JP2011115344A - Eating disorder diagnostic system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an eating disorder diagnostic system capable determining the psychological state of a person with eating disorder. <P>SOLUTION: This eating disorder diagnostic system includes: an eating behavior signal detection means for detecting a signal caused by the eating behavior of a user; a user declaration means to which the user declares the content of the own eating behavior; an eating behavior content analysis means for analyzing the content of the eating behavior on the basis of the eating behavior signal detected by the eating behavior signal detection means; a true/false determination means determining the true/false of the content of the user's declaration by comparing the eating behavior analysis result analyzed by the eating behavior content analysis means with the content of the declaration input by the user himself/herself using a user input means; and a storage means for storing at least the true/false determination information determined by the true/false determination means. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to an eating disorder diagnosis system system capable of diagnosing eating disorders.

  Modern people must receive and digest a huge amount of information every day as society becomes more advanced. In addition, it is necessary to communicate well with many people in diluting human relationships, and it is impossible to live in modern society without cooperation with others. Under such circumstances, psychological stress is increased due to communication failure, relationship between people, gap between desire and reality, and so-called eating disorder patients with anorexia and bulimia are increasing.

  In an abundant society full of food, as well as patients with lifestyle-related diseases such as diabetes and hypertension, life improvement guidance that corrects moderate exercise and disordered daily life is effective for patients with food eating disorders. is there. However, since the amount of exercise can be grasped, it is easy to control, but it is not easy to objectively grasp the contents of diet.

  If it is an inpatient, the meal manager (hospital side) can easily know the meal time, the meal content, the presence or absence of eating, and the like. However, in the case of a non-hospital patient, it is necessary to have the patient record the meal time, meal content, etc. and report it, but the non-hospital patient often does not report his / her diet behavior details correctly. .

  For example, you may report uncertain content all at once, or you may declare a lie to show yourself well. In particular, eating disorder patients tend to include innocence in their declarations. For this reason, it is not easy to perform appropriate diagnosis and treatment based on the patient's report contents.

  For example, there is a technique disclosed in Patent Document 1 regarding a management system for eating behavior.

: JP 2003-173375 A

  Patent Document 1 is a technology relating to a life management system that detects biological information related to a meal and inquires a user of a meal record after a predetermined time has elapsed since the detection. This technology uses pulse, skin conductivity, skin temperature, GSR (skin electrical reflex), number of chewing, chewing interval, photoelectric pulse wave, etc. as biological information. Since it does not have a function of analyzing / recording, the user needs to input the content of the meal action separately.

  However, although the user's own input of eating behavior details is likely to cause omissions or forgetting to record the meal content, in the case of an eating disorder patient, the patient's eating behavior itself is the most important for diagnosing the patient's medical condition. It is necessary to know the contents of eating behavior correctly. In addition, people with eating disorders such as bulimia and anorexia sometimes describe incorrect content in an attempt to hide their eating behavior. However, in the life management system according to this technique, a doctor or a registered dietitian who uses the description content (the person who manages it) cannot know the authenticity of the description content. Therefore, it is difficult to perform appropriate diagnostic treatment.

  The present invention has been made in view of the above. Eating disorders such as anorexia and overeating are mainly caused by psychological factors such as stress related to human relationships, feelings of fear, compulsion, and physical inferiority that are not necessarily concrete. It is known that behavior varies greatly depending on the mental state from time to time. This means that the mental state of an eating disorder person can be read and understood by knowing time-series changes in eating behavior contents and eating behavior patterns. In other words, the eating behavior content and eating behavior pattern are the keys to reading and understanding the mental state of the eating disorder.

  It is an object of the present invention to provide an eating disorder diagnosis system that can determine the authenticity of the content of a report regarding eating behavior by a person with an eating disorder and can grasp time-series changes in mental state. .

  The basic configuration of the eating disorder diagnosis system of the present invention for solving the above-mentioned problems is that the eating behavior signal detection means for detecting a signal accompanying the eating behavior of the user, and the user reporting means for the user to declare his / her eating behavior content, The eating behavior content analyzing means for analyzing the eating behavior content based on the eating behavior signal detected by the eating behavior signal detecting means, the eating behavior analysis result analyzed by the eating behavior content analyzing means, and the user input means It compares with the contents of the report entered by the user himself / herself, and determines the authenticity of the contents of the user declaration, and a storage means for storing at least the authenticity determination information determined by the authenticity determination means It is characterized by that.

  In this configuration, the eating behavior signal detection means monitors the user's behavior and recognizes the user's eating behavior when a signal associated with the eating behavior is detected. Further, the user reporting means accepts input of the user's own eating behavior content. Further, the eating behavior content analyzing means analyzes the eating behavior content based on the eating behavior signal detected by the eating behavior signal detection means. Further, the authenticity discrimination means compares the self-report content regarding the eating behavior input by the user himself / herself through the user reporting means based on the eating behavior analysis result analyzed by the eating behavior content analysis means, and Determine whether the report is true or false. Then, the storage means stores at least the authenticity determination information determined by the authenticity determination means.

  The eating disorder diagnosis system having this configuration automatically and objectively grasps the content of eating behavior of the user, determines whether or not the content of reporting of eating behavior by the user himself is correct, and stores the result. Objectively grasped eating behavior contents serve as materials for providing appropriate dietary guidance to the user, and whether the user declaration contents are true or false serves as materials for psychologically analyzing individual eating behavior contents. That is, according to the eating disorder diagnosis system configured as described above, it is possible to analyze not only the user's eating behavior contents but also the mental state that causes such eating behavior, so that appropriate diagnosis and treatment for eating disorder patients can be performed. Can be done.

  Here, the “user” means a person (diagnosed person) who directly uses the eating disorder diagnosis system, and this person is usually a person suspected of eating disorder or a patient with eating disorder. is there. However, this person may be a healthy person, and the system of the present invention can also be used for improving the eating habits of a healthy person.

  The said basic structure WHEREIN: The said user input means shall be provided with the input prompting function which requests | requires a user to input own eating action content, when the said eating action signal detection means detects eating action.

  With this configuration, it is possible to prevent a situation in which the user accidentally forgets to input the content of eating behavior.

  Further, in the above basic configuration, the eating disorder diagnosis system, instead of the authenticity determination means, the eating behavior analysis result analyzed by the eating behavior content analysis means and the user input by the user input means A weighted true / false discriminating means for comparing the content of the declared tax and determining whether the user's declared content is weighted or not, and further, based on the weight discriminating result in each eating behavior discriminated by the authenticity judging means, Mental state determination means for determining the state can be provided.

  In this configuration, the weighted authenticity judging means compares the eating behavior analysis result analyzed by the eating behavior content analyzing means with the reporting content input by the user himself / herself by the user input means, so that the authenticity of the user reporting content is determined. Is determined by weighting. In addition, the mental state determination unit determines the mental state of the user based on the weighted determination result in each eating behavior determined by the authenticity determination unit.

  The weighting determination result and the mental condition determination result in this configuration are extremely useful materials when diagnosing eating disorders. In addition, by using these results, doctors and nurses can perform appropriate treatment and dietary guidance while confirming the effects of medication and dietary guidance.

  Moreover, in each said structure, the eating action content analyzed by the said eating action content analysis means is selected from the group which consists of eating action start time, eating action end time, the kind of ingestion, and the amount of ingestion 1 More than one.

  The types of ingestion include, for example, solids or beverages, Western food using knives and forks or Japanese food using chopsticks, Western snacks centered on bread and milk, and Japanese breakfasts centered on rice and miso soup. Examples include noodle foods such as udon, soba and ramen, and sweets such as chocolate and snacks. However, it is not restricted to these. In addition to the mass and capacity, the amount of ingested food may be a rough grasp such as how many cups of rice bowls for rice and how many cups of drinks.

  In each of the above configurations, the eating behavior signal detection means includes an acceleration sensor and / or an angular velocity sensor that detects the movement of the lower arm of the user, and the eating behavior signal is attached to the lower arm of the user. Further, the acceleration sensor and / or the angular velocity sensor can detect one-dimensional to two-dimensional to three-dimensional acceleration information and / or one-dimensional to two-dimensional to three-dimensional angular velocity information.

  A person who voluntarily eats as the subject of the present invention is always accompanied by movement of the arm when performing the eating behavior. The movement of the arm during walking or at work is clearly different from the movement of the arm during eating. When eating, the arm is always refracted, and the lower arm is moved to the mouth with the joint of the arm as a base point. Therefore, it is possible to detect eating behavior by analyzing the three-dimensional acceleration information accompanying the movement of the arm.

  In addition, how to eat varies depending on the type of food, and the movement of the arm varies. Therefore, the type of food is estimated by the arm movement (one-dimensional (one-axis) pattern, two-dimensional (two-axis) pattern, or three-dimensional (three-axis) pattern) detected by the acceleration sensor and / or the angular velocity sensor. it can.

  In addition, after the eating behavior is started, the amount of food and the amount of food taken can be estimated by counting the number of times the lower arm (the hand in front) is held in the mouth. For example, take the arm to the mouth, experimentally derive the average intake and average intake capacity per round trip away from the mouth, store this in the storage means in advance, and measured this By multiplying the number of times, the eating behavior content analysis means can calculate the total amount of food and the total amount of food consumed.

  Moreover, since the three-dimensional acceleration information detected by the acceleration sensor has a different pattern between the solid and the beverage, the solid and the beverage can be distinguished from the acceleration sensor information. For example, since water is drunk, there is a difference that a movement that tilts for a certain time with a glass etc. in the mouth occurs, but this movement does not occur in the case of food intake.

  From the above, by constantly monitoring the movement of the arm with the acceleration sensor and / or the angular velocity sensor, the start and end of eating behavior, the type and amount of food consumed, etc. can be generally detected. In addition, the acceleration sensor and / or the angular velocity sensor is advantageous in that it can be worn without any resistance by the user as if it were a decorative article, for example, by using a wireless transmission type wristwatch. The acceleration sensor and / or the angular velocity sensor may be represented by an acceleration sensor, and the acceleration information includes information obtained by the acceleration sensor and / or the angular velocity sensor.

  In each of the above configurations, the eating behavior signal detection means includes a sound sensor that detects a peristaltic motion sound of the user's esophagus, and the eating behavior signal is a swallowing sound detected by a sound sensor attached to the user's throat. It can be.

  Peristaltic movement of the esophagus always occurs when food passes through the esophagus, swallowing volume when food passes through the esophagus, and swallowing volume when food other than the esophagus passes through the esophagus (for example, when swallowing saliva) Is different. Therefore, it is possible to detect that food has been ingested by the sound signal. The presence / absence of eating behavior, its type, etc. are determined by storing various known sound signal patterns in the storage means provided in the eating disorder diagnosis system, and collating the signals obtained by the sound sensor with this. realizable.

  For example, when a sound signal that is an eating behavior is first detected is set as a meal start time, and then the sound signal that is an eating behavior is not detected continuously for a certain period of time (for example, 10 minutes), it is detected immediately before that. It is assumed that the detection time of the sound signal is the meal end time. In addition, for example, an average intake amount and an average intake capacity per one peristaltic exercise are experimentally derived, and this is stored in advance in a storage means, so that one actual meal start time and The amount of meal can be estimated from the integration time or number of integrations of sound signals between meal completion times.

  In addition, as described above, since the swallowing sound pattern is different between solid foods and beverages (for example, since water is drunk, regular peristaltic movement occurs continuously in a short time, but in the case of food intake, it is continuous. It is possible to discriminate between the intake of solids and the intake of beverages, and the respective intakes can be estimated.

  The eating disorder diagnosis system of the present invention constantly monitors a user's eating behavior, and when eating behavior occurs, automatically and objectively grasps the contents of the eating behavior and stores and manages it, and based on this objective information. The authenticity of the eating behavior content self-reported by the user is determined.

  According to such an eating disorder diagnosis system of the present invention, illness diagnosis and guidance policy regarding eating behavior are not misleaded by inaccurate eating behavior content or inaccurate eating behavior content, and the authenticity of eating behavior content can be prevented. Since the contents of individual eating behaviors performed by the user can be analyzed psychologically based on the discrimination result, appropriate diagnosis and treatment for eating disordered persons becomes possible.

FIG. 1 is a block diagram of an eating disorder diagnosis system according to an embodiment. FIG. 2 is a diagram illustrating a configuration example of the eating disorder diagnosis system according to the embodiment. FIG. 3 is a diagram illustrating an operation flow of the eating disorder diagnosis system according to the embodiment. FIG. 4 is an example of presentation of diagnostic information output by the eating disorder diagnosis system of the present invention. FIG. 5 is another example of presentation of diagnostic information output by the eating disorder diagnosis system of the present invention.

Embodiment
The present embodiment will be described in detail with reference to the drawings. FIG. 1 is a block diagram of an eating disorder diagnosis system according to the present embodiment.

  As shown in FIG. 1, the eating disorder diagnosis system according to the present embodiment includes an eating behavior signal detection unit 1 that detects a signal associated with a user's eating behavior, and an eating behavior detected by the eating behavior signal detection unit 1. Eating behavior content analysis means 2 for analyzing the signal and identifying the eating behavior content, user reporting means 3 for the user himself / herself to input his / her own eating behavior details (meal time, ingested items, intake amount, etc.), and user reporting The authenticity determination means 4 for comparing the eating behavior content input to the means 3 (hereinafter referred to as user-reported content) and the eating behavior content specified by the eating behavior content analysis means 2 to determine the authenticity of the user-reported content A storage means 5 for storing the user-reported content and the result of determination by the authenticity determination means, and the eating action content specified by the eating action content analysis means 2, and an output means 6 for outputting these contents to a display device or the like. Preparation To have.

  The eating behavior signal detection means 1 constantly detects a signal associated with a user's eating behavior. Eating behavior signal detection means 1 includes an acceleration sensor that monitors arm movement, a sound sensor that monitors swallowing sound when food passes through the esophagus, a stomach sound sensor that monitors peristalsis of the stomach, the stomach and / or the brain. A blood flow sensor or the like that detects the blood flow rate to the can be used.

  In general, sensor output signals often contain noise. Therefore, it is preferable that the eating behavior signal detection unit 1 includes a noise removal processing unit and an analog processing unit that performs digital conversion processing (A / D conversion) on the sensor signal.

  The eating behavior content analyzing means 2 analyzes the eating behavior signal detected by the eating behavior signal detecting means 1 and specifies eating behavior contents such as eating behavior start time, eating behavior end time, ingestion type, and intake amount. .

  For example, the signal interval detected by the sensor, the number of signals, the time series pattern of the signal intensity, etc. are analyzed, and the eating action start time, eating action end time, type of ingestion in light of known information stored in the storage means 5 Identify intake, etc. Also, for example, for a specific signal, the average amount of food swallowed per signal of that strength is experimentally derived and stored in the storage means 5 in advance. Based on the total intake, etc.

  The analysis result of the eating behavior content analysis means 2 is transmitted to the management server through the output means 6, and an analysis operator such as a registered dietitian analyzes it in detail and inputs the result to the management server. it can.

  Here, the acceleration sensor, which is a main element of the eating behavior signal detection means 1, is attached in the vicinity of the user's wrist so that the change in the acceleration of the lower arm of the user can be detected. The magnitude of acceleration and the acceleration change pattern due to the movement of the lower arm are remarkably different between eating behavior and resting, running and working. Therefore, by examining characteristics such as an acceleration change pattern for each type of operation, and storing the various types of operation and acceleration information in association with each other in the storage unit 5 and collating signal information actually detected by the acceleration sensor with this. The presence or absence of eating behavior and the content of eating behavior can be analyzed.

  Moreover, when the main element of the eating action signal detection means 1 is a sound sensor, the sound sensor is attached around the user's neck. Thereby, swallowing sound, food esophageal sound, and mastication sound can be detected. The sound quality of the sound emitted from the vocal cords and the sound of food passing through the esophagus is significantly different, and the sound quality and sound range differ depending on the type of food. It becomes possible.

  In addition, when eating behavior is roughly classified, there are cases where beverages are ingested and cases where solids are ingested. Regarding the amount of intake, there are weight and capacity. It is possible to analyze the difference between the two. It should be noted that the terms “intake” and “total intake” are used in a sense that does not distinguish between these.

  The user reporting means 3 is means for the user himself / herself to input his / her own eating behavior content. The user reporting means 3 includes an input device such as a touch panel and a keyboard and a display device, for example. In addition, in order to facilitate the reporting of the eating behavior content, the detailed items (selected branch) of the eating behavior content are displayed on the screen of the display device that also serves as a touch panel, and the eating behavior content is selected by the user sequentially selecting the selected branch. It is also possible to adopt a method that can be automatically declared. As other input means, speech recognition and the like can be considered.

  In addition, the user declaration content input to the user reporting means 3 is preferably stored in the storage means 5 so that an administrator such as a doctor or a registered dietitian can use it as a material for interrogating the user after a certain period of time. A configuration in which only the administrator can store the information and store it (for example, a configuration requiring password input) is adopted.

  The authenticity discrimination means 4 includes the objective eating behavior content specified by the eating behavior content analysis means 2 and the eating behavior content (hereinafter referred to as user declaration content) concerning the user declaration input by the user reporting means 3. Comparison is made and the authenticity of the user declaration content is determined based on the objective eating behavior content. The details of objective eating behavior content and user report content may be set appropriately.

  Examples of the content of eating behavior include eating behavior start time, eating behavior end time, distinction between solid and beverage, intake amount, and the like. The contents of the authenticity determination information presented by the authenticity determination means 4 will be described later.

  The storage means 5 stores at least authenticity determination information, and further stores information necessary for the system to perform predetermined processing and information other than the authenticity determination information obtained by the system. It is for keeping. The storage means 5 can be composed of a hard disk, a semiconductor memory, a holographic memory, or the like.

  Information necessary for causing the system to perform predetermined processing includes acceleration information that associates the type of meal with the magnitude and change pattern of acceleration, and the sound quality, range, intensity, and duration detected by the sound sensor. Such as sound sensor information in association with the type of food intake and the amount of swallowing per signal.

  The information other than the authenticity discrimination information obtained by this system includes the analysis result of the eating behavior content that is the objective eating behavior content and the user report content. In addition, user information such as the user's gender, age, height, weight, medical history, etc. may be stored for convenience of diagnosis and treatment. Furthermore, typical patterns of normal eating behavior, eating behavior patterns that are likely to occur by eating disorder patients, and the like may be stored, and may be used for analysis of eating behavior details and authenticity determination.

  The output means 6 displays the authenticity determination information determined by the authenticity determination means 4 and the user declaration contents stored in the storage means on the display device or outputs them to the information recording medium. In addition, it is preferable that the output by the output means 6 is such that various information is not seen by the user or a third party, for example, on condition that a password is input.

  The above-mentioned eating action content analysis means 2, user reporting means 3, and authenticity determination means 4 are constituted by a processing device such as a central processing unit (CPU) and program software for driving the processing device. Executed. Moreover, all the specific element members that execute these means may be incorporated in one apparatus, or may be composed of a plurality of terminals. For example, the central processing unit (CPU) and the storage unit may be in a management server installed at a remote location other than the user's location. In this case, signals from acceleration sensors and sound sensors attached to the user's body are wirelessly transmitted to the management server.

  The contents of the eating disorder diagnosis system according to the present invention will be further described based on examples.

<Example 1>
The eating disorder diagnosis system of Example 1 is demonstrated using the block diagram (FIG. 2) which shows an apparatus structural example. In this system, an acceleration sensor is used as the eating behavior signal detection means.

  As shown in FIG. 2, the eating disorder diagnosis system according to the first embodiment includes acceleration sensors 11 and 11 fixed to the left and right arms (near the wrist) of the user, an analog processing unit 12, and a central processing unit (CPU). ) And the like, a storage unit 14, a display unit 15, an input unit 16, a communication unit 17 that performs communication, and a reception unit 18 that receives a signal detected by the acceleration sensor.

  A known acceleration sensor device can be used as the acceleration sensor 11. However, an acceleration / angular velocity sensor device (hereinafter simply referred to as an acceleration sensor) that can detect three-axis accelerations that are orthogonal to each other and can also detect three-axis angular velocities in terms of obtaining more motion information. To use). In this example, a wristwatch-like bracelet type which is such an acceleration sensor device and can transmit a detection signal wirelessly is used. This is because a cordless type device capable of wireless transmission has an advantage of reducing the user's mounting burden. However, the detection signal may be transmitted in a wired manner.

  The analog processing unit 12 incorporates a removal circuit for removing noise, an A / D converter, and the like, and a known analog processing circuit can also be used for the analog processing unit 12.

  The storage unit 14 is configured by a hard disk, a semiconductor memory, or the like, the display unit 15 is configured by a liquid crystal display or the like, and the input unit 16 is configured by a keyboard, a mouse, or the like. A known member may be used for these elements. In addition, a known device may be used for the receiving unit 18 that receives a signal from the acceleration sensor 11 and the communication unit 17 that communicates with the outside.

  Here, many of the elements required for the eating disorder diagnosis system according to the present invention are in common with the elements that the mobile phone originally has. Therefore, for example, the processing unit 13, the storage unit 14, the display unit 15, the input unit 16, the communication unit 17, and the like can be combined with elements that are inherently included in the mobile phone. When a part of the elements required for the eating disorder diagnosis system according to the present invention is also used as the elements of the mobile phone, a mobile phone with an eating disorder diagnosis system can be configured at a low cost. This is convenient because the convenience of the service is further enhanced.

  FIG. 3 shows an operation flow of automatic detection of eating behavior according to the present embodiment. With reference to FIG. 3, an operation flow of an eating disorder diagnosis system in which the movement of both arms of the user is detected by an acceleration sensor will be described.

<S11: Monitoring of arm movement>
An acceleration sensor 11 attached in the vicinity of the wrists of both arms of the user always monitors an acceleration signal accompanying the movement of the arms.

<S12: Preprocessing of sensor signal>
The analog processing unit 12 removes noise from the signal acquired by the acceleration sensor and performs preprocessing such as A / D conversion.

<S13: Eating behavior signal detection>
Based on the sensor signal after the processing unit 13 has pre-processed by the analog processing unit 12, it is determined whether the sensor signal is an eating behavior signal.

  Since the movement of the lower arm when ingesting food is different from that during running or labor, etc., acceleration information associated with the movement of the lower arm when ingesting food, that is, an eating behavior signal is stored in the storage unit 14 in advance. The presence or absence of eating behavior can be determined by storing the signal information detected by the acceleration sensor and collating it with the signal information.

  If it is determined in S13 that it is an eating action, the process proceeds to S14, and if it is determined that the action is not an eating action, the process returns to S11 (arm movement monitoring).

<S14: Discrimination between beverage intake and solid intake>
The processing unit 13 determines whether the user's eating behavior is a beverage intake behavior or a solid matter intake behavior following the determination of S13.

<S15: Determination of eating behavior content>
Further, the processing unit 13 determines the time when the eating behavior signal is first detected as the eating behavior start time, and finally detects when the acceleration sensor signal related to the eating behavior is not detected for a certain time (for example, 20 minutes). The detection time of the acceleration sensor signal concerning the performed eating behavior is determined as the end time of the eating behavior. Then, the number of eating behavior signals detected from the start to the end of eating behavior is integrated for each eating behavior (for each beverage and solid), and this number of multiplications is multiplied by the estimated amount per signal. The beverage intake total amount or the beverage intake total amount is calculated. In addition, the type of ingested food is estimated and specified from the eating behavior signal pattern.

  It is based on the following reason that such information can be obtained based on the acceleration sensor signal. Usually, in the case of beverage intake, one hand (arm tip) with a cup etc. is carried to the mouth and stays there for a while while drinking the beverage, while the other hand (arm tip) is The movement has stopped. Such movements of both arms are different in the case of ingesting solids and in the case of ingesting beverages, and often differ depending on the type of food.

  In addition, the movements associated with eating behavior are unique movements that carry food to the mouth without dropping food, and there is no fast movement exceeding three times the gravitational acceleration. Therefore, it can be clearly distinguished from arm movements such as walking and running.

  For example, when eating rice with chopsticks, both the hand holding the bowl and the hand holding the chopstick are related to each other and move in an unusual way. In the case of Western food eaten with a fork and knife, it moves differently from Japanese food. In addition, noodles such as ramen and soba have unique movements that can be distinguished from others. These movement characteristics appear as differences in acceleration magnitude and pattern. Therefore, the characteristics of the eating behavior signal associated with each eating behavior are stored in the storage unit 14 in advance, and by comparing the measured acceleration sensor signal pattern with these, the distinction between the beverage intake behavior and the solid food intake behavior, The type of food, the type of food ingested, etc. can be specified.

<S16: Recording of eating behavior details>
The processing unit 13 causes the storage unit 14 to store eating behavior information such as the meal start time, the meal end time, the beverage intake total amount, and the solids intake total amount determined above.

<S17: Input of eating behavior content by user>
Separately from the above S11 to S16, the user himself / herself reports his / her own eating behavior details (meal start time / end time, ingested item, ingested amount, etc.) (S17). In addition, this input may be a mode in which the user voluntarily records each time when eating behavior, but when it is determined that there is eating behavior in S13, the processing unit 13 "please enter the meal contents" It is also possible to display a message prompting the recording of the eating behavior content such as text or voice on the display unit.

<S18: Record of user report content>
The processing unit 13 stores the content reported by the user in the storage unit 14.

<S19: Authenticating User Report Contents>
The processing unit determines the authenticity of the user report content that the user has declared based on the objective eating behavior content determined based on the acceleration sensor information. The authenticity determination is represented by, for example, “◯” when the user report content matches the objective eating behavior content, and “x” when they do not match. Of course, expressions such as partial matching “Δ” may be used. In other words, although this system could not be determined to be false, there was a suspicious behavior, and a separate decision by a doctor or the like was requested. However, in the following example of this embodiment, for the sake of convenience of explanation, it is assumed that two choices of “◯” and “×” can be determined. Further, it is not necessary to determine the authenticity of the user report contents for all items of the user report contents. It is only necessary to determine in advance items that are familiar with authenticity determination and items that are useful for grasping the mental state, and that only this item is determined.

<S20: Record of authenticity determination result, etc.>
The storage unit 14 stores the authenticity determination result and other useful information. In addition to the above-mentioned “○”, “×”, etc., the raw data itself used in the analysis, or various information obtained in the analysis / discrimination process (for example, the difference between the meal start time between automatic discrimination and user input, etc.) ) Is also included in useful information. As a result, the system administrator can take out and view the user's eating behavior information when necessary. This information is useful for understanding the mental status of patients with eating disorders and for diagnosing and treating medical conditions. In addition, an eating disorder diagnosis system using an acceleration sensor can constantly monitor the user's behavior in daily life, and thus is useful for diagnosis and treatment of alcoholic patients and for prevention of recurrence of drug dependence such as a stimulant patient.

<Example of diagnostic information list presented by this system>
Here, diagnosis information that can be provided by the eating disorder diagnosis system according to the first embodiment will be described. FIG. 4 shows an example of an anorexic patient, and FIG. 5 shows an example of an bulimia patient. These are merely examples, and the information that can be presented by the eating disorder diagnosis system of the present invention is not limited to the examples of FIGS.

  4 and 5 divide 24 hours a day into 7 zones, and the eating behavior analysis results and the like can be output for each zone. For example, zone 1 in FIG. 4 is a breakfast intake time zone. In this time zone, the user is reporting that breakfast has been eaten even though no objective eating behavior has been detected. Therefore, the authenticity determination is “×” (the user report is false).

  On the other hand, in zone 3, which is a lunch time zone, eating behavior is detected at 12: 05-12: 30, and it is reported that the user has reported udon. Therefore, the authenticity determination is “◯” (the user report is correct).

  In this way, the authenticity determination is displayed as “◯” or “×”. Therefore, it is possible to grasp the daily mental state of the user by increasing the number of authenticity determination items or graphing the false reporting rate (× / [◯ + ×]) for each day. For example, in the case of anorexic patients who are administering a drug, if a false report rate tends to decrease, it can be determined that there is a drug administration effect. On the other hand, when the false reporting rate increases or does not change, it is possible to take measures such as switching to another drug. In addition, by observing the transition of the false reporting rate, it can be determined whether the user's mental state is in a state of jealousness or a state of intent.

  Furthermore, in the system according to the first embodiment, the eating behavior analysis result analyzed by the eating behavior content analysis unit is compared with the reporting content input by the user by the user input unit, and the user reporting content is weighted. Weighted true / false determining means for determining the mental state of the user based on the weighted determination results for each eating behavior determined by the true / false determining means can be provided. With such a system, more detailed mental state information can be obtained.

  For example, as shown in FIGS. 4 and 5, the degree of authenticity is evaluated by a score of 0 to 3, and the mental state is evaluated according to a preset score. Here, a score of 0 indicates no false declaration, a score of 1 indicates that the report content is lightly false, a score of 2 indicates that the report content is clearly false, and a score of 3 indicates that the report content is not true. Also, for example, the score for 24 hours is 0 to 1 normal, 2 to 5 is a mental state is somewhat unstable, 6 to 9 is a problem with the mental state, treatment is necessary, 10 or more is a serious state, For example, monitoring and treatment are required.

  Further, this score can be added every 24 hours to obtain a daily score, which can be graphed by taking the added score on the vertical axis and the number of days on the horizontal axis. If it does in this way, since a user's mental state is projected on a graph, a patient's mental state can be grasped visually by using this graph.

  It should be noted that true / false weighting and mental condition determination criteria can be arbitrarily set. For example, when the objective eating behavior is “Yes”, the weighting score is a plus point, and when the objective eating behavior is “No”, the weighting score is a minus point. Specifically, when the objective eating behavior is “no eating behavior” and the user declaration content is “eating”, the weighting evaluation is “−3”, and the objective eating behavior is “with eating behavior” and the user reporting When the content is “not eaten” or unreported, the weighting evaluation is “+3”, and a plus point and a minus point are added and displayed. In this way, there is an advantage that it becomes easier to distinguish between anorexia and overeating.

<Example 2>
The second embodiment is an example in which a sound sensor that detects peristaltic movement sound of the esophagus is used as the eating behavior signal detection means.

  The eating disorder diagnosis system of the second embodiment uses a sound sensor instead of the acceleration sensor 11 of FIG. Other elements may be the same as those in the first embodiment. Therefore, only differences from the first embodiment will be described here.

  The sound sensor is attached near the user's esophagus (at the throat). This sound sensor monitors swallowing sounds and throat peristalsis. Although a known device can be used as the sound sensor, a method of transmitting a detection signal by wire is preferable from the viewpoint of reducing the mounting burden on the user.

  The determination as to whether the sound sensor signal is an eating behavior signal can be made based on the eating behavior signal associated with the eating behavior stored in the storage unit. This is because there is a difference in sound quality and sound pattern between the sound of food and drink passing through the esophagus and the sound in other cases. If the detected sound quality and / or sound pattern matches the detected sound quality and / or sound pattern stored in the storage unit, it is determined to be eating behavior, and if not, it is determined not to be eating behavior. It will be.

  For example, when a beverage is ingested, water sounds such as swallowing sounds and gurgling at short intervals between drinking and drinking are detected, so that it is understood that the beverage has been ingested easily. On the other hand, when a solid is ingested, a stronger peristaltic motion sound is detected, indicating that the solid is ingested. Moreover, since the sound sensor attached to the throat also detects mastication sound, the intake of solid matter can be determined from the mastication sound, and the amount of food intake can be estimated from the number of mastications and the number of peristaltic movement sounds.

  In the eating disorder diagnosis system of the present invention, the eating behavior signal detection means or the like monitors the eating behavior of the user, and objectively recognizes the presence or absence of the eating behavior based on the eating behavior related signal. A system administrator such as a doctor can properly manage the user's eating behavior and provide improvement guidance by using this information. Further, the eating disorder diagnosis system of the present invention determines the authenticity of user-reported content that the user has declared based on the objective eating behavior content detected by the eating behavior signal detection means, etc., and presents it to the administrator To do. This authenticity discrimination information is useful for the diagnosis and treatment of the degree of eating disorder of the user and the psychological state in the eating disorder. Therefore, the industrial applicability of the present invention is great.

Claims (6)

Eating behavior signal detection means for detecting a signal associated with a user's eating behavior;
User reporting means for users to declare their eating behavior,
Eating behavior content analyzing means for analyzing eating behavior content based on the eating behavior signal detected by the eating behavior signal detecting means,
Comparing the eating behavior analysis result analyzed by the eating behavior content analyzing means with the reporting content input by the user himself / herself with the user input means, and determining the authenticity of the user reporting content;
Storage means for storing at least authenticity determination information determined by the authenticity determination means;
Eating disorder diagnosis system comprising: The eating disorder diagnosis system according to claim 1,
The user input means includes an input prompting function that requests the user to input his / her own eating action content when the eating action signal detecting means detects eating action,
Eating disorder diagnosis system characterized by that. The eating disorder diagnosis system according to claim 1 or 2,
Instead of the authenticity determination means, the eating behavior analysis result analyzed by the eating behavior content analysis means is compared with the reporting content input by the user himself / herself with the user input means, and the weight of the user reporting content is weighted Weighted authenticity determination means for determining
And a mental state determination unit that determines a user's mental state based on a weighted determination result in each eating behavior determined by the authenticity determination unit.
Eating disorder diagnosis system characterized by that. In the eating disorder diagnosis system according to claim 1 to 3,
The eating behavior content analyzed by the eating behavior content analyzing means is one or more selected from the group consisting of the eating behavior start time, the eating behavior end time, the type of intake, and the amount of intake.
Eating disorder diagnosis system characterized by that. The eating disorder diagnosis system according to claim 1,
The eating behavior signal detection means includes an acceleration sensor and / or an angular velocity sensor for detecting the movement of the lower arm of the user,
The eating behavior signal is one-dimensional to two-dimensional to three-dimensional acceleration information and / or one to two-dimensional to three-dimensional angular velocity information detected by the acceleration sensor and / or the angular velocity sensor attached to the lower arm of the user. is there,
Eating disorder diagnosis system characterized by that. The eating disorder diagnosis system according to claim 1 or 2,
The eating behavior signal detection means includes a sound sensor for detecting a peristaltic movement sound of the user's esophagus,
The eating behavior signal is a swallowing sound detected by the sound sensor attached to the user's throat.
Eating disorder diagnosis system characterized by that.

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