Patent Application
20230021605
The present invention relates to a system for assisting in health maintenance and promotion in an oral cavity and a throat region in order to extend healthy life expectancy, and particularly relates to a system for assisting in and supporting improvement of quality of chewing as an “function of chewing and eating deliciously”.
Chewing food, swallowing behavior, salivation, and the like exert a great influence on the brain and the whole body, and exert a great influence on physical and mental health and healthy life expectancy. Health maintenance and enhancement in an oral cavity and a throat region are considered to consequently extend healthy life expectancy.
Particularly, sufficient chewing of solid meals is considered to lead to promotion of physical and mental growth, brain activation, enhancement of motor function, obesity inhibition, aging prevention, and sociality maintenance, to exhibit an effect of extending healthy life expectancy. Insufficient chewing such as the small number of chewing times in intake of a meal leads to deterioration of a chewing function of a growing child, and oral frailty of elderly people (see Non-patent Literature 1).
Furthermore, “partial chewing” in which chewing is performed always on the same side affects teeth and the jaw, the face, and the like to, for example, shorten lifetimes of teeth on one side, easily soil teeth by which chewing is not performed, apply a load onto the jaw joint, or distort the face, and also affects the whole body to result in, for example, distortion of the body or stiff shoulders or low back pain. Balance (occlusal interference) in occlusion between the left side and the right side is also considered to be associated with physical and affective stress, and affects both sympathetic nerve and parasympathetic nerve functions.
A device such as an electromyograph for counting the number of chewing times and a device for numerically indicating an occlusal force have been provided for measuring chewing quality. However, a simple system that can accurately obtain in detail quality of complicated chewing behavior having complex aspects, has not been provided.
Therefore, the present invention has been made in order to overcome the aforementioned circumstances, and an object of the present invention is to provide a simple system that can accurately obtain in detail quality of complicated chewing behavior having complex aspects, and that is a chewing assistance system capable of accurately supporting improvement of chewing quality, and health maintenance and promotion.
In view of the aforementioned circumstances, the inventor of the present invention has found, as a result of thorough study, that chewing behavior and chewing quality can be accurately analyzed and determined in detail by, for example, frequency-analyzing a muscle activity signal obtained by an electromyograph or the like during a meal and utilizing a power value in a specific frequency band that is dominant particularly in activity during chewing, and assistance in improvement of chewing quality and health maintenance and promotion can be performed based on the determination result, to complete the present invention.
That is, the present invention includes the following inventive aspects.
(1) A chewing assistance system including an information processing device that includes: chewing information storage means that stores information about chewing quality; muscle activity obtaining means that obtains a muscle activity signal of masticatory muscle of a person; analysis means that frequency-analyzes the muscle activity signal obtained by the muscle activity obtaining means, and analyzes chewing behavior based on the frequency-analyzed muscle activity signal; quality determination means that determines quality of the chewing behavior based on information of the chewing behavior analyzed by the analysis means; and extraction means that extracts assistance information corresponding to the chewing quality determined by the quality determination means, from the chewing information storage means.
(2) In the chewing assistance system according to the above-described (1), the analysis means frequency-analyzes the muscle activity signal and analyzes the chewing behavior based on a change state of a power value in a specific frequency band.
(3) In the chewing assistance system according to the above described (2), the analysis means analyzes the chewing behavior based on an envelope obtained by performing, for each block, fast Fourier transform of electromyogram data as the muscle activity signal, by using the envelope as the change state.
(4) In the chewing assistance system according to the above described (2) or (3), the analysis means determines that chewing is performed, when the change state indicates a value that exceeds a predetermined threshold value.
(5) In the chewing assistance system according to the above-described (4), as to the threshold value, chewing is determined to be performed when an integral value calculated as the change state from the envelope exceeds a predetermined threshold value.
(6) In the chewing assistance system according to the above-described (2) or (3), the analysis means analyzes chewing balance between a left side and a right side according to the change state of the muscle activity signal of the masticatory muscle on each of the left side and the right side.
(7) In the chewing assistance system according to the above-described (3), the analysis means analyzes characteristics of a masticatory substance based on a gradient and a duration of a chewing section in which chewing is determined to be performed from the change state of the envelope.
(8)
The chewing assistance system according to the above-described (3) includes a user information storage unit that stores correlation between values of an occlusal force and values of a muscle activity of a user, the correlation being acquired by obtaining a value of a muscle activity during eating of prescribed food having such known characteristics that an occlusal force required for biting-through is obtained as a fixed value. The analysis means analyzes the occlusal force during chewing, based on the correlation and a value in a chewing section in which chewing is determined to be performed from the change state of the envelope.
(8) In the chewing assistance system according to any one of the above-described (1) to (7), the analysis means includes a machine learning mechanism, and the chewing behavior is determined with reference to a learning result from the machine learning mechanism.
(9) In the chewing assistance system according to any one of the above-described (1) to (8), the chewing behavior analyzed by the analysis means includes behavior representing at least one of a total number of chewing times, chewing rhythm, transition of occlusal actions during a meal, an occlusal force level, chewing balance between anterior and posterior sides/between left and right sides, and characteristics of a masticatory substance.
(10) In the chewing assistance system according to any one of the above-described (1) to (9), the quality of the chewing behavior to be determined by the quality determination means includes quality based on at least one of determinations as to whether a total number of chewing times is large or small, whether chewing rhythm is proper, whether transition of occlusal actions is proper, whether an occlusal force is proper, whether chewing balance between a left side and a right side is proper, whether diet is unbalanced, and whether or not use of masseter is proper.
(11) In the chewing assistance system according to any one of the above-described (1) to (10), the quality determination means compares a chewing behavior with a previous chewing behavior of a same person and determines whether the chewing behavior has improved.
(12) In the chewing assistance system according to any one of the above-described (1) to (11), the quality determination means has a machine learning mechanism, and the quality of the chewing behavior is determined with reference to a learning result from the machine learning mechanism.
A chewing assistance program including a control program for causing an information processing device to function as the chewing assistance system according to any one of the above-described (1) to (12), the chewing assistance program causing the information processing device to function as the muscle activity obtaining means, the analysis means, the quality determination means, and the extraction means.
According to the present invention described above, a muscle activity signal is frequency-analyzed, chewing behavior is analyzed based on the frequency-analyzed muscle activity signal, quality of the chewing behavior is determined, and assistance information corresponding to the determined chewing quality can be provided. Therefore, a simple system that can obtain in detail quality of complicated chewing behavior having complex aspects can be provided to accurately support improvement of chewing quality, and health maintenance and promotion.
The present invention having such a configuration can provide the system that can accurately provide growth information about quality of healthy chewing for growing children, and that contributes to healthy development of a chewing action function for the growing children. Also for elderly people, the present invention can provide the system that can accurately provide assistance information corresponding to chewing quality and that contributes to maintenance and enhancement of a chewing action function for elderly people
Next, an embodiment of the present invention will be described in detail with reference to the accompanied drawings.
Chewing during a meal is associated with a person's favorite hardness/softness of food, a motion of biting through and masticating the food, the number of times of chewing the food, a chewing time, rhythm, and the like. Balance between chewing teeth is also among them. For determining chewing quality based on whether or not such a function of chewing and eating deliciously is proper, the system of the present invention frequency-analyzes a muscle activity signal of masticatory muscle, and analyzes chewing behavior such as the number of chewing times, chewing rhythm, transition of occlusal actions, an occlusal force, chewing balance between a left side and a right side, unbalanced diet, and a way of using masseter, so as to determine the chewing quality, and to indicate change with the elapse of time according to difference between the past state and the present state, for example. Therefore, the system of the present invention can present an improving state of the chewing quality.
Specifically, as shown in
The processing unit 2 includes a CPU such as a microprocessor as a main unit and also has a not-illustrated storage unit, such as a RAM and a ROM, in which a program for providing procedures of various processing operations, and process data are stored. The storage means 3 includes a memory, a hard disk, and the like disposed inside and/or outside the information processing device 10. A part or all of contents in the storage unit may be stored in, for example, a hard disk or a memory of another computer that is connected to the information processing device 10 so as to be communicable with each other. The information processing device having such a configuration may be a dedicated device that is installed in a dental clinic, a hospital, another institution, a store, or the like, or may be a general-purpose household personal computer. The information processing device may be, for example, a smartphone carried by a user.
The processing unit 2 includes, as its functions, a muscle activity obtaining unit 21 as muscle activity obtaining means, an analysis unit 22, a quality determination unit 23 as quality determination means, an information extraction unit 24, and an information output processing unit 25. The muscle activity obtaining unit 21 obtains a muscle activity signal, of masticatory muscle of a user, which is obtained and transmitted by the muscle activity measurement unit 4, and stores the muscle activity signal in a muscle activity data storage unit 31a of a user information storage unit 31. The analysis unit 22 frequency-analyzes the muscle activity signal, analyzes chewing behavior based thereon, and stores information of the analyzed chewing behavior in a chewing behavior storage unit 31b of the user information storage unit 31. The quality determination unit 23 determines chewing quality based on the information of the chewing behavior, and stores information of the determined chewing quality in a determination information storage unit 31c of the user information storage unit 31. The information extraction unit 24 receives input of the information of the determined chewing quality, and extracts information to be recommended from information, about the chewing quality, stored in a chewing information storage unit 32. The information output processing unit 25 presents the information to the user by, for example, displaying the information on a display (information display unit 5). These processing functions are executed by the above-described program.
The muscle activity measurement unit 4 corresponds to an electromyograph or the like, and preferably includes communication means capable of performing short-range radio transmission and reception of data to and from a smartphone of a user as the information processing device 10. The muscle activity measurement unit 4 also corresponds to, for example, an external electromyograph that is wired-connected or wirelessly connected to, for example, a dedicated computer device as the information processing device 10. A muscle activity signal of masticatory muscle of a user is obtained by the muscle activity measurement unit 4.
The muscle activity measurement unit 4 measures a muscle activity of at least one of four masticatory muscles which are temporal muscles and masseters on both sides of a head portion in order to obtain the muscle activity signal. At least two muscle activities to be compared with each other are measured in order to measure balance during chewing. That is, muscle activities of at least left and right temporal muscles or at least left and right masseters are to be obtained in order to measure balance between the left side and the right side. Muscle activities of at least temporal muscle and masseter on the left side or at least temporal muscle and masseter on the right side are to be obtained in order to measure balance between the anterior side and the posterior side.
The analysis unit 22 functions as analysis means, and frequency-analyzes the muscle activity signal obtained by the muscle activity obtaining unit 21, and analyzes chewing behavior based on a change state of a power value in a specific frequency band. The analysis can be more accurately performed by thus utilizing the power value in the specific frequency band (for example, 150 Hz to 450 Hz) that is particularly dominant in activity during chewing.
More specifically, an FFT processing unit 22a and a behavior analysis processing unit 22b are provided. The FFT processing unit 22a obtains data of the muscle activity signal from the muscle activity data storage unit 31a, performs fast Fourier transform for each block, obtains an average power value in a specific frequency band, and stores the power value in a power value storage unit 311, and further generates an envelope of the obtained power value (hereinafter, simply referred to as “envelope” in the description herein), and stores the envelope in an envelope storage unit 312. The behavior analysis processing unit 22b analyzes the chewing behavior and stores the result in an analysis result storage unit 313.
A specific example of the process performed by the FFT processing unit 22a is as follows. The process is performed on the assumption that the muscle activity measurement unit is a device that performs sampling at 2000 samples/second. Firstly, the FFT processing unit 22a divides raw data (2000 samples/second) of the muscle activity signal into blocks each including a predetermined number of samples (64 samples in this example) and performs fast Fourier transform for each block.
In this example, in the fast Fourier transform for each block, 0 to 1000 Hz is equally divided into 32 to set 32 pins (frequency), and a power value for each predetermined number of pins is calculated. Each pin represents a frequency (integer multiple) for each 31.25 Hz. The FFT processing unit 22a further calculates an average value of, for example, eight power values in a specific frequency band (between 7 pin and 14 pin, that is, between 218.75 and 437.5 Hz in this example) for each block, and outputs the average value as an average power value of each block. The power value represents amplitude of a frequency spectrum at a specific frequency.
For example,
The muscle activity data (raw data) stored in the muscle activity data storage unit 31a is preferably deleted from the storage means 3 at a time when the analysis result is stored in the analysis result storage unit 313 from the viewpoint of reducing a storage region.
The behavior analysis processing unit 22b analyzes various chewing behaviors by, for example, using such an envelope generated by the FFT processing unit 22a, and stores the chewing behaviors in the analysis result storage unit 313. The analyzed chewing behaviors represent, for example, the number of chewing times, chewing rhythm, transition of occlusal actions during a meal, an occlusal force level, chewing balance between the anterior and posterior sides/between the left and right sides, and characteristics of a masticatory substance. In this example, the analysis of the chewing behavior is performed on the assumption that the behavior analysis processing unit 22b includes a chewing determination unit 221 for determining whether or not the chewing is performed. The chewing determination unit 221 determines that chewing is performed when the envelope indicates a value that exceeds a predetermined threshold value. Specifically, the chewing is determined as follows.
(Chewing Determination)
Preferably, a background is firstly calculated from an envelope, among envelopes, in a definite non-chewing section in which a muscle activity (average power value) is small and stable, and a value obtained by multiplying the background by a coefficient is set as a threshold value for determining the chewing. When the threshold value is exceeded under a certain condition, chewing is determined to be performed.
Specifically, the value of the background can be firstly calculated through low pass filter processing of the envelope. The filter may be a primary autoregressive filter represented by the following equation.
Y
n=0.99Yn-1+0.01Xn-80
“Xn-80” represents a value of an envelope that has been obtained 2.56 seconds earlier. In this equation, “2.56” represents a value obtained as 80 samples/31.25 samples/s=2.56 s. “Yn-1” represents the latest value of the background level, and “Yn” represents anew value of the background level. “0.99” represents a filter constant, and “0.01” is for ensuring the total gain with a gain factor (gain coefficient) of an input signal.
The calculation is preferably performed by integers in order to reduce a calculation load on a built-in processor. This can be performed by performing multiplication of a value from FFT algorithm at a magnification of 10000 (8 bit algorithm). Furthermore, the above-described filter is obtained by the following equation.
Y
n=(99Yn-1+Xn-80)/100
Preferably, the value of the background is not calculated until a predetermined time elapses from detection of the end of chewing after detection of the start of the chewing, and the background level obtained before the detection of the start of the chewing is maintained.
As shown in
As another method for determining whether or not chewing is performed, a method in which the background is set as a value of a moving average of the envelopes in a certain section as represented by the following equation, is considered. Similarly to the above-described calculation of the background, a value that has been obtained 2.56 seconds earlier (80 samples) is set as a threshold value for the present time, and a background threshold value to be adopted for the average value can be set (for example, 1.2 times).
Y
n
=X
n-80+4σn-80
In the equation, “Yn” represents a new value of the background level, and Xn-80 represents a value of a moving average of an envelope which has been obtained 2.56 seconds earlier. “2.56” represents a value obtained as 80 samples/31.25 samples/s=2.56 s. The value of the moving average represents an envelope obtained before a time point of the calculation (10 samples/31.25 samples/s=320 ms earlier), “a” represents a standard deviation, and “4” represents a deviation coefficient.
In this case, also for a threshold value for determining chewing, for example, a value obtained by multiplying the background average value and the deviation by a predetermined deviation coefficient (for example, 4) is preferably set as the threshold value.
Although the start of chewing is detected when the envelope indicates a value greater than the threshold value for a predetermined time period, it is also preferable as an embodiment that, as shown in
In the “background”, vibration is repeated in a certain range even in a state where a muscular action of a human body does not occur, and a potential (noise) in the resting state is “background (noise)”. However, as in the present invention, in the chewing activity, a muscle activity which does not exceed the threshold value for determination occurs due to human reaction (shake the head, have an intake of breath) other than chewing, and when such a muscle activity occurs over a certain time period, the threshold value is increased by using only the above-described method for calculating the threshold value, and the determination of chewing may be hindered depending on the degree.
Specifically, since the calculation of the threshold value is stopped only when the muscle activity indicates a value that exceeds the threshold value, a muscle activity event determination threshold value is increased due to the following two influences. (1) a case where, although a muscle activity event indicates a value exceeding the threshold value for a moment, the duration is short and this is not counted as the event, and (2) a case where a small-scale muscle activity occurs for a short time period. The increase of the threshold value in these cases may lead to erroneous determination with respect to chewing to be actually counted and causes chewing strength to be calculated as an excessively small value.
Therefore, the threshold value is preferably calculated as follows. That is, a fluctuation range of an envelope in a certain time period is calculated, and if abrupt change occurs such that the fluctuation range exceeds a predetermined value, calculation of the threshold value is stopped in the section. Meanwhile, if the fluctuation is maintained so as to be gentle such that the fluctuation range of an envelope in a certain time period does not exceed the predetermined value, calculation of the threshold value is performed in the section. Thus, increase of the threshold value due to the influence in the above-described case (1) or (2) is inhibited, and a stable threshold value can be obtained, and the muscle activity event determination threshold value can accurately follow gentle increase of a myogenic potential and increase of a myogenic potential over a long time period.
(Balance Analysis)
In the present embodiment, the behavior analysis processing unit 22b further includes a balance analysis unit 222 for analyzing chewing balance among the anterior, the posterior, the left, and the right sides. For example, for chewing balance between the left side and the right side, the balance analysis unit 222 calculates one or both of the maximum peak value and an integral value of each of envelopes that are generated in the same one chewing section from muscle activity data of the same masticatory muscles (temporal muscles/masseters) on the left side and the right side, and performs comparison to determine difference between the left-side chewing force and the right-side chewing force. In a case where the difference in force exceeds a predetermined threshold value, or in a case where such chewing continues a plural number of times, chewing is determined to be performed predominantly on the left side or the right side in an unbalanced manner.
Even in partial chewing in which food is continuously bit by teeth on one side, the muscle activities on the left side and the right side indicate almost the same tendencies in masticatory muscle (temporal muscle and masseter) in a healthy condition. Therefore, it is difficult to obtain difference in predominance between the left side and the right side directly from an original signal (raw data) of the muscle activity. In the balance analysis using frequency analysis as in the present embodiment, one, of muscles on the left side and the right side, which is predominantly used can be accurately determined in the analysis.
For chewing balance between the anterior side and the posterior side, the maximum peak values of the envelopes which are generated in the same one chewing section from muscle activity data of temporal muscle and masseter are calculated and compared with each other. If the peak value of the temporal muscle is less than the peak value on the masseter side by a predetermined threshold value or more, or if such chewing continues a plural number of times, chewing can be determined to be performed predominantly on the anterior teeth side in an unbalanced manner.
(Masticatory Substance Characteristics Analysis)
In the present embodiment, the behavior analysis processing unit 22b further incudes a masticatory substance characteristics analysis unit 223 for analyzing characteristics (texture: physical characteristics such as hardness and softness) of a chewed food material. The masticatory substance characteristics analysis unit 223 can analyze the above-described characteristics from a gradient and a duration of a chewing section of an envelope. There is a tendency that the harder the food material is, the greater the gradient in the chewing section is. Characteristics of a masticatory substance, that is, a degree of hardness/softness of the food material can be determined according to the gradient.
The shape in each of chewing sections of envelopes which are obtained from the muscle activity data when a plurality of kinds of foods (prescribed food) having known characteristics are chewed, is stored as a reference shape, and, for example, pattern analysis is performed by using the shape, whereby the characteristics of the masticatory food can be determined. Furthermore, it is also preferable that determination is performed by a machine learning mechanism by using, as training data, the shape (feature point such as a gradient and a peak) of the envelope obtained when a user or the like has chewed the above-described prescribed food.
(Occlusal Force Analysis Unit)
In the present embodiment, the behavior analysis processing unit 22b further includes an occlusal force analysis unit 224 for analyzing an occlusal force at the time of chewing. Relationship between muscle activity data and an occlusal force is different depending on a person. That is, even when chewing is performed at the same occlusal force, a value of the muscle activity is different depending on a person. Therefore, in the present embodiment, a correlation table representing correlation between values (the above-described power values) of the muscle activity and values of an occlusal force is previously generated for the user, and stored in the user information storage unit 31.
The correlation table is generated by obtaining muscle activity data (power values) when a user bites through a plurality of kinds of foods (prescribed foods) having known characteristics as described above. The hardness of the prescribed food, that is, an occlusal force required for biting through the prescribed food is obtained as a fixed value. Therefore, the muscle activity data (power value) obtained when the prescribed food is bit through corresponds to the occlusal force value obtained from the food in one-to-one relationship.
Therefore, the occlusal force analysis unit 224 converts a power value (average value or maximum value) of an envelope in a chewing section to an occlusal force by using the correlation table, and can thus analyze the occlusal force at the time of chewing. The graph in
According to a modification of a technique for generating the correlation table, by connecting a muscle activity measurement unit and an occlusal force measurement unit used in combination, correlation between the occlusal force and the muscle activity data may be directly obtained.
(Chewing Action Analysis)
In the present embodiment, the behavior analysis processing unit 22b further includes a chewing action analysis unit 225 for analyzing a chewing-and-crushing action, a masticating action, a grinding action, and the like. The chewing action analysis unit 225 determines that chewing is performed by a chewing-and-crushing action when the gradient is small and time is long in the chewing section, whereas the chewing action analysis unit 225 determines that chewing is performed by a masticating action when the gradient is great and time is short. In a case where the chewing action is determined based on the data such as a gradient as described above, it is also preferable that one chewing is divided into a plurality of sections, and displacement or the like in a specific section or each section is used to perform analysis in more detail.
If such a chewing-and-crushing action or a masticating action can be analyzed, transition of these chewing actions can be further analyzed. In a meal, the action typically shifts in the order of putting of a food material in the mouth, a chewing-and-crushing action, a masticating action, a grinding or merging action, and a swallowing action. If such an action can be determined, a series of actions from putting of a food material in the mouth to swallowing can be grasped and eating behavior (behavior characteristics) such as a speed and a habit of eating a meal can be determined.
The quality of the chewing behavior determined by the quality determination unit 23 includes, for example, qualities based on determination as to whether the number of chewing times is large or small, whether or not chewing rhythm is proper, whether or not transition of occlusal actions is proper, whether or not an occlusal force is proper, whether or not chewing balance between the left side and the right side is proper, whether or not diet is unbalanced, or whether or not use of masseter is proper.
For example, information as to whether or not a user has improved chewing quality as compared with the her/his previous quality and information as to whether or not the chewing quality is commensurate with the age are preferably obtained so as to be included in the quality, according to the obtained data, previous information for the user in the determination information storage unit 31c, and age-based statistical information. Preferably, the quality determination unit 23 has a machine learning mechanism 23a, and determines the quality of the chewing behavior, with reference to learning results from the machine learning mechanism 23a.
The information extraction unit 24 functions as extraction means. For example, if the chewing quality is not commensurate with the age, the information extraction unit 24 preferably extracts information such as age-based oral cavity function information, and information about a device for growing/improving purpose and a medical specialist based on a residence of the user. According thereto, it is also preferable that the improvement is suggested so as to, for example, more slowly perform chewing and/or chew harder food.
What the problem is (force, chewing behavior, or a chewing place) can be presented to a user, and a person who cannot make proper use although the user has teeth and a potential for healthy chewing, is supported so as to improve the chewing quality. Furthermore, chewing behavior is considered to be particularly important for children. For example, grating or chattering in chewing can be pointed out to promote improvement. Furthermore, for elderly people, a way of using masticatory muscle is considered to be particularly important, and a kind of masticatory muscle used, a load thereon, and the like can be determined and presented.
Firstly, the muscle activity obtaining unit 21 obtains, from the muscle activity measurement unit 4, muscle activity data of masticatory muscle of a user at least from putting of the prescribed food (predetermined food) or ordinary food in the mouth up to swallowing (S101), and stores the muscle activity data in the muscle activity data storage unit 31a of the user information storage unit 31 (S102).
Subsequently, the FFT processing unit 22a performs fast Fourier transform of the muscle activity data for each block and thus obtains an average power value in a specific frequency band (S103), stores the average power value in the power value storage unit 311 (S104), generates an envelope of the obtained power value (S105), and stores the envelope in the envelope storage unit 312 (S106).
Subsequently, the behavior analysis processing unit 22b analyzes chewing behavior based on the envelope (S107), and stores the result in the analysis result storage unit 313 (S108). Subsequently, the quality determination unit 23 determines quality of the chewing behavior based on the analysis result (S109), and stores information of the determined quality of the chewing behavior in the determination information storage unit 31c of the user information storage unit 31 (S110).
Subsequently, the information extraction unit 24 receives input of the information of the determined chewing quality and extracts information to be recommended from information of chewing quality stored in the chewing information storage unit 32 (S111). The information output processing unit 25 presents the extracted information to the user by, for example, displaying the information on a display (the information display unit 5) (S112).
Although the embodiment of the present invention has been described above, the present invention is not limited to the embodiment at all. For example, instead of the processing unit being configured by software processing performed by a computer, it is also preferable that a part or the entirety of the processing unit is configured by a hardware processing circuit. In this case, a processing circuit for artificial intelligence can also be used as the machine learning mechanism, and it is needless to say that the present invention can be implemented in various modes without departing from the gist of the present invention.
The present invention allows quality of complicated chewing behavior having complex aspects to be accurately determined in detail, and allows assistance information based on the determination result to be provided. Therefore, by combining the present invention with instruments and commodities/services for education and training of chewing for children, commodities and services contributing to healthy development of children can be provided. Furthermore, by combining the present invention with cosmetic training instruments and services for, for example, use of masticatory muscle and well-balanced chewing among the anterior, the posterior, the left, and the right sides, cosmetic commodities and services for preventing distortion of the face and obesity, and maintaining vital healthy facial expression can also be provided. Moreover, by combining the present invention with commodities and services for addressing oral frailty such as deterioration of an oral cavity function and weakening of the body for elderly people and the like, commodities and services contributing to extension of healthy life expectancy can also be provided.
| Number | Date | Country | Kind |
|---|---|---|---|
| 2019-237303 | Dec 2019 | JP | national |
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/JP2020/048015 | 12/22/2020 | WO |
