the near impedance, the present invention uses a synchronous signal acquisition method through a signal controller.

Hereinafter, an operation process of the adjustment target device 2 will be described with reference to FIG. 6.

FIG. 6 is a flowchart illustrating an operation process of an adjustment target device in FIG. 4.

First, in the adjustment target device 2, the input interface module 60 receives the adjustment signal from the biometric information conversion module 50 (S601), and transmits it to the application system 70 (S602). That is, the input interface module 60 transmits an indication signal from the indicating unit 610 to the application system 70 to instruct and adjust the application system 70, and the command unit 620 transmits a command signal to the application system ( 70 to command adjustment of the application system 70, and the control unit 630 transmits a control signal to the application system 70 to control and control the application system 70.

Accordingly, the application system 70 receives the adjustment signal from the input interface module 60 and is adjusted according to the corresponding adjustment signal (S603). That is, the application system 70 receives an instruction signal from the instruction unit 610 and adjusts the instruction according to the instruction signal, receives the instruction signal from the command unit 620 and adjusts the instruction according to the instruction signal. The control signal is received from the control unit 630 and controlled according to the control signal.

The present invention proposes a new concept of the human interface system using a biosignal, and biological signals such as EMG and bioimpedance have been studied as alternative control interfaces for tracking the position and force of the human arm. In addition, EEG is used for the detection of basic human intentions. The biological human interface system can remotely control the application system 70 in the distance as well as for computer simulation models represented in three-dimensional graphics.

Using this advantage, the human interface system using the biosignal of the present invention can control the position of a distant robot using a near impedance signal, estimate the joint force from the EMG signal, and feed back the human arm force. have. In addition, because it is possible to measure the state of mind using EEG signals, biometric information can be used to help users of certain systems. Therefore, according to the present invention, the interaction between the human and the machine is possible by the biosignal.

As a result, in the present invention, a variety of biological signals of a human may be newly interpreted and based on this, the conventional mechanical interface method may be supplemented or replaced with an interface method using a biological signal. In particular, it is more advantageous because it is hardly restricted by space and time. The human interface system according to the present invention is not only applicable to most fields used up to now, but also to implement a variety of interfaces by transferring new information such as force information that is difficult to express conventionally.

In addition, the embodiment according to the present invention is not limited to the above-mentioned, and can be implemented by various alternatives, modifications, and changes within the scope apparent to those skilled in the art.

As described above, the present invention can solve the spatial and temporal constraints that are disadvantages of the conventional human interface system, and it is also possible to interface with new biometric information that is difficult to transfer in the conventional human interface system. In addition, since the signal from the human living body is directly used, the user can be more friendly and have an intuitive interface.

Claims (17)

A human interface device which measures a biosignal from a human body, processes, converts, and analyzes the biosignal to obtain biometric information, and combines the biometric information into a coordinated signal; The human interface system using a bio-signal, characterized in that the adjustment device receives the adjustment signal from the human interface device is adjusted according to the adjustment signal. The method of claim 1, The human interface device, A biosignal measurement module for detecting and amplifying a biosignal from a human body; A biosignal processing module configured to receive the amplified biosignal from the biosignal measuring module and preprocess and collect the biosignal; A biosignal conversion module configured to receive and convert a biosignal collected from the biosignal processing module to extract a feature point; A biosignal analysis module for estimating biometric information using feature points extracted by the biosignal conversion module; And a biometric information conversion module configured to receive the biometric information estimated from the biosignal analysis module and convert the biometric information into an adjustment signal for the adjustment target device. The method of claim 2, The bio-signal measuring module, An EMG detector including an EMG measurement electrode for measuring an EMG signal from muscles of a human body, and an EMG amplifier for amplifying the EMG signal measured from the EMG measurement electrode; A standard impedance electrode for measuring a near-impedance signal from a muscle of a human body, a standard signal electrode for generating a high frequency standard signal, and a standard signal from the standard signal electrode are received, and compared with the near-impedance signal of the near-impedance measurement electrode. A human interface system using a bio-signal, comprising a near-impedance detector having an input electrode measuring a near-impedance signal by a degree of change of the signal. The method of claim 3, The bio-signal measuring module, The human interface system using a bio-signal further comprises an EEG detection unit having an EEG measurement electrode for measuring the EEG signal from the brain of the human body and an EEG amplifier for amplifying the EEG signal measured from the EEG measurement electrode. The method of claim 2, The biosignal processing module, An electromyography having an electromyogram signal received from the electromyogram amplifier, an electromyography preprocessor for removing noise for the electromyography signal and filtering to obtain a band frequency, and an electromyography collector for receiving and collecting the filtered electromyography signal from the electromyography preprocessor; A processing unit; A muscle receiving the collected near impedance signal from the input electrode and removing the noise for the corresponding near impedance signal and filtering to obtain a band frequency, and receiving the collected near impedance signal from the near impedance preprocessor Human interface system using a bio-signal comprising a near-impedance processing unit having an impedance collector. The method of claim 5, The biosignal processing module, EEG processor receiving EEG signal received from the EEG amplifier to remove the noise for the EEG signal and the EEG pre-filter for filtering to obtain the band frequency and EEG processing unit having an EEG collector receiving and collecting the EEG filtered from the EEG pre-processor Human interface system using a bio-signal, characterized in that further comprises. The method of claim 2, The bio signal conversion module, The frequency EMG signal for the EMG A / D (Analog / Digital) converter for converting the analog EMG signal received from the EMG collector into a digital EMG signal and the level of the EMG signal received from the EMG A / D converter. An EMG converter including an EMG converter for converting into a potential difference displacement of the muscle, and an EMG feature point extractor for extracting feature points by the converted EMG signal received from the EMG converter; A frequency near-impedance signal is converted into a near-impedance A / D converter for converting the analog-impedance signal received from the near-impedance collector into a digital near-impedance signal and a time-to-impedance signal for the digital EMG signal level received from the near-impedance A / D converter. And a near-impedance converter having a near-impedance feature extractor for extracting a feature point by the converted near-impedance signal received from the near-impedance transducer and the near-impedance transducer for converting into muscle displacement. Human interface system using signals. The method of claim 7, wherein The bio signal conversion module, EEG A / D converter that converts the analog EEG signal received from the EEG collector into a digital EEG signal and the potential difference of the brain by converting the time EEG signal for the level of the digital EEG signal received from the EEG A / D converter into a frequency EEG signal A human interface system using a bio-signal further comprises an electroencephalogram converting unit comprising an electroencephalogram converter for converting the displacement and an electroencephalogram feature extractor for extracting feature points by the converted electroencephalogram signal received from the electroencephalogram converter. The method of claim 2, The biosignal analysis module, A force information estimator for estimating muscle force information using the EMG feature points extracted by the EMG feature point extractor; And a position information estimator for estimating the position information of the muscle using the muscle impedance feature point extracted by the muscle impedance feature point extractor. The method of claim 9, The biosignal analysis module, And a determination information estimator for estimating determination information of the brain using the EEG feature points extracted by the EEG feature point extractor. The method of claim 1, The adjustment target device, An input interface module receiving an adjustment signal from the biometric information conversion module; And a plurality of application systems that receive adjustment signals from the input interlacing module and are adjusted according to the corresponding adjustment signals. The method of claim 11, The input interface module, An instruction unit configured to receive an indication signal from the biometric information conversion module and transmit the indication signal to the application system to indicate and adjust the application system; A command unit configured to receive a command signal from the biometric information conversion module and transmit the command signal to the application system to control a corresponding application system; And a control unit configured to receive a control signal from the biometric information conversion module and transmit the control signal to the application system to control and adjust the application system. The method of claim 1, The bio signal is, Human interface system using a bio-signal consisting of an EMG signal and a muscle impedance signal, or consisting of an EMG signal, a muscle impedance signal and an EEG signal. Measuring a biosignal from a human body, processing, converting, and analyzing the biosignal to obtain biometric information, converting the biosignal information into a coordinated signal, and then transmitting the biometric information; Receiving the adjustment signal and adjusting an application system according to the adjustment signal. The method of claim 14, Acquiring biometric information from the biosignal and converting the biometric information into an adjustment signal may include: Detecting and amplifying a biological signal from a human body; Receiving and amplifying the amplified biosignal; Extracting feature points by receiving and converting the collected biological signals; Receiving the extracted feature points and estimating biometric information using the corresponding feature points; And receiving and combining the estimated biometric information into a coordinated signal. The method of claim 14, The process of adjusting the application system according to the adjustment signal received from the adjustment signal, Receiving a coordination signal and delivering it to an application system; And receiving the adjustment signal and adjusting the received adjustment signal according to the adjustment signal. The method of claim 14, The bio signal is, A human interface method using a biosignal comprising an EMG signal and an EMG signal or an EMG signal, an EMP signal, and an EEG signal.