Chewing assistance system

Abstract

The present invention is provided with: a chewing information storage means (31b) for storing information relating to the quality of chewing; a muscular activity acquisition means (21) for acquiring a muscular activity signal from a human masticatory muscle; an analysis means (22) for subjecting the muscular activity signal, acquired by the muscular activity acquisition means, to frequency analysis and analyzing a chewing mode on the basis thereof; a quality determination means (23) for determining the quality of the chewing mode on the basis of information about the chewing mode analyzed by the analysis means; and an extraction means (24) for extracting, from the chewing information storage means (32), assistance information corresponding to the quality of chewing determined by the quality determination means.

Description

[Technical Field]

[0001] The present invention relates to a system that supports the maintenance and promotion of oral and pharyngeal health with the aim of extending healthy life expectancy, and in particular to a system that supports and assists in improving the quality of mastication, which is the "function of chewing and eating deliciously." [Background technology]

[0002] Chewing food, swallowing, saliva secretion, and other processes have a significant impact on the brain and the entire body, significantly affecting both physical and mental health and healthy lifespan. Maintaining and improving the health of the oral cavity and pharyngeal region is said to ultimately extend healthy lifespan.

[0003] In particular, sufficient chewing of chewy food is believed to be effective in extending healthy lifespan by promoting physical and mental growth, activating the brain, improving motor function, preventing obesity, slowing aging, and maintaining sociality. Insufficient chewing, such as eating food with few chews, can lead to a decline in the chewing function of developing children and oral frailty in the elderly (see Non-Patent Document 1).

[0004] Furthermore, the effects of "biased chewing," which is always chewing on the same side, are not limited to the teeth, jaw, and face, but can also eventually affect the whole body, causing distortion of the body, stiff shoulders, and back pain. The imbalance between the left and right occlusions (occlusal interference) is also said to cause physical and emotional stress, affecting both the sympathetic and parasympathetic nervous systems.

[0005] There are devices to measure the quality of chewing, such as electromyographs that count chewing movements and devices that quantify bite force, but a simple system that can accurately grasp the quality of complex chewing patterns with multiple aspects in detail has not yet been provided. [Prior art documents] [Patent documents]

[0006] [Patent Document 1] Japanese Patent Application Publication No. 6-98865 [Patent Document 2] Japanese Patent Application Publication No. 2019-47859

[0007] [Non-Patent Document 1] Yoshinori Kobayashi, Commissioned paper: Occlusion and mastication create a healthy life expectancy, Journal of the Japanese Society of Prosthodontics Ann Jpn Prosthodont Soc 3, p189-219, 2011 Summary of the Invention [Problem to be solved by the invention]

[0008] In view of the above situation, the present invention aims to solve the problem of providing a mastication support system that is a simple system that can accurately grasp the quality of complex mastication patterns with multiple aspects in detail, and that can accurately support the improvement of mastication quality and the maintenance and promotion of health. [Means for solving the problem]

[0009] In light of this current situation, the inventors have conducted extensive research and have found that by frequency analyzing muscle activity signals obtained during eating using an electromyograph or the like, and in particular by utilizing the power values ​​of specific frequency bands in which activity is particularly dominant during chewing, it is possible to analyze and assess in detail and accurately the manner and quality of chewing, and based on the assessment results, it is possible to support improvement in the quality of chewing and the maintenance and promotion of health, thereby completing the present invention.

[0010] That is, the present invention includes the following inventions. (1) A chewing assistance system comprising an information processing device including: a chewing information storage means for storing information relating to the quality of chewing; a muscle activity acquisition means for acquiring muscle activity signals of a person's chewing muscles; an analysis means for frequency-analyzing the muscle activity signals acquired by the muscle activity acquisition means and analyzing the chewing behavior based on the frequency analysis; a quality determination means for determining the quality of the chewing behavior based on the information on the chewing behavior analyzed by the analysis means; and an extraction means for extracting, from the chewing information storage means, assistance information according to the quality of chewing determined by the quality determination means.

[0011] (2) The mastication assistance system according to (1), wherein the analyzing means performs frequency analysis on the muscle activity signal and analyzes the mastication behavior based on the change in power value of a specific frequency band.

[0012] (3) The mastication assistance system according to (2), wherein the analyzing means analyzes the mastication behavior based on an envelope obtained by performing a fast Fourier transform on each block of electromyogram data as a muscle activity signal, and uses this as the changing state.

[0013] (4) The mastication assistance system according to (2) or (3), wherein the analyzing means determines that mastication is occurring when the change in the state of the mouth exceeds a predetermined threshold.

[0014] (5) The mastication assistance system according to (4), wherein mastication is determined to be occurring when an integral value calculated from the envelope as the change state exceeds a predetermined threshold.

[0015] (6) The mastication assistance system according to (2) or (3), wherein the analyzing means analyzes the left-right mastication balance from the change in the muscle activity signals of the same masticatory muscles on the left and right.

[0016] (7) The mastication assistance system according to (3), wherein the analyzing means analyzes the characteristics of the object being masticated based on the gradient and duration of a mastication section that is determined to be mastication from the change in the envelope. (8) The chewing assistance system according to (3) is provided with a user information storage unit that stores the correlation between the user's muscle activity value and the bite force value, which is obtained by acquiring the muscle activity value when eating a prescribed food that has a known characteristic that the bite force required to bite through is fixed at a constant value, and the analysis means analyzes the bite force during chewing based on the value of the chewing section that is determined to be chewing from the change in the envelope and the correlation.

[0017] (8) The mastication assistance system according to any one of (1) to (7), wherein the analysis means has a machine learning mechanism and determines the mastication behavior by referring to a learning result of the machine learning mechanism.

[0018] (9) A chewing assistance system according to any one of (1) to (8), wherein the chewing behavior analyzed by the analysis means includes behaviors related to at least one of the number of chews, chewing rhythm, progression of chewing movements during a meal, degree of chewing force, front-to-back / left-to-right chewing balance, and characteristics of the object being chewed.

[0019] (10) A chewing support system according to any one of (1) to (9), wherein the quality of the chewing manner judged by the quality judging means includes at least one of the number of chews, the quality of the chewing rhythm, the quality of the progression of the occlusal movement, the quality of the occlusal force, the quality of the balance between the left and right chewing movements, the presence or absence of imbalance in the food intake, and the quality of the use of the masseter muscles.

[0020] (11) The mastication assistance system according to any one of (1) to (10), wherein the quality evaluation means includes a comparison with the past mastication behavior of the same person and a judgment as to whether or not there has been improvement.

[0021] (12) The mastication assistance system according to any one of (1) to (11), wherein the quality determining means has a machine learning mechanism and determines the quality of the mastication behavior by referring to a learning result of the machine learning mechanism.

[0022] A control program for causing an information processing device to function as a chewing support system described in any one of (1) to (12), the chewing support program causing the information processing device to function as the muscle activity acquisition means, analysis means, quality determination means, and extraction means. [Effects of the Invention]

[0023] According to the present invention as described above, muscle activity signals are frequency analyzed, and based on this, the chewing pattern is analyzed to determine the quality of the chewing pattern, and support information can be provided according to the determined chewing quality. Therefore, a simple system can be provided that can grasp the quality of complex chewing patterns with multiple aspects in detail, and can provide appropriate support for improving the quality of chewing and maintaining and promoting health.

[0024] According to the present invention, it is possible to provide accurate information on healthy development of masticatory quality, particularly for children during their developmental period, and to provide a system that contributes to the healthy development of masticatory motor functions in children during their developmental period. Also, for the elderly, it is possible to provide a system that contributes to the maintenance and improvement of masticatory motor functions in the elderly by providing accurate support information according to the quality of mastication. [Brief explanation of the drawings]

[0025] [Figure 1] FIG. 1 is a block diagram showing the configuration of a mastication assistance system according to a representative embodiment of the present invention. [Figure 2A] Raw data of muscle activity signals from the left masticatory muscles. [Figure 2B] Raw data of muscle activity signals from the right masticatory muscles. [Figure 3A] Heatmap showing the frequency distribution of muscle activation power values ​​on the left. [Figure 3B] Heatmap showing the frequency distribution of muscle activation power values ​​on the right. [Figure 4] FIG. 10 is an explanatory diagram showing a method for determining a chewing period from an envelope curve. [Figure 5] Graph showing raw data of muscle activity signals of masticatory muscles and the envelope obtained by FFT processing of the raw data. [Figure 6] 10 is a graph showing raw data of a chewing section, each area integral value of an envelope curve, and bite force of the chewing section. [Figure 7] This is a graph showing the raw data of muscle activity signals on the left and right when chewing with a bias towards the left teeth, and the envelope obtained by FFT processing this data. [Figure 8] This is a graph showing the raw data of muscle activity signals on the left and right when chewing with a bias towards the right teeth, and the envelope obtained by FFT processing this data. [Figure 9] 1 is a graph showing raw data of muscle activity signals of the temporalis muscle and masseter muscle when chewing with the back teeth and the envelope obtained by FFT processing of the raw data. [Figure 10] 1 is a graph showing raw data of muscle activity signals of the temporalis muscle and masseter muscle when chewing with the front teeth, and the envelope obtained by FFT processing of the raw data. [Figure 11A] A graph showing raw data of muscle activity signals with different chewing speeds and rhythms, and the envelopes obtained by FFT processing of this data. [Figure 11B] A graph showing raw data of muscle activity signals with different chewing speeds and rhythms, and the envelopes obtained by FFT processing of this data. [Figure 12] FIG. 1 is a flowchart showing a processing procedure of a mastication assistance system according to a representative embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0026] Next, an embodiment of the present invention will be described in detail with reference to the accompanying drawings.

[0027] Chewing during a meal involves factors such as a person's preferred hardness or softness of food, the movements of tearing and chewing, the number of times chewing, the duration of chewing, and rhythm. Another factor is the balance of the teeth used for chewing. The quality of mastication is defined as the ability to chew and eat deliciously. This system analyzes the frequency of the masticatory muscle activity signals to analyze masticatory behavior, including the number of chews, chewing rhythm, progression of occlusal movements, bite force, left-right chewing balance, food imbalance, and use of the masseter muscles. It then assesses the quality of mastication and can show improvements in masticatory quality by showing changes over time based on the difference between past and present results.

[0028] Specifically, as shown in Fig. 1, the mastication assistance system 1 of the present invention is composed of one or more information processing devices 10, each of which includes a processing device 2, a storage means 3, a muscle activity meter 4, and an information display unit 5. Specifically, the information processing device 10 is a computer or the like that includes the processing device 2 at the center, a storage means, an input means such as a pointing device, a keyboard, or a touch panel, a display means such as a display, and a communication control unit (not shown).

[0029] The processing device 2 is mainly composed of a CPU such as a microprocessor, and has a storage unit consisting of RAM and ROM (not shown) in which programs defining the procedures of various processing operations and processing data are stored. The storage means 3 consists of a memory or hard disk inside or outside the information processing device 10. Some or all of the contents of the storage unit may be stored in the memory or hard disk of another computer communicatively connected to the information processing device 10. Such an information processing device may be a dedicated device installed in a dental clinic, hospital, other facility, store, etc., or may be a general-purpose personal computer installed in the home. It may also be a smartphone carried by the user.

[0030] The processing device 2 functionally comprises a muscle activity acquiring unit 21 as muscle activity acquiring means that acquires muscle activity signals of the user's masticatory muscles acquired and transmitted by the muscle activity meter 4 and stores the signals in a muscle activity data storage unit 31a in a user information storage unit 31; an analyzing unit 22 that performs frequency analysis of the muscle activity signals, analyzes the mastication pattern based on the frequency analysis, and stores information on the analyzed mastication pattern in a mastication pattern storage unit 31b in the user information storage unit 31; and an analyzing unit 23 that determines the quality of mastication based on the information on the mastication pattern. The device is equipped with a quality judgment unit 23 as quality judgment means that performs processing to store information on the judged chewing quality in a judgment information storage unit 31c in the user information storage unit 31, an information extraction unit 24 that receives the information on the judged chewing quality as input and extracts recommended information from the information on the chewing quality stored in the chewing information storage unit 32, and an information output processing unit 25 that presents the information to the user by displaying it on a display (information display unit 5), etc., and these processing functions are realized by the above program.

[0031] The muscle activity meter 4 corresponds to an electromyograph or the like, and is preferably equipped with a communication means capable of wirelessly transmitting and receiving data over short distances to a user's smartphone constituting the information processing device 10. It also corresponds to an external electromyograph connected by wire or wirelessly to a dedicated computer device or the like constituting the information processing device 10. The muscle activity meter 4 acquires muscle activity signals from the user's masticatory muscles.

[0032] The muscle activity meter 4 acquires muscle activity signals from at least one of the four masticatory muscles, the temporal muscles and masseter muscles on both sides of the head. To measure balance during mastication, at least two muscle activities are measured for comparison. That is, to measure left-right balance, at least the muscle activities of the left and right temporal muscles or the left and right masseter muscles are acquired. To measure front-to-back balance, at least the muscle activities of the left temporal muscle and masseter muscle or the right temporal muscle and masseter muscle are acquired.

[0033] The analysis unit 22 functions as an analysis means, performs frequency analysis of the muscle activity signals acquired by the muscle activity acquisition unit 21, and analyzes the chewing behavior based on the change in the power value in a specific frequency band. In this way, by utilizing the power value in a specific frequency band (for example, 150 Hz to 450 Hz) where activity is particularly dominant during chewing, more accurate analysis becomes possible.

[0034] More specifically, the system is equipped with an FFT processing unit 22a that retrieves muscle activity signal data from the muscle activity data storage unit 31a, performs a fast Fourier transform on each block to obtain average power values ​​in a specific frequency band, stores these in a power value storage unit 311, and also creates an envelope of the obtained power values ​​(hereinafter simply referred to as an "envelope") and stores it in an envelope storage unit 312, and a behavior analysis processing unit 22b that analyzes the chewing behavior and stores the results in an analysis result storage unit 313.

[0035] A specific example of processing by the FFT processing unit 22a is as follows: Assuming that the muscle activity meter is a device that samples at 2000 samples / second, the FFT processing unit 22a first divides the raw data of the muscle activity signal (2000 samples / second) into blocks of a predetermined number of samples (64 samples in this example) and performs a fast Fourier transform on each block.

[0036] In this example, the Fast Fourier Transform of each block is performed by dividing 0 to 1000 Hz into 32 equal parts, setting 32 pins (frequencies), and calculating the power value for each of the predetermined number of pins. Each pin has a frequency of 31.25 Hz (an integer multiple). The FFT processing unit 22a then calculates the average value of, for example, eight power values ​​in a specific frequency band (here, between pins 7 and 14, i.e., 218.75 to 437.5 Hz) for each block, and outputs this as the average power value for each block. The power value is the amplitude of the frequency spectrum at the specific frequency.

[0037] Figures 2A and 2B show raw data (2000 samples / second) of muscle activity signals from the left and right masticatory muscles, and Figures 3A and 3B show heat maps of the frequency distribution of power values ​​obtained by fast Fourier transforming the raw data for each block by the FFT processing unit 22a according to the specific example described above. Figures 2A and 3A show the data / heat maps on the left side, and Figures 2B and 3B show the data / heat maps on the right side. These heat maps show that taking the average of the power values ​​between pins 7 and 14 allows for more accurate determination of the masticatory state from the muscle activity signals. However, pin ranges other than "7 to 14" are also acceptable; for example, "6 to 14" or "6 to 15" are also preferable.

[0038] 5 shows an example of an envelope curve connecting the average power values ​​for each block (every 32 ms) output by the FFT processing unit 22a after fast Fourier transforming the raw data according to the above example. This envelope curve is a graph that more accurately reflects the chewing behavior than a graph of raw muscle activity data. By using this envelope curve, the chewing behavior can be more accurately determined using data in the frequency band related to chewing.

[0039] It is preferable to delete the muscle activity data (raw data) stored in the muscle activity data storage unit 31a from the storage means 3 at the time the analysis results are stored in the analysis result storage unit 313, as this leads to a reduction in storage area.

[0040] The behavior analysis processing unit 22b analyzes various chewing behaviors by using the envelope curve created by the FFT processing unit 22a, and stores the results in the analysis result storage unit 313. Examples of chewing behaviors to be analyzed include the number of chews, chewing rhythm, the progression of occlusal movements during a meal, the degree of occlusal force, the front-back / left-right chewing balance, and the characteristics of the object being chewed. In this example, as a prerequisite for analyzing the chewing behavior, the behavior analysis processing unit 22b is provided with a chewing determination unit 221 that determines whether or not chewing is occurring. The chewing determination unit 221 determines that chewing is occurring when the envelope curve exceeds a predetermined threshold. In more detail, the following applies:

[0041] (Chewing judgment) It is preferable to first calculate the background from the envelope of the section where muscle activity (average power value) is small and stable and clearly indicates non-mastication, and then multiply that background by a coefficient to set the threshold for determining whether mastication is occurring. Anything that exceeds this threshold under certain conditions is then determined to be mastication.

[0042] Specifically, the background value can be calculated by low-pass filtering the envelope. The filter can be a first-order autoregressive filter of the following formula: Y n =0.99Y n-1 +0.01X n-80

[0043] "X n-80 " is the envelope value 2.56 seconds ago. Here, "2.56" is the value calculated by 80samples / 31.25samples / s=2.56s. "Y n-1 ” is the latest value of the background level, “Y n" is the new value for the background level. "0.99" is the filter constant and "0.01" is the gain factor of the input signal to ensure an overall gain of 1.

[0044] The calculations are preferably performed in integer arithmetic to reduce the computational load on the embedded processor. This can be done by multiplying the values ​​from the FFT algorithm by a factor of 10000 (8-bit algorithm). Furthermore, the filter described above is calculated using the following formula: Y n =(99Y n-1 +X n-80 ) / 100

[0045] It is preferable that the background value is not calculated after the start of mastication is detected until a predetermined time has elapsed since the end of mastication is detected, and the background level before the start of mastication is maintained.

[0046] As shown in FIG. 4, the threshold value is the background level multiplied by a predetermined value (for example, 2.6 times the background level). The start and end of mastication are preferably detected when the envelope exceeds and falls below the threshold for two sample times (64 ms), respectively. In this way, for example, the mastication determination unit 221 determines whether or not mastication is occurring. Accordingly, information on the number of times, speed, and rhythm of mastication can also be analyzed. As shown in FIGS. 11A and 11B, mastication speeds and rhythms vary.

[0047] Another method for determining whether chewing is occurring is to use the background as a moving average value of a certain section of the envelope, as shown in the following formula: As with the background calculation described above, the current threshold value is set to 2.56 seconds ago (80 samples), and a background threshold can be set (for example, 1.2 times) to use as the average value.

[0048] Y n =X n-80 +4σ n-80 Here, "Y n” is the new value of the background level, X n-80 is the moving average value of the envelope 2.56 seconds ago. Here, "2.56" is the value calculated by 80 samples / 31.25 samples / s = 2.56 s. The moving average value is the envelope from 10 samples / 31.25 samples / s = 320 ms ago before the calculation time, "σ" is the standard deviation, and "4" is the deviation coefficient.

[0049] . In this case, it is also preferable that the threshold value for determining whether or not the sample is being chewed is set to, for example, a value obtained by multiplying the deviation from the background average value by a predetermined deviation coefficient (for example, 4).

[0050] Furthermore, the start of mastication is detected when the envelope curve exceeds a threshold value for a predetermined time, but it is also a preferred embodiment to further calculate the area integral value of the envelope curve of the mastication section and set an integral threshold value, and to exclude from mastication when the integral value of the envelope curve of a mastication section detected as mastication falls below the integral threshold value, as shown in Figure 6. In this way, short and weak sections (sections surrounded by A in Figure 5 and sections surrounded by A' in Figure 6) among the mastication sections determined in Figure 5 can be excluded from the scope of mastication, and only reliable mastication actions can be determined and counted as mastication.

[0051] Incidentally, the "background" refers to the fact that even when there is no muscle activity, the human body repeatedly vibrates within a certain range, and this resting potential (noise) is referred to as the "background (noise)." However, in chewing activities such as those of the present invention, muscle activity that does not exceed the judgment threshold occurs due to human reactions other than chewing (such as shaking the head or gasping), and if such muscle activity occurs over a certain period of time, the threshold will rise using only the threshold calculation method described above, and depending on the degree of this, it may interfere with the judgment of chewing.

[0052] Specifically, because the calculation of the threshold stops only when muscle activity exceeds the threshold, the muscle activity event determination threshold increases due to the following two influences: (1) when a muscle activity event exceeds the threshold for a moment but is not counted as an event because its duration is short, and (2) when small-scale muscle activity occurs for a short period of time. An increase in the threshold due to these factors can lead to incorrect determination of chewing that should actually be counted, and to undercalculation of chewing strength.

[0053] Therefore, it is preferable to calculate the threshold as follows: That is, the fluctuation range of the envelope within a certain period of time is calculated, and if the fluctuation range changes abruptly and exceeds a predetermined value, the calculation of the threshold for that period is stopped. On the other hand, if the fluctuation range of the envelope within the certain period of time is only a gradual change that does not exceed the predetermined value, the calculation of the threshold for that period is executed. This makes it possible to suppress the increase in the threshold due to the effects of (1) and (2) above, obtain a stable threshold, and enable the muscle activity event determination threshold to accurately track a gradual increase in myoelectric potential and a long-term increase in myoelectric potential.

[0054] (Balance analysis) The behavior analysis processing unit 22b of this embodiment further includes a balance analysis unit 222 that analyzes the front-back and left-right chewing balance. For example, regarding the left-right chewing balance, the balance analysis unit 222 determines the magnitude of the left-right chewing force by calculating and comparing one or both of the integral value and the maximum peak value per chewing section of each envelope created from the muscle activity data of the same left and right chewing muscles (temporalis muscle / masseter muscle). If the difference between the magnitudes exceeds a predetermined threshold, or if the chewing continues multiple times, it can be determined that the chewing is biased to the left or right.

[0055] Figure 7 shows the left and right envelope curves when chewing is biased to the left. It is clear from the figure that the integral and peak values ​​of each chewing section are larger on the left side. Conversely, Figure 8 shows the envelope curve when chewing is biased to the right side, with both the integral and peak values ​​being larger on the right side. In this way, it can be seen that the left-right balance can be analyzed by comparing the integral values ​​or maximum peak values ​​of the left and right envelope curves of the chewing section.

[0056] Even if you are chewing food using only one tooth, the masticatory muscles (temporalis and masseter) tend to exhibit similar muscle activity on the left and right sides in a healthy state. Therefore, it is difficult to directly determine the dominance of the left and right sides from the raw muscle activity signal (raw data). By performing a balance analysis using frequency analysis as in this example, it is possible to accurately analyze which muscle is used more dominantly, left or right.

[0057] Regarding the balance between front and rear chewing, the maximum peak value per chewing section of each envelope created from muscle activity data of the temporalis and masseter muscles is calculated and compared, and if the peak value of the temporalis muscle is smaller than the peak value of the masseter muscle by a predetermined threshold or more, or if this chewing continues multiple times, it can be determined that chewing is biased toward the front teeth.

[0058] Figure 9 shows the envelope curves of muscle activity data for the temporalis and masseter muscles when chewing using the back teeth. As is clear from the figure, the temporalis and masseter muscles are active at the same level, with similar peak values ​​for both in each chewing interval. Figure 10 shows the envelope curves for chewing primarily with the front teeth, with the temporalis muscle activity clearly lower and the maximum peak value between each chewing interval clearly lower for the temporalis muscle than for the masseter muscle. In this way, by comparing the maximum peak values ​​of the envelope curves of the temporalis and masseter muscles in each chewing interval, it is possible to analyze whether chewing is biased toward the front teeth.

[0059] (Analysis of chewable material characteristics) The behavior analysis processing unit 22b of this embodiment further includes a chewed object characteristic analysis unit 223 that analyzes the characteristics of the chewed food material (texture: physical characteristics such as hardness, softness, etc.). The chewed object characteristic analysis unit 223 can analyze the above characteristics from the gradient and duration of the chewing section of the envelope. The harder the food material is, the greater the gradient of the chewing section tends to be, and this gradient can be used to determine the characteristics of the food material being masticated, i.e., the degree of hardness / softness of the food material.

[0060] Furthermore, the shape of each chewing section of the envelope curve obtained from muscle activity data when chewing multiple types of food (prescribed foods), each with known characteristics, can be stored as a basic shape, and the characteristics of the chewed food material can be determined by pattern analysis, etc. Furthermore, it is also preferable to use the shape of the envelope curve (feature points such as slope and peaks) when a user or the like chews the prescribed foods as training data and make a determination using a machine learning mechanism.

[0061] (occlusal force analysis department) The behavior analysis processing unit 22b of this embodiment further includes an occlusal force analysis unit 224 that analyzes occlusal force during chewing. The relationship between muscle activity data and occlusal force differs from person to person. That is, even when chewing with the same occlusal force, muscle activity values ​​differ from person to person. Therefore, in this embodiment, a correlation table between muscle activity values ​​(the above-mentioned power values) and occlusal force values ​​is created in advance for the user and stored in the user information storage unit 31.

[0062] This correlation table is created by obtaining muscle activity data (power values) when a user bites through multiple types of food (prescribed foods) with known characteristics. Since the hardness of prescribed foods, i.e., the bite force required to bite through them, is set to a fixed value, the muscle activity data (power values) when biting through these prescribed foods will correspond one-to-one to the bite force values ​​obtained from the foods.

[0063] Therefore, the bite force analysis unit 224 can analyze the bite force during chewing by converting the power values ​​(average and maximum values) of the envelope of the chewing section into bite force using the correlation table. The graph in Figure 6 shows the bite force obtained from the envelope of Figure 5. In this example, multiple types of prescribed foods are chewed in advance to obtain the correlation table, but instead of using such a table, it is also possible to approximate the correlation as a proportional relationship and obtain a correlation coefficient (proportionality constant) from one or multiple types of prescribed foods.

[0064] As a modified method for creating the correlation table, it is also possible to directly obtain the correlation between bite force and muscle activity data by connecting both a muscle activity meter and a bite force meter.

[0065] (Chewing motion analysis) The behavior analysis processing unit 22b of this embodiment further includes a chewing action analysis unit 225 that analyzes chewing actions, crushing actions, grinding actions, etc. The chewing action analysis unit 225 determines that chewing is performed by a crushing action when the gradient of the chewing section is small and the time is long, and determines that chewing is performed by a crushing action when the gradient is large and the time is short. When determining chewing action using data such as gradient, it is also preferable to divide one chewing action into multiple sections and perform a more detailed analysis using a specific section or the amount of displacement between each section.

[0066] If we can analyze these chewing and crunching movements, we can further analyze the progression of these chewing movements. A meal usually progresses in the order of putting ingredients in the mouth, chewing, crushing, grinding or gathering, and swallowing. If we can determine this, we can understand the series of movements from putting ingredients in the mouth to swallowing, and we can determine eating habits (behavioral characteristics) such as eating speed and eating habits.

[0067] The quality of the chewing pattern judged by the quality judgment unit 23 includes the number of chews, the quality of the chewing rhythm, the quality of the progression of the occlusal movement, the quality of the occlusal force, the quality of the balance of the left and right chewing, whether or not the food is eaten unbalanced, and the quality of the use of the masseter muscles.

[0068] It is preferable that the quality determination unit 23 includes information such as whether the user has improved the quality of mastication compared to the past, whether the user has mastication quality appropriate for their age, etc., based on the obtained data, the user's past information in the determination information storage unit 31c, and statistical information according to age. It is preferable that the quality determination unit 23 has a machine learning mechanism 23a and determines the quality of the mastication behavior by referring to the learning results of the machine learning mechanism 23a.

[0069] The information extraction unit 24 functions as an extraction means, and preferably extracts information such as age-appropriate oral function information, development / improvement equipment, and specialist information appropriate to the user's residence, if the user does not have appropriate chewing quality for their age. It is also preferable to provide suggestions for improvement, such as chewing more slowly or chewing harder foods.

[0070] It can also show users what the problem is (force, chewing method, or chewing location), helping to improve the quality of chewing for people who have teeth and the potential for healthy chewing but are not using them properly. Chewing method is considered particularly important for children, and it can point out things like grinding or hard chewing, encouraging improvement. Furthermore, it is considered particularly important for elderly people to use their masticatory muscles, and it can determine the type of masticatory muscles being used, the amount of load, and other factors and provide suggestions.

[0071] FIG. 12 is a flowchart showing the processing procedure performed by the mastication assistance system 1 of this embodiment.

[0072] First, the muscle activity acquisition unit 21 acquires muscle activity data of the user's masticatory muscles from the muscle activity meter 4, from the time when at least a prescribed food (predetermined food) or a normal meal is put into the mouth until it is swallowed (S101), and stores the data in the muscle activity data storage unit 31a in the user information storage unit 31 (S102).

[0073] Next, the FFT processing unit 22a performs a fast Fourier transform on the muscle activity data for each block to obtain average power values ​​in a specific frequency band (S103), stores these in the power value storage unit 311 (S104), and creates an envelope of the obtained power values ​​(S105) and stores them in the envelope storage unit 312 (S106).

[0074] Next, the behavior analysis processing unit 22b analyzes the chewing behavior based on the envelope curve (S107) and stores the result in the analysis result storage unit 313 (S108). Next, the quality determination unit 23 determines the quality of the chewing behavior based on the analysis result (S109) and stores information on the determined quality of the chewing behavior in the determination information storage unit 31c in the user information storage unit 31 (S110).

[0075] Next, the information extraction unit 24 receives the determined information on the quality of mastication as an input and extracts recommended information from the information on the quality of mastication stored in the mastication information storage unit 32 (S111). Then, the information output processing unit 25 presents the extracted information to the user by displaying it on a display (information display unit 5) or the like (S112).

[0076] Although the embodiments of the present invention have been described above, the present invention is not limited to these examples. For example, instead of configuring the processing device using software processing by a computer, it is preferable to configure part or all of it using hardware processing circuits. In this case, an artificial intelligence processing circuit can be used as the machine learning mechanism, and it goes without saying that the present invention can be embodied in various forms within the scope of the gist of the present invention. [Industrial Applicability]

[0077] The present invention can accurately and precisely assess the quality of complex chewing patterns, which have multiple aspects, and provide support information based on the assessment results. Therefore, by combining it with tools, products, and services for children's chewing education and chewing training, it is possible to provide products and services that contribute to children's healthy development. Furthermore, by combining it with beauty training tools and services that teach balanced chewing from left to right and front to back and how to use masticatory muscles, it is possible to provide beauty products and services that prevent facial distortion and obesity and maintain a lively and healthy expression. Furthermore, by combining it with products and services that address oral frailty, such as decreased oral function and physical decline in the elderly, it is possible to provide products and services that contribute to extending healthy lifespan. [Explanation of symbols]

[0078] 1. Chewing assistance system 2 Processing equipment 3 Memory means 4 Muscle activity meter 5 Information display section 10. Information processing equipment 21 Muscle activity acquisition section 22 Analysis Department 22a FFT processing section 22b Behavior analysis processing unit 23 Quality Judgment Department 23a Machine Learning Mechanism 24 Information extraction part 25 Information output processing section 31 User information storage unit 31a Muscle activity data storage unit 31b Chewing behavior memory unit 31c Judgment information storage unit 32 Chewing information storage unit 221 Chewing judgment section 222 Balance Analysis Section 223 Chewable product characteristics analysis department 224 Occlusal force analysis department 225 Chewing motion analysis section 311 Power value storage unit 312 Envelope storage section 313 Memory section of the parsing results

Claims

1. a mastication information storage means for storing information relating to the quality of mastication; a muscle activity acquiring means for acquiring muscle activity signals of a person's masticatory muscles; an analysis means for frequency-analyzing the muscle activity signal acquired by the muscle activity acquisition means and analyzing the chewing behavior based on the frequency analysis; a quality determination means for determining the quality of the chewing behavior based on the information on the chewing behavior analyzed by the analysis means; an extracting means for extracting, from the mastication information storage means, assistance information according to the quality of mastication determined by the quality determining means; The information processing device comprises: The analysis means divides electromyogram data as muscle activity signals into blocks of a predetermined number of samples, and analyzes the chewing behavior based on an envelope curve connecting average power values ​​in a specific frequency band obtained by fast Fourier transform of each block.

2. 2. The mastication assistance system according to claim 1, wherein said analyzing means determines that mastication is occurring when said change in state exceeds a predetermined threshold.

3. The mastication assistance system according to claim 2 , wherein mastication is determined to be occurring when an integral value calculated from the envelope as the change state exceeds a predetermined threshold.

4. 2. The mastication assist system according to claim 1, wherein said analyzing means analyzes the left and right mastication balance from the change in the muscle activity signals of the same masticatory muscles on the left and right.

5. 2. The mastication assistance system according to claim 1, wherein said analyzing means analyzes the characteristics of the object being masticated based on the gradient and duration of a mastication section determined to be mastication from the change in said envelope curve.

6. a user information storage unit that stores a correlation between a user's muscle activity value and a bite force value, the correlation being obtained by acquiring a muscle activity value when the user eats a prescribed food that has a known characteristic that the bite force required to bite through the food is fixed at a constant value; 2. The mastication assist system according to claim 1, wherein the analyzing means analyzes the bite force during mastication based on the correlation and a value of a mastication section determined to be during mastication from the change in the envelope.

7. the analysis means has a machine learning mechanism; The mastication assistance system according to any one of claims 1 to 6, wherein the mastication behavior is determined by referring to a learning result by the machine learning mechanism.

8. The chewing mode analyzed by the analysis means is: The chewing assistance system according to any one of claims 1 to 7, including aspects relating to at least one of the number of chews, chewing rhythm, progress of occlusal movements during a meal, degree of occlusal force, front-to-back / left-to-right chewing balance, and characteristics of the object being chewed.

9. The mastication support system according to any one of claims 1 to 8, wherein the quality of the mastication manner judged by the quality judgment means includes at least one of the number of mastications, the quality of the mastication rhythm, the quality of the progression of the occlusal movement, the quality of the occlusal force, the quality of the balance between the left and right mastications, the presence or absence of imbalance in eating, and the quality of the use of the masseter muscles.

10. The quality determination means The mastication assistance system according to any one of claims 1 to 9, further comprising a step of comparing the mastication behavior of the same person with a past mastication behavior to determine whether or not there has been an improvement.

11. the quality determination means has a machine learning mechanism; The mastication assistance system according to any one of claims 1 to 10, wherein the quality of the mastication behavior is determined by referring to a learning result by the machine learning mechanism.

12. A control program for causing an information processing device to function as the chewing assistance system described in any one of claims 1 to 11, wherein the chewing assistance program causes the information processing device to function as the muscle activity acquisition means, analysis means, quality determination means, and extraction means.

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