Electromyography (EMG) instruments, methods and systems

Abstract

Supports the expression of a smile. As one embodiment of the present invention, the electromyography (EMG) meter is an EMG meter for measuring EMG, comprising: a determination unit that determines whether the difference between a characteristic quantity of the EMG measured by the EMG meter and a characteristic quantity of the EMG objectively determined to be an attractive smile is within a predetermined range; and an output unit that outputs whether the difference is within or outside the predetermined range.

Description

Technical Field

[0001] This invention relates to electromyography (EMG) devices, methods, and systems. Background Technology

[0002] In the past, smiles have been required in many situations across various professions and daily life. Therefore, methods to support the expression of smiles have been considered.

[0003] For example, Patent Document 1 describes a method that measures the smile value of a user contained in a captured image, converts the smile value into a smile level, and indicates that the image is a face image with a smile level higher than the smile level, thereby improving the user's smile level.

[0004] Existing technical documents

[0005] Patent documents

[0006] Patent Document 1: Japanese Patent Application Publication No. 2017-182594 Summary of the Invention

[0007] The problem that the invention aims to solve

[0008] However, in Patent Document 1, the smile value was measured from a still image, which failed to capture the dynamic features of the smile.

[0009] Furthermore, in Patent Document 1, the user makes an expression towards the camera of the smile feedback device. Since the user is aware of the camera while observing their own face and making a smile, it is not a normal state and may result in an unnatural smile.

[0010] Therefore, the purpose of this invention is to support the expression of a smile.

[0011] Methods for solving problems

[0012] As one embodiment of the present invention, the electromyography (EMG) meter is an EMG meter for measuring EMG, comprising: a determination unit for determining whether the difference between a characteristic quantity of the EMG measured by the EMG meter and a characteristic quantity of the EMG that is objectively determined to be an attractive smiling face is within a predetermined range; and an output unit for outputting whether the difference is within or outside the predetermined range.

[0013] Invention Effects

[0014] According to the present invention, it is possible to support the expression of a smile. Attached Figure Description

[0015] Figure 1 This is an overall structural diagram of one embodiment of the present invention.

[0016] Figure 2This is a functional block diagram of a smile detection system according to one embodiment of the present invention.

[0017] Figure 3 This is a diagram illustrating the results of evaluating the smiles of subjects according to an embodiment of the present invention.

[0018] Figure 4 This is a diagram illustrating the results of evaluating the smiles of subjects according to an embodiment of the present invention.

[0019] Figure 5 This is a graph illustrating the results of evaluating the attractiveness of a subject's smile according to an embodiment of the present invention.

[0020] Figure 6 This is a graph illustrating the results of evaluating the attractiveness of a subject's smile according to an embodiment of the present invention.

[0021] Figure 7 This is a diagram used to illustrate myoelectric potentials related to one embodiment of the present invention.

[0022] Figure 8 This is a diagram used to illustrate eight feature quantities related to one embodiment of the present invention.

[0023] Figure 9 This is a diagram used to illustrate the relationship between the attractiveness of a smile and the size, rate of increase, and correlation between the eyes and mouth of the smile, according to an embodiment of the present invention.

[0024] Figure 10 This is a flowchart of the registration process according to one embodiment of the present invention.

[0025] Figure 11 This is a flowchart of a smile detection process according to an embodiment of the present invention.

[0026] Figure 12 This is a block diagram illustrating an example of the hardware structure of a myoelectric meter and a smile detection device according to an embodiment of the present invention. Detailed Implementation

[0027] Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

[0028] This specification describes an implementation using myoelectric potentials (MPPs) at four locations on a user's face, but is not limited thereto. The present invention can detect smiles using MPPs at multiple locations on a user's face. For example, multiple locations may include the area around the eyes, and at least two or more of the zygomaticus minor, zygomaticus major, and risorius muscles.

[0029] <Overall Structure>

[0030] Figure 1This is an overall structural diagram relating to one embodiment of the present invention. (See diagram below.) Figure 1 As shown, the smile detection system 1 includes an electromyography (EMG) meter 10 and a smile detection device 20. The EMG meter 10 can transmit and receive data with the smile detection device 20 via wired or wireless communication. These will be described separately below.

[0031] The electromyography (EMG) meter 10 is a device for measuring EMG. Based on the EMG measured by the EMG meter 10, the EMG meter 10 can determine whether the user is smiling and output the result.

[0032] The smile detection device 20 can determine whether a user is smiling based on the user's electromyography (EMG) measured by the electromyography (EMG) meter 10 and output the result. The smile detection device 20 can be any computer such as a personal computer, tablet computer, or smartphone.

[0033] <Function Box>

[0034] Figure 2 This is a functional block diagram of a smile detection system 1 according to an embodiment of the present invention. Figure 2 As shown, the smile detection system 1 includes a measurement unit 101, a registration unit 102, a judgment unit 103, an output unit 104, and an electromyography data storage unit 105. Furthermore, by executing a program, the smile detection system 1 can function as the measurement unit 101, the registration unit 102, the judgment unit 103, and the output unit 104.

[0035] Alternatively, the electromyography (EMG) meter 10 may include a measurement unit 101, a registration unit 102, a judgment unit 103, an output unit 104, and an EMG data storage unit 105 (that is, without using the smile detection device 20), or the smile detection device 20 may include at least a portion of the registration unit 102, the judgment unit 103, the output unit 104, and the EMG data storage unit 105 (that is, using the smile detection device 20).

[0036] The measurement unit 101 measures the electromyographic potentials (EMGs) on the user's face. Additionally, the measurement unit 101 generates an electromyogram (a record of the active potentials generated in the muscles over time).

[0037] For example, the measurement unit 101 measures the myoelectric potentials at at least two of the orbicularis oculi, zygomaticus minor, zygomaticus major, and risorius muscles on the user's face.

[0038] For example, the measurement unit 101 measures the myoelectric potentials (MPPs) at the right or left eye of the user's face (i.e., 1 channel), and the MPPs at one of the right or left zygomaticus minor, right or left zygomaticus major, and right or left risorius muscles (i.e., 1 channel), totaling 2 channels. Alternatively, the measurement unit 101 measures the MPPs at the left and right eyes of the user's face (i.e., 2 channels), and the MPPs at one of the left and right zygomaticus minor, left and right zygomaticus major, and left and right risorius muscles (i.e., 2 channels), totaling 4 channels. For example, the MPPs at the eyes may be the MPPs at the orbicularis oculi muscle, the MPPs at the frontalis muscle, etc. (Hereinafter, the orbicularis oculi muscle will be described).

[0039] The registration unit 102 stores the magnitude of the user's electromyographic potentials (e.g., maximum and minimum values ​​(or amplitudes)) in the electromyographic data storage unit 105. In one embodiment of the invention, the maximum and minimum values ​​(or amplitudes) of the user's electromyographic potentials when the user is smiling are pre-registered, and when the user's electromyographic potentials approach the maximum and minimum values ​​(or amplitudes), it is possible to determine whether the smile is attractive.

[0040] The electromyography data storage unit 105 stores information related to muscle potentials. The following explanation is divided into <<the maximum and minimum values ​​(or amplitudes) of the user's muscle potentials>> and <<characteristic quantities of the muscle potentials of an attractive smiling face>>.

[0041] <<Maximum and minimum values ​​(or amplitudes) of the user's myoelectric potentials>>

[0042] The electromyography data storage unit 105 stores the maximum and minimum values ​​(or amplitudes) of the user's electromyography potentials stored by the registration unit 102.

[0043] <<Characteristics of the electromyographic properties of an attractive smiling face>>

[0044] The electromyography data storage unit 105 stores characteristic quantities of the electromyography (EMG) that are objectively judged to be attractive smiles (e.g., the integral value of the EMG (size of the smile), the rate of rise, the correlation between the eyes and mouth, etc.).

[0045] The judgment unit 103 determines whether the difference between the characteristic quantity of the myoelectric potential measured by the measurement unit 101 and the characteristic quantity of the myoelectric potential that is objectively judged to be an attractive smiling face (e.g., the integral value of the myoelectric potential (size of the smiling face), the rate of rise, the correlation between the eyes and mouth, etc.) stored in the electromyography data storage unit 105 is within a predetermined range.

[0046] Specifically, the determination unit 103 extracts feature quantities from the electromyographic potentials measured by the measurement unit 101. For example, the determination unit 103 determines whether the difference between the integral value of the electromyographic potentials at each of the four locations on the user's face (representing the size of the smile, as described later) and the size of the smile stored in the electromyographic data storage unit 105 is within a predetermined range. Additionally, the determination unit 103 determines whether the difference between the rate at which the electromyographic potentials at each of the four locations on the user's face reach their maximum value (representing the rate of ascent, as described later) and the rate of ascent stored in the electromyographic data storage unit 105 is within a predetermined range. Furthermore, the determination unit 103 determines whether the difference between the strength of the correlation between the electromyographic potentials at the major orbicularis oculi muscle and the electromyographic potentials at at least one of the zygomaticus minor, zygomaticus major, and risorius muscles (representing the eye-mouth correlation, as described later) and the strength of the eye-mouth correlation stored in the electromyographic data storage unit 105 is within a predetermined range. Moreover, the determination unit 103 may determine only the integral value of the electromyographic potentials (size of the smile), only the rate of ascent, or only the eye-mouth correlation.

[0047] In this way, the judgment unit 103 can determine whether the characteristic quantity of the myoelectric potential when the user is smiling is within a predetermined range for judging an attractive smile.

[0048] <<Pre-determined scope>>

[0049] The predetermined range can be determined based on the results of machine learning, or it can be determined by humans.

[0050] The output unit 104 outputs the difference determined by the judgment unit 103 as being within or outside a predetermined range. Specifically, when the judgment unit 103 determines that the difference is within or outside the predetermined range, the output unit 104 notifies the user by generating sound, vibration, light, or a combination thereof. The output unit 104 immediately notifies the user based on the result of the judgment unit 103.

[0051] <Characteristic quantities of electromyography>

[0052] Here, we will explain the characteristic quantities of myoelectric potentials that are objectively judged as attractive smiles. The following explanations are divided into <<Integral value of myoelectric potential (size of the smile)>>, <<Rise rate>>, <<Correlation between eyes and mouth>>, and <<Other>>.

[0053] <<Integral value of myoelectric potential (size of smiley face)>>

[0054] For example, the characteristic quantity of the electromyographic activity (EMG) that objectively determines an attractive smile is the integral value of the EMG. More specifically, the characteristic quantity is the integral value of the EMG at four locations (or two locations) on the user's face. The integral value of the EMG represents the size of the smile. By using the size of the smile, smiles that are not sufficiently large can be removed from the list of attractive smiles.

[0055] <<Speed ​​of Ascent>>

[0056] For example, the characteristic quantity of the electromyographic activity (EMG) of an objectively attractive smile is the rate at which the EMG reaches its maximum value. More specifically, the characteristic quantity is the rate at which the EMG at each of the four (or two) locations on the face reaches its maximum value. The rate at which the EMG at each of the four locations on the face reaches its maximum value represents the rate until the smile reaches its maximum. By using the rate of increase, smiles with a slow rate of increase (i.e., unnatural smiles that reach their maximum value slowly) can be removed from attractive smiles.

[0057] <<The Connection Between Eyes and Mouth>>

[0058] For example, a key characteristic of the electromyographic activity (EMG) in objectively judging an attractive smile is the strength of the correlation between the EMGs around the eyes and those around the mouth. More specifically, the characteristic is the strength of the correlation between the EMGs at the primary orbicularis oculi muscle and those at least one of the zygomaticus minor, zygomaticus major, and risorius muscles. The strength of the correlation between the EMGs at the primary orbicularis oculi muscle and those at least one of the zygomaticus minor, zygomaticus major, and risorius muscles represents the strength of the correlation between eye and mouth movements during a smile. By using the eye-mouth correlation, smiles with weak eye-mouth correlation (i.e., unnatural smiles where the eyes and mouth are not correlated (e.g., the eyes are not moving) can be removed from attractive smiles.

[0059] <<Other>>

[0060] For example, the characteristic quantities of the electromyographic activity (EMG) that objectively indicate an attractive smile can be the magnitude of the EMG, the timing of the EMG measurement, the pattern of EMG changes over time, and the rate of decline (that is, information on the rate at which the EMG at each of the four locations on the face returns from a smiling state to a blank expression). Furthermore, the characteristic quantities of the EMG that objectively indicate an attractive smile can also be a combination of characteristic quantities from multiple types of EMG, the average of the characteristic quantities across the four channels (the four locations on the face), and the deviation, etc.

[0061] The following is a reference. Figures 3-9 On the one hand, an explanation was given regarding the experiment that discovered the characteristic quantities of myoelectric potential.

[0062] First, videos of each of the five participants (3 in their 20s and 2 in their 40s) smiling in seven different ways were filmed. Then, evaluators (40 people) watched the videos and rated the naturalness of the smiles (out of 11 levels: 5 for naturalness, 5 for neither natural nor unnatural, and 5 for unnaturalness) and the attractiveness of the smiles (out of 11 levels: 5 for attractiveness, 5 for neither attractive nor unattractive, and 5 for unattractiveness). Figure 3 and Figure 4 This is a diagram illustrating the results of evaluating the smiles of subjects according to an embodiment of the present invention.

[0063] like Figure 3 As shown, the evaluation assessed whether the smiles of each of the five subjects (Subject No. 1, Subject No. 2, Subject No. 3, Subject No. 4, and Subject No. 5) were natural and attractive. The horizontal axis represents the seven types of smiles, and the vertical axis represents the average evaluation from 40 evaluators.

[0064] like Figure 4 As shown, data from all smiles of all subjects were used to investigate the correlation between the degree of naturalness / unnaturalness and the degree of attractiveness / unattractiveness of smiles, and the results showed a correlation. In other words, it was determined that the more natural a smile is, the more attractive it is. Therefore, in this invention, to detect natural smiles, the myoelectric potentials of the user's face are used to determine whether the user is smiling.

[0065] Figure 5 This is a graph illustrating the results of evaluating the attractiveness of subjects' smiles according to an embodiment of the present invention. It shows the evaluation results (40 evaluators) of the attractiveness of each subject's smile (smile #1, smile #2, smile #3, smile #4, smile #5, smile #6, smile #7) by seven evaluators (subject No.1, subject No.2, subject No.3, subject No.4, and subject No.5). The horizontal axis represents attractiveness (that is, the degree of attractiveness and the degree of lack of attractiveness (furthermore, standardized based on standard deviation)), and the vertical axis represents the number of evaluators who evaluated that attractiveness. Figure 5 As shown, the evaluation of the attractiveness of a smiley face is subject to evaluator bias. Therefore, in one embodiment of the present invention, as... Figure 6 As shown, considering evaluator bias, the attractiveness of smiles can be evaluated with approximately 89% accuracy. Figure 6 In this context, 25%, 50%, and 75% represent the upper 25%, 50% (median), and 75% levels of the scores from 40 evaluators. Here, the estimated value falling within the 25-75% range is considered "correct" to determine the precision.

[0066] Figure 7 This is a diagram used to illustrate myoelectric potentials according to one embodiment of the present invention. In one embodiment of the present invention, as a characteristic quantity of the myoelectric potential, it is possible to use... Figure 7 That kind of integrated electromyography. In Figure 7 In this context, the initiation time is the period until the myoelectric potentials at each of the four facial locations reach their maximum value. Conversely, the release time is the period until the myoelectric potentials at each of the four facial locations return from a smiling state to a blank expression.

[0067] Figure 8 This is a diagram used to illustrate eight characteristic quantities related to one embodiment of the present invention. The myoelectric potentials (MPPs) at the main orbicularis oculi muscles of the left and right sides of the face (i.e., 2 channels) and the myoelectric potentials (i.e., 2 channels) at one of the left and right zygomaticus minor muscles, left and right zygomaticus major muscles, and left and right risorius muscles, totaling 4 channels.

[0068] iEMG is the integrated value of the myoelectric potentials at four different locations on the face (that is, each of the four channels) (i.e., the size of the smile). iEMG is Figure 7 The area of ​​the slanted portion.

[0069] iEMG(min) represents the minimum integrated value of the myoelectric potential in each channel.

[0070] iEMG(ave) represents the average of the integrated values ​​of the myoelectric potentials of each channel across the four channels.

[0071] iEMG(dev) represents the variance among the four channels of the integral value of the myoelectric potential in each channel.

[0072] iEMG(w1) is obtained by weighting the latter half of the measurement time.

[0073] iEMG(w2) is obtained by weighting the first half of the measurement time.

[0074] B(ave) represents the speed at which the myoelectric potential at each of the four locations on the face (that is, each of the four channels) reaches its maximum value (that is, the speed at which the smile reaches its maximum). More specifically, it is the average of the speed at which the myoelectric potential in each channel reaches its maximum value across the four channels.

[0075] B(dev) represents the strength of the correlation between the electrical potential at the main orbicularis oculi muscle and the electrical potentials at at least one of the zygomaticus minor, zygomaticus major, and risorius muscles (that is, the strength of the correlation between eye movements and mouth movements when smiling). More specifically, it is the variance among the four channels of the velocity at which the electrical potentials in each channel reach their maximum value. This can be interpreted as: a smaller variance indicates a stronger correlation, and a larger variance indicates a weaker correlation.

[0076] Among the characteristic quantities of myoelectric potentials, those related to the attractiveness of a smile are as follows: Figure 9 That's how it was discovered.

[0077] Figure 9 This is a diagram illustrating the relationship between the attractiveness of a smile and the size, rate of increase, and eye-mouth characteristics of a smile, according to an embodiment of the present invention. Figure 9 As shown, the attractiveness of a smiley face is related to its size (iEMG), rate of increase (B(ave)), and the relationship between the eyes and mouth (B(dev)).

[0078] Thus, it is known that the size of the smile (iEMG), the rate of rise (B(ave)), and the correlation between the eyes and mouth (B(dev)) among the characteristic quantities of facial electromyography are related to the attractiveness of the smile.

[0079] <Method>

[0080] The following is a reference. Figure 10 While explaining the registration process, refer to... Figure 11 The explanation covers smile detection and processing.

[0081] <<Registration Processing>>

[0082] Figure 10 This is a flowchart of the registration process according to one embodiment of the present invention.

[0083] In step 11 (S11), the measurement unit 101 measures the myoelectric potentials (MPPs) of the user's face when the user is smiling (e.g., when the user displays their most attractive smile). For example, the measurement unit 101 measures the MPPs of the main orbicularis oculi muscles on the left and right sides of the user's face (i.e., 2 channels), as well as the MPPs of one of the left and right zygomaticus minor muscles, left and right zygomaticus major muscles, and left and right risorius muscles (i.e., 2 channels).

[0084] In step 12 (S12), the registration unit 102 stores the magnitude of the user's electromyographic potential (e.g., maximum and minimum values ​​(or amplitude)) in the electromyographic data storage unit 105.

[0085] <<Smile Detection and Processing>>

[0086] Figure 11 This is a flowchart of a smile detection process according to an embodiment of the present invention.

[0087] In step 21 (S21), the measurement unit 101 measures the myoelectric potentials of the user's face. For example, the measurement unit 101 measures the myoelectric potentials of the main orbicularis oculi muscles on the left and right sides of the user's face (that is, 2 channels), and the myoelectric potentials of one of the left and right zygomaticus minor muscles, the left and right zygomaticus major muscles, and the left and right risorius muscles (that is, 2 channels).

[0088] In step 22 (S22), the determination unit 103 extracts the characteristic quantities of the myoelectric potential in S21.

[0089] In step 23 (S23), the determination unit 103 determines whether the difference between the characteristic quantity of the electromyography extracted in S22 and the registered characteristic quantity (that is, the characteristic quantity stored in the electromyography data storage unit 105) is within a predetermined range. If it is within the predetermined range, the process proceeds to step 24. If it is not within the predetermined range, the process ends (or, it may output that it is not within the predetermined range).

[0090] In step 24 (S24), the difference between the output unit 104 and the output of S23 is within a predetermined range.

[0091] In one embodiment of the present invention, an electromyography (EMG) meter 10 can be used to support the expression of an attractive smile. Specifically, the EMG meter 10 can measure the electromyographic activity of a user's face, and the user can be notified of an attractive smile based on the output of the EMG meter 10.

[0092] <Effect>

[0093] Thus, in one embodiment of the present invention, since the user is notified in real time the instant they become an attractive smiley face, the user can notice "what kind of feeling" or "what kind of mood" led to the smiley face. By mentally remembering the "event" and "moment" in which they become an attractive smiley face, the user can naturally become an attractive smiley face.

[0094] <Hardware Structure>

[0095] Figure 12 This is a block diagram illustrating an example of the hardware structure of a myoelectric meter 10 and a smile detection device 20 according to an embodiment of the present invention.

[0096] The myoelectric potential meter 10 has a measuring device 1010 for measuring myoelectric potentials.

[0097] The electromyography (EMG) meter 10 and the smile detection device 20 have a CPU (Central Processing Unit) 1001, a ROM (Read Only Memory) 1002, and a RAM (Random Access Memory) 1003. The CPU 1001, ROM 1002, and RAM 1003 form what is called a computer.

[0098] Additionally, the electromyography (EMG) meter 10 and the smile detection device 20 may include an auxiliary storage device 1004, a display device 1005, an operation device 1006, an I / F (Interface) device 1007, and a drive device 1008. Furthermore, the hardware components of the EMG meter 10 and the smile detection device 20 are interconnected via bus B.

[0099] CPU 1001 is a computing device that executes various programs installed in auxiliary storage device 1004.

[0100] ROM 1002 is a non-volatile memory. ROM 1002 functions as the main storage device for storing various programs and data required by the CPU 1001 to execute various programs installed in the auxiliary storage device 1004. Specifically, ROM 1002 functions as the main storage device for storing boot programs such as BIOS (Basic Input / Output System) and EFI (Extensible Firmware Interface).

[0101] RAM1003 is a volatile memory such as DRAM (Dynamic Random Access Memory) or SRAM (Static Random Access Memory). RAM1003 functions as a main storage device that provides the working area for various programs installed on the auxiliary storage device 1004 to be executed by the CPU 1001.

[0102] The auxiliary storage device 1004 is an auxiliary storage device that stores various programs and information used when executing various programs.

[0103] The display device 1005 is a display device that displays the internal state of the electromyography meter 10 and the smile detection device 20.

[0104] The operating device 1006 is an input device that manages the electromyography meter 10 and the smile detection device 20 and inputs various instructions to the electromyography meter 10 and the smile detection device 20.

[0105] I / F device 1007 is a communication device for connecting to a network and communicating with electromyography device 10 and smile detection device 20.

[0106] The drive device 1008 is a device used to set the storage medium 1009. The storage medium 1009 mentioned here includes media that record information optically, electrically, or magnetically, such as CD-ROMs, floppy disks, and optical discs. Alternatively, the storage medium 1009 may also include semiconductor memories that record information electrically, such as EPROMs (Erasable Programmable Read Only Memory) and flash memory.

[0107] Furthermore, various programs installed on the auxiliary storage device 1004 can be installed by the drive device 1008, for example, by setting the drive device 1008 with the distributed storage medium 1009, or by reading the various programs recorded on the storage medium 1009. Alternatively, various programs installed on the auxiliary storage device 1004 can also be installed by downloading them from the network via the I / F device 1007.

[0108] The embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific embodiments described above, and various modifications and alterations can be made within the scope of the spirit of the present invention as set forth in the claims.

[0109] This international application claims priority based on Japanese Patent Application No. 2021-043016, filed on March 17, 2021, the entire contents of which are incorporated herein by reference.

[0110] Explanation of reference numerals in the attached figures

[0111] 1. Smile Detection System

[0112] 10. Electromyography device

[0113] 20 Smile Detection Device

[0114] 101 Measurement Department

[0115] Registration Department 102

[0116] 103 Judgment Department

[0117] 104 Output Section

[0118] 105 Electromyography Data Storage Department

[0119] 1001 CPU

[0120] 1002 ROM

[0121] 1003 RAM

[0122] 1004 Auxiliary storage device

[0123] 1005 Display Device

[0124] 1006 Operating Device

[0125] 1007 I / F device

[0126] 1008 drive unit

[0127] 1009 Storage Media

[0128] 1010 Measuring Device

Claims

1. A myoelectric potential meter, which is a myoelectric potential meter for measuring myoelectric potentials, wherein, have: The judgment unit determines whether the difference between the characteristic quantity of the myoelectric potential measured by the myoelectric potential meter and the characteristic quantity of the myoelectric potential objectively judged as an attractive smiling face is within a predetermined range. and The output unit outputs whether the difference is within or outside a predetermined range. The characteristic quantity of the myoelectric potential is the rate at which the myoelectric potential at various locations on the user's face reaches its maximum value, or the strength of the correlation between the myoelectric potential around the eyes and the myoelectric potential at at least one of the zygomaticus minor, zygomaticus major, and risorius muscles.

2. The electromyography device according to claim 1, The characteristic quantities of the myoelectric potential also include the magnitude of the myoelectric potential, the pattern of change of the myoelectric potential over time, the rate of decrease, the combination of multiple types of this information, and the information on the average and deviation of the information across multiple locations on the user's face.

3. The electromyography device according to claim 1, The characteristic quantity of the myoelectric potential is the integral value of the myoelectric potential at various locations on the user's face.

4. The electromyography device according to any one of claims 1 to 3, The output unit immediately notifies the user based on the result of the judgment unit.

5. The electromyography device according to any one of claims 1 to 3, The output unit notifies the user by generating sound, vibration, light, or combinations thereof.

6. The electromyography device according to any one of claims 1 to 3, The predetermined range is determined based on the results of machine learning, which uses the output to objectively determine whether it is an attractive smiley face when the difference is input.

7. The electromyography device according to any one of claims 1 to 3, The "multiple locations" include the area around the eyes, and at least two of the following muscles: the zygomaticus minor, the zygomaticus major, and the risorius.

8. A method, performed by a myoelectric potentiometer for measuring myoelectric potentials, wherein, include: The step of determining whether the difference between the characteristic quantity of the myoelectric potential measured by the myoelectric potential meter and the characteristic quantity of the myoelectric potential objectively judged as an attractive smiling face is within a predetermined range; and The step of outputting the difference as being within or outside a predetermined range. The characteristic quantity of the myoelectric potential is the rate at which the myoelectric potential at various locations on the user's face reaches its maximum value, or the strength of the correlation between the myoelectric potential around the eyes and the myoelectric potential at at least one of the zygomaticus minor, zygomaticus major, and risorius muscles.

9. A system comprising a myoelectric potential meter for measuring myoelectric potentials and a computer, wherein, have: The judgment unit determines whether the difference between the characteristic quantity of the myoelectric potential measured by the myoelectric potential meter and the characteristic quantity of the myoelectric potential objectively judged as an attractive smiling face is within a predetermined range. and The output unit outputs whether the difference is within or outside a predetermined range. The characteristic quantity of the myoelectric potential is the rate at which the myoelectric potential at various locations on the user's face reaches its maximum value, or the strength of the correlation between the myoelectric potential around the eyes and the myoelectric potential at at least one of the zygomaticus minor, zygomaticus major, and risorius muscles.

10. One approach is to use an electromyography (EMG) device to support the expression of an attractive smile. in, The electromyography device includes: The judgment unit determines whether the difference between the characteristic quantity of the myoelectric potential measured by the myoelectric potential meter and the characteristic quantity of the myoelectric potential objectively judged as an attractive smiling face is within a predetermined range. and The output unit outputs whether the difference is within or outside a predetermined range. The characteristic quantity of the electromyography (EMG) is the rate at which the EMG at various locations on multiple parts of the user's face reaches its maximum value, or the strength of the correlation between the EMG around the eyes and the EMG at at least one of the zygomaticus minor, zygomaticus major, and risorius muscles. The method includes: The steps of using the electromyography device to measure the electromyographic potentials of the user's face; and The step of notifying the user of an attractive smiling face based on the output of the electromyography device.

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