Meal monitoring device and method thereof

Abstract

A meal monitoring device according to an embodiment of the present invention comprises: a data reception unit that receives measurement data of a parameter related to the ear of a target object and blood sugar data of the target object; a first estimated time calculation unit that calculates a first estimated time that is estimated to be a food intake activity time of the target object on the basis of the measurement data; a second estimated time calculation unit that calculates a second estimated time that is estimated to be a glucose absorption time of the target object on the basis of the blood sugar data; and a meal time calculation unit that calculates a meal time of the target object on the basis of the first estimated time and the second estimated time.

Description

Meal monitoring device and method thereof

[0001] The present invention relates to a meal monitoring device and method thereof.

[0002] Diabetes is a chronic disease prevalent in modern society. In Korea, it affects over 2 million people, or 5% of the population. Diabetes is caused by a variety of factors, including obesity, stress, poor eating habits, and genetic predisposition. This deficiency, either absolute or relative, prevents the pancreas from properly balancing blood sugar levels, leading to an excessive increase in blood sugar levels. Blood normally contains a certain concentration of glucose, from which tissue cells derive energy.

[0003] However, if glucose increases more than necessary, it cannot be properly stored in the liver, muscles, or fat cells, but accumulates in the blood, and as a result, diabetic patients maintain blood sugar levels that are much higher than those of normal people, and as the excessive blood sugar passes through the tissues and is excreted in the urine, the sugar absolutely necessary for each tissue of the body becomes insufficient, causing abnormalities in each tissue of the body.

[0004] Diabetes is characterized by having almost no symptoms in the early stages, but as the disease progresses, it causes specific symptoms such as diabetes-specific polydipsia, polyuria, weight loss, general malaise, itchy skin, and long-lasting wounds on the hands and feet that do not heal. As the disease progresses further, complications such as vision impairment, high blood pressure, kidney disease, stroke, periodontal disease, muscle cramps and neuralgia, and gangrene appear.

[0005] Diagnosing diabetes and preventing complications requires systematic blood sugar measurement and treatment, as well as ongoing monitoring of blood sugar levels. In particular, accurate prediction and management of blood sugar levels requires dietary information, such as the type, amount, and time of food consumed. To monitor this type of diet, existing diet management systems require users to manually input meal times and amounts.

[0006] However, meal monitoring methods that require users to directly input meal information are prone to omissions and errors in meal-related information. These omissions and errors ultimately hinder accurate prediction of blood sugar fluctuations, making diabetes management difficult.

[0007] Background technology of the present invention includes Japanese Patent Application Publication No. 2011-062335 and U.S. Patent Application Publication No. US2010 / 0168537.

[0008] The present invention relates to a meal monitoring device and method that can record and provide meal-related information such as meal time and meal amount with high accuracy.

[0009] The problem to be solved in the embodiment is not limited to this, and it can be said that the purpose or effect that can be understood from the solution or implementation form of the problem described below is also included.

[0010] A meal monitoring device according to an embodiment of the present invention includes: a data receiving unit that receives measurement data of parameters related to an ear of a target object and blood sugar data of the target object; a first estimated time calculating unit that calculates a first estimated time estimated as a food intake activity time of the target object based on the measurement data; a second estimated time calculating unit that calculates a second estimated time estimated as a glucose absorption time of the target object based on the blood sugar data; and a meal time calculating unit that calculates a meal time of the target object based on the first estimated time and the second estimated time.

[0011] The first estimated time calculation unit can compare a feature value calculated from the measurement data with a first reference value corresponding to the feature value, and calculate the first estimated time based on a result of the comparison between the feature value and the first reference value.

[0012] The second estimated time calculation unit may compare a blood sugar value included in the blood sugar data with a second reference value corresponding to the blood sugar value, and calculate the second estimated time based on a result of the comparison between the blood sugar value and the second reference value.

[0013] The meal time calculation unit may calculate a reference time range based on the second estimated time; select a third estimated time that exists within the reference time range among the first estimated times; detect a start estimated time that is the earliest time and an end estimated time that is the latest time among the third estimated times; and calculate the meal time based on the start estimated time and the end estimated time.

[0014] The system may further include a meal amount calculation unit that calculates the amount of meal consumed by the subject at the meal time based on the measurement data and the blood sugar data.

[0015] The measurement data of the above ear-related parameters may include at least one of vibration signal data, optical signal data, acoustic signal data, and pressure signal data.

[0016] The first estimated time calculation unit may generate a harmonic pattern of the vibration signal data based on frequency information included in the vibration signal data; determine whether a similarity between the harmonic pattern and a preset reference harmonic pattern is greater than or equal to a first threshold; determine whether the frequency information is included in a predetermined frequency range; and calculate a time interval in which the similarity is greater than or equal to a predetermined threshold and the frequency information is included in a predetermined range as the first estimated time.

[0017] The first estimated time calculation unit can calculate a change in light using information about the amount of light included in the optical signal data; compare the change in light with a first threshold range; and calculate a time section in which the change in light is included in the first threshold range as the first estimated time.

[0018] The first estimated time calculation unit may generate an acoustic pattern of the acoustic signal data based on acoustic information included in the acoustic signal data; calculate a similarity by comparing the acoustic pattern with a preset reference acoustic pattern; and calculate a time interval in which the similarity is greater than or equal to a second threshold as the first estimated time.

[0019] The first estimated time calculation unit may calculate power and the amount of change in the power using pressure information included in the pressure signal data; determine whether the magnitude of the force is greater than or equal to a third threshold; determine whether the amount of change in the force is within a second threshold range; and calculate a time interval in which the magnitude of the force is greater than or equal to the third threshold and the amount of change in the force is within the second threshold range as the first estimated time.

[0020] A meal monitoring method according to an embodiment of the present invention includes the steps of: receiving measurement data of parameters related to an ear of a target object and blood sugar data of the target object; calculating a first estimated time estimated as a food intake activity time of the target object based on the measurement data; calculating a second estimated time estimated as a glucose absorption time of the target object based on the blood sugar data; and calculating a meal time of the target object based on the first estimated time and the second estimated time.

[0021] The step of calculating the first estimated time may include a step of comparing a feature value calculated from the measurement data with a first reference value corresponding to the feature value; and a step of calculating the first estimated time based on a result of the comparison between the feature value and the first reference value.

[0022] The step of calculating the second estimated time may include a step of comparing a blood sugar value included in the blood sugar data with a second reference value corresponding to the blood sugar value; and a step of calculating the second estimated time based on a result of the comparison between the blood sugar value and the second reference value.

[0023] The step of calculating the meal time of the target object may include: calculating a reference time range based on the second estimated time; selecting a third estimated time that exists within the reference time range among the first estimated times; detecting a start estimated time that is the earliest time and an end estimated time that is the latest time among the third estimated times; and calculating the meal time based on the start estimated time and the end estimated time.

[0024] The method may further include a step of calculating the amount of food consumed by the subject at the meal time based on the measurement data and the blood sugar data.

[0025] The measurement data of the above ear-related parameters may include at least one of vibration signal data, optical signal data, acoustic signal data, and pressure signal data.

[0026] The step of calculating the first estimated time may include: a step of generating a harmonic pattern of the vibration signal data based on frequency information included in the vibration signal data; a step of determining whether a similarity between the harmonic pattern and a preset reference harmonic pattern is greater than or equal to a first threshold; a step of determining whether the frequency information is included in a predetermined frequency range; and a step of calculating a time section in which the similarity is greater than or equal to a predetermined threshold and the frequency information is included in a predetermined range as the first estimated time.

[0027] The step of calculating the first estimated time may include: a step of calculating a change in light using information about the amount of light included in the optical signal data; a step of comparing the change in light with a first threshold range; and a step of calculating a time section in which the change in light is included in the first threshold range as the first estimated time.

[0028] The step of calculating the first estimated time may include: a step of generating an acoustic pattern of the acoustic signal data based on acoustic information included in the acoustic signal data; a step of calculating a similarity by comparing the acoustic pattern with a preset reference acoustic pattern; and a step of calculating a time section in which the similarity is greater than or equal to a second threshold as the first estimated time.

[0029] The step of calculating the first estimated time may include: a step of calculating power and a change in the power using pressure information included in the pressure signal data; a step of determining whether the magnitude of the force is greater than or equal to a third threshold; a step of determining whether the change in the force is within a second threshold range; and a step of calculating a time section in which the magnitude of the force is greater than or equal to the third threshold and the change in the force is within the second threshold range as the first estimated time.

[0030] According to an embodiment, accurate information related to meals, such as meal times and meal amounts, is provided to the user.

[0031] Additionally, providing accurate information on meal times and meal amounts can help predict blood sugar changes more accurately.

[0032] Additionally, it provides users with the convenience of meal monitoring by recording and providing information such as meal times and meal amounts without user intervention.

[0033] The various advantageous and beneficial effects of the present invention are not limited to the above-described contents, and will be more easily understood in the course of explaining specific embodiments of the present invention.

[0034] FIG. 1 is a schematic diagram showing one embodiment of a meal monitoring system according to an embodiment of the present invention.

[0035] FIG. 2 is a first embodiment of a sensing device according to an embodiment of the present invention.

[0036] FIG. 3 is a second embodiment of a sensing device according to an embodiment of the present invention.

[0037] FIG. 4 is a third embodiment of a sensing device according to an embodiment of the present invention.

[0038] FIG. 5 is a fourth embodiment of a sensing device according to an embodiment of the present invention.

[0039] Figure 6 is a configuration diagram of a meal monitoring device according to an embodiment of the present invention.

[0040] Figure 7 is a flowchart of a meal monitoring method according to an embodiment of the present invention.

[0041] Figure 8 is a flowchart detailing step S720 of Figure 7.

[0042] FIG. 9 is a flowchart showing a first embodiment of step S720 of FIG. 7.

[0043] FIG. 10 is a flowchart showing a second embodiment of step S720 of FIG. 7.

[0044] FIG. 11 is a flowchart showing a third embodiment of step S720 of FIG. 7.

[0045] Fig. 12 is a flowchart showing a fourth embodiment for step S720 of Fig. 7.

[0046] Figure 13 is a drawing for explaining a process for calculating meal times according to an embodiment of the present invention.

[0047] The present invention is susceptible to various modifications and embodiments. Specific embodiments are illustrated and described in the drawings. However, this is not intended to limit the present invention to specific embodiments, but rather to encompass all modifications, equivalents, and alternatives falling within the spirit and technical scope of the present invention.

[0048] Terms including ordinal numbers, such as "second," "first," etc., may be used to describe various components, but the components are not limited by these terms. These terms are used solely to distinguish one component from another. For example, without departing from the scope of the present invention, a second component may be referred to as a "first component," and similarly, a first component may also be referred to as a "second component." The term "and / or" includes a combination of multiple related items described herein or any of multiple related items described herein.

[0049] When a component is referred to as being "connected" or "connected" to another component, it should be understood that it may be directly connected or connected to that other component, but that there may be other components intervening. Conversely, when a component is referred to as being "directly connected" or "connected" to another component, it should be understood that there are no other components intervening.

[0050] The terminology used in this application is only used to describe specific embodiments and is not intended to limit the present invention. The singular expression includes the plural expression unless the context clearly indicates otherwise. In this application, it should be understood that the terms "comprise" or "have" indicate the presence of a feature, number, step, operation, component, part, or combination thereof described in the specification, but do not preclude the possibility of the presence or addition of one or more other features, numbers, steps, operations, components, parts, or combinations thereof.

[0051] Unless otherwise defined, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains. Terms defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant technology, and shall not be interpreted in an idealized or overly formal sense unless explicitly defined herein.

[0052] Hereinafter, embodiments will be described in detail with reference to the attached drawings. Regardless of the drawing numbers, identical or corresponding components are given the same reference numbers, and redundant descriptions thereof will be omitted.

[0053] FIG. 1 is a schematic diagram showing one embodiment of a meal monitoring system according to an embodiment of the present invention.

[0054] A meal monitoring system according to an embodiment of the present invention calculates a target subject's meal time and provides the calculated meal time to the user. Here, the meal monitoring system may utilize measurement data related to ear parameters of the target subject and blood sugar data of the target subject to calculate the target subject's meal time.

[0055] If the meal monitoring system according to an embodiment of the present invention calculates and provides meal times for a target object, the user can utilize the calculated meal times as information necessary for managing the target object's health. For example, the calculated meal times can be used to determine whether the user's blood sugar increase was caused by food intake or by non-dietary causes, such as infection. As another example, the meal amount provided along with the meal times can be used to determine whether the user has consumed an appropriate amount of food for blood sugar management. Here, the user refers to the person who receives information regarding the calculated meal times for the target object. The user may be a person, such as the target object. In addition, the user may be a healthcare professional (HCP), such as a doctor or nurse, who manages the health of the target object. In addition, the user may be a family member or guardian of the target object. In addition, the user may be a server system that performs functions such as managing the health of the target object or collecting / analyzing health management information.

[0056] Referring to FIG. 1, a meal monitoring system according to an embodiment of the present invention includes a sensing device (10), a blood sugar measuring device (20), and a meal monitoring device (30).

[0057] First, the sensing device (10) may refer to a device that generates measurement data by measuring parameters related to the ear of a target object. Here, the target object may refer to a human, but is not limited thereto. The target object may also include animals other than humans. In addition, the ear-related parameter may refer to a parameter generated in the ear when the target object consumes food. According to one embodiment, the ear-related parameter may include a vibration signal generated in the ear canal and the eardrum when the target object consumes food. According to one embodiment, the ear-related parameter may include an optical signal reflected from the ear canal and the eardrum when the target object consumes food. According to one embodiment, the ear-related parameter may include an acoustic signal generated in the ear canal and the eardrum when the target object consumes food. According to one embodiment, the ear-related parameter may include a pressure signal generated in the ear canal and the eardrum when the target object consumes food.

[0058] The sensing device (10) may be placed on the ear of a target object to measure ear-related parameters. According to one embodiment, the sensing device (10) may be implemented as a device in the form of an earphone or a headphone. The sensing device (10) may also be implemented in a form that is coupled to a device such as an earphone or a headphone.

[0059] The sensing device (10) can be communicatively connected to the meal monitoring device (30). The sensing device (10) can be communicatively connected to the meal monitoring device (30) wirelessly and / or by wire. The sensing device (10) can include a wireless and / or wired communication device for communicatively connecting to the meal monitoring device (30). For example, the sensing device (10) can include a USB cable device, an infrared communication device, an NFC (Near Field Communication) device, a Bluetooth device, a WiFi (Wireless Fidelity) device, an LTE device, etc.

[0060] Next, the blood glucose measurement device (20) may refer to a device that generates blood glucose data by measuring the glucose level of a target object. Accordingly, the blood glucose data may include information related to the glucose level of the target object. The blood glucose data may be time-series data generated by continuously measuring the glucose level of the target object. Here, "continuous" includes not only linearly measuring the glucose level, but also measuring the glucose level at predetermined time intervals. For example, measuring the glucose level 60 times over 10 minutes, i.e., every 10 seconds, may be interpreted as continuously measuring the glucose level.

[0061] The blood glucose measurement device (20) may be a continuous glucose monitoring system (CGMS). In one embodiment, the blood glucose measurement device (20) may be a device that measures the blood glucose of a subject through interstitial fluid present under the skin. In another embodiment, the blood glucose measurement device (20) may be a device that continuously measures the blood glucose of a subject from bodily fluids such as saliva or tears. In another embodiment, the blood glucose measurement device (20) may be a device that continuously measures the blood glucose of a subject from blood. In addition, various devices capable of continuously measuring the blood glucose of a subject may be included as examples of the blood glucose measurement device (20).

[0062] The blood glucose measurement device (20) may include various device configurations for measuring blood glucose of a target object. According to one embodiment, the blood glucose measurement device (20) may include a transmitter including a sensor for measuring a glucose measurement signal of the target object, and a receiver for receiving the measured glucose signal and analyzing the blood glucose level. The transmitter and receiver may be implemented as separate devices, but are not limited thereto, and may be implemented together in a single device. In addition, the receiver of the blood glucose measurement device (20) may be implemented as the same device as the meal monitoring device (30). In addition, the blood glucose measurement device (20) may be implemented as various types of device configurations.

[0063] The blood sugar measuring device (20) can be connected to a meal monitoring device (30). The blood sugar measuring device (20) can be connected to the meal monitoring device (30) wirelessly and / or by wire. The blood sugar measuring device (20) can include a communication device according to a communication method in order to be connected to the meal monitoring device (30). For example, the blood sugar measuring device (20) can include a USB cable device, an infrared communication device, an NFC (Near Field Communication) device, a Bluetooth device, a WiFi (Wireless Fidelity) device, an LTE device, etc.

[0064] Next, the meal monitoring device (30) may be a device that calculates the meal time of the target object. Here, meal time refers to the time the target object eats, i.e., the time it consumes food. The act of consuming food includes all actions that transmit food through the mouth to the esophagus, such as chewing food and swallowing food.

[0065] The meal monitoring device (30) can calculate the meal time of the target object using measurement data of parameters related to the ears of the target object and blood sugar data of the target object. To this end, the meal monitoring device (30) can receive measurement data and blood sugar data from the sensing device (10) and the blood sugar measuring device (20).

[0066] The meal monitoring device (30) may include a communication device for wirelessly and / or wiredly communicating with the sensing device (10) and the blood glucose measuring device (20). For example, the meal monitoring device (30) may include a USB cable device, an infrared communication device, an NFC (Near Field Communication) device, a Bluetooth device, a WiFi (Wireless Fidelity) device, an LTE device, etc.

[0067] In addition, the meal monitoring device (30) may include a processor and memory to process measurement data and blood sugar data. Accordingly, the meal monitoring device (30) may be implemented as various types of computing devices including a processor and memory.

[0068] FIG. 2 is a first embodiment of a sensing device according to an embodiment of the present invention.

[0069] In Fig. 2, the target object is assumed to be a human being. The sensing device (10) illustrated in Fig. 2 may be a device that measures vibration signal data among parameters related to the human ear.

[0070] Human eating behavior can be mainly divided into chewing (mastication) and swallowing (swallowing). In chewing, the lower and upper teeth collide or grind while chewing food. At this time, the collision and grinding between the teeth generate vibrations (SIGV) in the eardrum (ED) and the outer wall of the external auditory canal (EC). In swallowing, food moves from the oral cavity, pharynx, and esophagus to the stomach. At this time, as the food moves through the oral cavity, pharynx, and esophagus in that order, vibrations (SIGV) occur in the eardrum (ED) and the outer wall of the external auditory canal (EC), which are connected to the oral cavity, pharynx, and esophagus.

[0071] The sensing device (10) illustrated in FIG. 2 measures the vibration (SIGV) of the ear generated by the eating behavior. The sensing device (10) can be placed on a human ear and generate vibration (SIGV) signal data by measuring the vibration (SIGV) generated in the outer wall of the external auditory canal (EC) and the eardrum (ED) during eating. The sensing device (10) can directly measure the vibration (SIGV) from the skin surface of the ear, but is not limited thereto. The sensing device (10) can also generate a vibration (SIGV) signal by measuring the vibration (SIGV) of the ear generated by the eating behavior through the vibration (SIGV) of the air in the external auditory canal (EC).

[0072] FIG. 3 is a second embodiment of a sensing device according to an embodiment of the present invention.

[0073] In Fig. 3, the target object is assumed to be a human being. The sensing device (10) illustrated in Fig. 3 may be a device that measures optical signal data among ear-related parameters.

[0074] Human eating behavior can be mainly divided into chewing and swallowing. Chewing occurs when the upper and lower teeth collide or grind while chewing food. This grinding and grinding between the teeth can cause deformations in the eardrum (ED) and external auditory canal (EC). Swallowing occurs when food moves through the oral cavity, pharynx, and esophagus before reaching the stomach. As food moves through the oral cavity, pharynx, and esophagus, the eardrum (ED) and external auditory canal (EC), which are connected to the oral cavity, pharynx, and esophagus, are deformed.

[0075] The sensing device (10) illustrated in FIG. 3 generates data by measuring the shape deformation of the eardrum (ED) and external auditory canal (EC) of the ear caused by the eating behavior. Specifically, the sensing device (10) can generate data by measuring the shape deformation by transmitting output light (L1) toward the external auditory canal (EC) of the ear and receiving reflected light (L2) reflected by the outer wall of the external auditory canal (EC) and the eardrum (ED). In other words, the sensing device (10) can generate optical signal data corresponding to the shape deformation of the ear.

[0076] To generate optical data, the sensing device (10) may include a light emitting unit (11-1) and a light receiving unit (11-2). The light emitting unit (11-1) may transmit output light (L1) having a predetermined frequency to the external auditory canal (EC), and the light receiving unit (11-2) may receive reflected light (L2) that is the output light (L1) transmitted by the light emitting unit (11-1) and reflected within the ear. The sensing device (10) may generate optical signal data by electrically converting the amount of the received reflected light (L2).

[0077] FIG. 4 is a third embodiment of a sensing device according to an embodiment of the present invention.

[0078] The sensing device (10) illustrated in FIG. 4 may be a device that measures acoustic signal data among parameters related to the ear.

[0079] Human eating behavior can be mainly divided into chewing and swallowing. Chewing occurs when the upper and lower teeth collide or grind while chewing food. This grinding and grinding between the teeth can cause deformations in the eardrum (ED) and external auditory canal (EC). Swallowing occurs when food moves through the oral cavity, pharynx, and esophagus before reaching the stomach. As food moves through the oral cavity, pharynx, and esophagus, the eardrum (ED) and external auditory canal (EC), which are connected to the oral cavity, pharynx, and esophagus, are deformed.

[0080] The sensing device (10) illustrated in FIG. 4 generates data by measuring the shape deformation of the eardrum (ED) and external auditory canal (EC) of the ear caused by the eating behavior. Specifically, the sensing device (10) can generate data by measuring the shape deformation by transmitting an output sound wave (A1) toward the external auditory canal (EC) of the ear and receiving a reflected sound wave (A2) reflected by the outer wall of the external auditory canal (EC) and the eardrum (ED). That is, the sensing device (10) can generate acoustic signal data corresponding to the shape deformation of the ear.

[0081] To generate acoustic signal data, the sensing device (10) may include a speaker (12-1) and a microphone (12-2). The speaker (12-1) refers to a device that transmits sound. The speaker (12-1) can transmit an output sound wave (A1) having a predetermined frequency to the external auditory canal (EC). According to one embodiment, the output sound wave (A1) transmitted from the speaker (12-1) may have a frequency range of 15 kHz or more. According to another embodiment, the output sound wave (A1) transmitted from the speaker (12-1) may have a frequency range of 20 kHz or more. Since acoustic signals in a frequency range such as those in the above embodiments are difficult for humans to perceive, the disruption of daily life caused by the operation of the sensing device (10) can be minimized. The microphone (12-2) refers to a device that receives sound. The microphone (12-2) can receive a sound wave having a predetermined frequency. The sensing device (10) may refer to time series data of the received reflected sound wave (A2). The frequency range of the reflected sound wave (A2) received by the microphone (12-2) may be different from the frequency range of the output sound wave (A1) transmitted from the speaker (12-1).

[0082] FIG. 5 is a fourth embodiment of a sensing device according to an embodiment of the present invention.

[0083] The sensing device (10) illustrated in FIG. 5 may be a device that measures pressure signal data among ear-related parameters.

[0084] Human eating behavior can be mainly divided into chewing and swallowing. In the case of chewing, the lower and upper teeth collide or grind while chewing food. At this time, the collision and grinding between the teeth causes changes in the shape of the external auditory canal (EC) and the eardrum (ED), resulting in changes in pressure inside the external auditory canal (EC). In the case of swallowing, food moves into the stomach through the oral cavity, pharynx, and esophagus. At this time, as the food moves through the oral cavity, pharynx, and esophagus in that order, the shapes of the external auditory canal (EC) and the eardrum (ED), which are connected to the oral cavity, pharynx, and esophagus, change, and this causes changes in the pressure (P) inside the external auditory canal (EC).

[0085] The sensing device (10) illustrated in Fig. 5 measures the pressure (P) in the ear generated by the eating behavior. The sensing device (10) is placed in a human ear and can measure the pressure (P) inside the external auditory canal (EC) generated during eating, thereby generating pressure signal data.

[0086] Figure 6 is a configuration diagram of a meal monitoring device according to an embodiment of the present invention.

[0087] Referring to FIG. 6, a meal monitoring device (30) according to an embodiment of the present invention includes a data receiving unit (31), a first estimated time calculating unit (32), a second estimated time calculating unit (33), and a meal time calculating unit (34). In addition, the meal monitoring device (30) may further include a meal amount calculating unit (35) and an information providing unit (36).

[0088] First, the data receiving unit (31) receives measurement data of parameters related to the ear of the target object and blood sugar data of the target object. Here, the measurement data of parameters related to the ear may include at least one of vibration signal data, optical signal data, acoustic signal data, and pressure signal data.

[0089] Next, the first estimated time calculation unit (32) calculates a first estimated time, which is estimated to be the food consumption activity time of the target object, based on the measurement data.

[0090] Specifically, the first estimated time calculation unit (32) can compare the feature values ​​calculated from the measurement data with the first reference value corresponding to the feature value. The feature values ​​may vary depending on the type of the measurement data, and the first reference value may vary depending on each feature value. In addition, the first estimated time calculation unit (32) can calculate the first estimated time based on the comparison result between the feature values ​​and the first reference value.

[0091] Various types of measurement data generated from the ear can be measured based on various actions of the target object, in addition to the act of eating. For example, measurement data can be generated based on various activities of the target object, such as talking, coughing, and walking. Therefore, to increase the accuracy of the first estimated time, the accuracy of distinguishing between eating and non-eating actions must be improved. Accordingly, the present invention improves the accuracy of the first estimated time using various embodiments.

[0092] According to one embodiment, the measurement data may be vibration signal data. The first estimated time calculation unit (32) may generate a harmonic pattern of the vibration signal data based on frequency information included in the vibration signal data. In addition, the first estimated time calculation unit (32) may determine whether the similarity between the harmonic pattern and a preset reference harmonic pattern is greater than or equal to a first threshold. In addition, the first estimated time calculation unit (32) may determine whether the frequency information is included in a predetermined frequency range. Then, the first estimated time calculation unit (32) may calculate a time section in which the similarity is greater than or equal to the first threshold and the frequency information is included in the predetermined range as the first estimated time.

[0093] In general, vibrations generated in the ear (tympanic membrane, external auditory canal, etc.) can be generated not only through eating but also through various other activities such as talking, coughing, and walking. Therefore, the present invention distinguishes between eating and non-eating activities by utilizing the harmonic patterns of vibration signals corresponding to eating activities such as chewing and swallowing. However, considering the diversity of non-eating activities, non-eating activities may have harmonic patterns similar to eating activities. Therefore, the present invention improves the accuracy of estimating the time of eating activities by comparing frequency ranges in addition to comparing harmonic patterns.

[0094] According to another embodiment, the measurement data may be optical signal data. The first estimated time calculation unit (32) may calculate the amount of change in light using information regarding the amount of light included in the optical signal data. In addition, the first estimated time calculation unit (32) may compare the amount of change in light with a first threshold range. Then, the first estimated time calculation unit (32) may calculate the time interval in which the amount of change in light falls within the first threshold range as the first estimated time.

[0095] In general, the shape of the ear (tympanic membrane, external auditory canal, etc.) can change through various activities, such as talking, coughing, and walking, in addition to eating. Changes in the shape of the ear can result in changes in the distance from a given point (e.g., the entrance of the external auditory canal) to the eardrum or to the outer wall of the external auditory canal. These changes in the shape of the ear can have similar forms depending on each activity. Therefore, the present invention distinguishes between eating and non-eating activities by comparing the amount of change in reflected light with a predetermined threshold range, thereby improving the accuracy of estimating the time of eating.

[0096] According to another embodiment, the measurement data may be acoustic signal data. The first estimated time calculation unit (32) may generate an acoustic pattern of the acoustic signal data based on acoustic information included in the acoustic signal data. Furthermore, the first estimated time calculation unit (32) may compare the acoustic pattern with a preset reference acoustic pattern to calculate a similarity. Then, the first estimated time calculation unit (32) may calculate a time interval in which the similarity is greater than or equal to a second threshold as the first estimated time.

[0097] In general, the shape of the ear (tympanic membrane, external auditory canal, etc.) can change through various activities, such as talking, coughing, and walking, in addition to eating. Changes in the shape of the ear can result in changes in the distance from a given point (e.g., the entrance of the external auditory canal) to the eardrum or the distance to the outer wall of the external auditory canal. These changes in the shape of the ear can have similar forms depending on each activity. Therefore, the present invention improves the accuracy of estimating the time of eating by differentiating between eating and non-eating activities by comparing the pattern of reflected sound waves with a predetermined reference pattern.

[0098] According to another embodiment, the measurement data may be pressure signal data. The first estimated time calculation unit (32) may calculate power and the amount of change in power using the pressure information included in the pressure signal data. In addition, the first estimated time calculation unit (32) may determine whether the magnitude of the force is greater than or equal to a third threshold. In addition, the first estimated time calculation unit (32) may determine whether the amount of change in force is included within a second threshold range. The first estimated time calculation unit (32) may calculate a time section in which the magnitude of the force is greater than or equal to the third threshold and the amount of change in force is included within the second threshold range as the first estimated time.

[0099] In general, the pressure generated in the ear (tympanic membrane, external auditory canal, etc.) can be generated through various activities such as talking, coughing, and walking in addition to the act of eating. Therefore, the present invention distinguishes between non-meal activities and eating activities by using the energy amount calculated from the pressure signal according to eating activities such as chewing and swallowing. In addition, in light of the diversity of non-meal activities, non-meal activities may have energy amounts similar to eating activities. Therefore, the present invention distinguishes between eating activities and non-meal activities by comparing the duration of the energy amount with a predetermined range value in addition to comparing the energy amount, thereby improving the accuracy of estimating the time of eating activities.

[0100] The embodiments related to the first estimated time calculation unit (32) above may be implemented independently, but are not limited thereto. They may also be implemented as a combination of at least two embodiments related to the first estimated time calculation unit (32).

[0101] Next, the second estimated time calculation unit (33) calculates a second estimated time, which is estimated to be the glucose absorption time of the target object, based on the blood sugar data.

[0102] Specifically, the second estimated time calculation unit (33) can compare the blood sugar value included in the blood sugar data with a second reference value corresponding to the blood sugar value. Here, the second reference value can be set based on the expected blood sugar level that may increase according to food intake, and can be set in a customized manner for the user. In addition, the second estimated time calculation unit (33) can calculate the second estimated time based on the result of the comparison between the blood sugar value and the second reference value. For example, the second estimated time calculation unit (33) can calculate the time interval in which the blood sugar value is higher than the second reference value as the second estimated time.

[0103] Next, the meal time calculation unit (34) calculates the meal time of the target object based on the first estimated time and the second estimated time.

[0104] Specifically, the meal time calculation unit (34) can calculate a reference time range based on the second estimated time. Then, the meal time calculation unit (34) can select a third estimated time that exists within the reference time range among the first estimated times. In addition, the meal time calculation unit (34) can detect the earliest estimated start time and the latest estimated end time among the third estimated times. In addition, the meal time calculation unit (34) can calculate the meal time based on the estimated start time and the estimated end time.

[0105] Even if the estimated time determined to be a food intake activity is calculated by the first estimated time calculation unit (32), the time of activities other than eating may be calculated as the first estimated time due to various activities other than food intake. Accordingly, the present invention improves the accuracy of meal time prediction by calculating the meal time of the target object through a comparison of the first estimated time and the second estimated time.

[0106] The meal amount calculation unit (35) calculates the meal amount consumed by the subject at meal time based on the measurement data and blood sugar data.

[0107] Specifically, the meal amount calculation unit (35) can calculate the meal amount consumed by the subject during the meal time calculated based on the blood sugar level included in the blood sugar level data. According to one embodiment, the meal amount can be calculated by applying the average value of the blood sugar levels corresponding to the meal time to an algorithm for meal amount calculation. In this case, the algorithm for meal amount calculation may be pre-stored in the meal monitoring device (30).

[0108] The information provision unit (36) provides at least one of the calculated meal time information and meal amount information.

[0109] According to one embodiment, the information providing unit (36) may provide at least one of meal time information and meal amount information using a display device or audio device mounted on the meal monitoring device (30). According to another embodiment, the information providing unit (36) may provide at least one of meal time information and meal amount information to an external terminal device that is in communication with the meal monitoring device (30).

[0110] Figure 7 is a flowchart of a meal monitoring method according to an embodiment of the present invention.

[0111] The meal monitoring method of FIG. 7 can be performed using the meal monitoring device (30) described above.

[0112] Referring to FIG. 7, a meal monitoring method according to an embodiment of the present invention includes steps S710 to S740, and may further include steps S750 to S760.

[0113] First, the data receiving unit (31) receives measurement data of parameters related to the ear of the target object and blood sugar data of the target object (S710). As previously discussed, the measurement data may include at least one of vibration signal data, optical signal data, acoustic signal data, and pressure signal data.

[0114] Next, the first estimated time calculation unit (32) calculates the first estimated time, which is estimated to be the food intake activity time of the target object, based on the measurement data (S720).

[0115] Next, the second estimated time calculation unit (33) calculates a second estimated time, which is estimated to be the glucose absorption time of the target object, based on the blood sugar data (S730).

[0116] Next, the meal time calculation unit (34) calculates the meal time of the target object based on the first estimated time and the second estimated time (S740).

[0117] Next, the meal time calculation unit (34) can calculate the amount of food consumed by the subject at meal time based on the measurement data and blood sugar data (S750).

[0118] Next, the information provision unit (36) can provide at least one of the calculated meal time information and meal amount information (S760).

[0119] Figure 8 is a flowchart detailing step S720 of Figure 7.

[0120] Referring to FIG. 8, step S720 according to an embodiment of the present invention may include steps S810 to S830.

[0121] First, the first estimated time calculation unit (32) can calculate feature values ​​from measurement data (S810). Here, the feature values ​​may vary depending on the type / type of the measurement data.

[0122] Next, the first estimated time calculation unit (32) can compare the feature values ​​calculated from the measurement data with the first reference values ​​corresponding to the feature values ​​(S820). Here, the first reference values ​​can be preset to correspond to each feature value.

[0123] Next, the first estimated time calculation unit (32) can calculate the first estimated time based on the comparison result between the feature value and the first reference value (S830).

[0124] FIG. 9 is a flowchart showing a first embodiment of step S720 of FIG. 7.

[0125] Fig. 9 is a flowchart for a detailed description when the measurement data is vibration signal data. Referring to Fig. 9, step S720 of Fig. 7 may include steps S910 to S960.

[0126] First, the first estimated time calculation unit (32) can generate a harmonic pattern of vibration signal data based on frequency information included in the vibration signal data (S910).

[0127] Next, the first estimated time calculation unit (32) can calculate the similarity between the harmonic pattern generated from the vibration signal data and the preset reference harmonic pattern (S920).

[0128] Next, the first estimated time calculation unit (32) can determine whether the similarity between the harmonic pattern and the preset reference harmonic pattern is greater than or equal to the first threshold (S930).

[0129] If the similarity is greater than a predetermined threshold, the first estimated time calculation unit (32) can determine whether the frequency information is included in a predetermined frequency range (S940).

[0130] When the frequency information is included in a predetermined frequency range, the first estimated time calculation unit (32) can calculate a time interval in which the similarity is greater than or equal to a first threshold and the frequency information is included in the predetermined range as the first estimated time (S950).

[0131] On the other hand, if the similarity is less than the first threshold or if the similarity is greater than the first threshold but the frequency information is not included in a predetermined frequency range, the first estimated time calculation unit (32) can calculate the corresponding time section as not being the first estimated time (S960).

[0132] For convenience of explanation, step S940 is described as being performed after step S930, but this is not limited to this. In other examples, step S940 may be performed before step S930, or steps S930 and S940 may be performed substantially simultaneously.

[0133] FIG. 10 is a flowchart showing a second embodiment of step S720 of FIG. 7.

[0134] Fig. 10 is a flowchart for a detailed description when the measurement data is optical signal data. Referring to Fig. 10, step S720 of Fig. 7 may include steps S1010 to S1040.

[0135] First, the first estimated time calculation unit (32) can calculate the amount of change in light using information about the amount of light included in the optical signal data (S1010).

[0136] Next, the first estimated time calculation unit (32) can compare the amount of change in light with the first critical range (S1020).

[0137] Then, the first estimated time calculation unit (32) can calculate the time interval in which the amount of change in light is included in the first critical range as the first estimated time (S1030).

[0138] On the other hand, if the amount of change in light is not included in the first critical range, the first estimated time calculation unit (32) can calculate the corresponding time section as not being the first estimated time (S1040).

[0139] FIG. 11 is a flowchart showing a third embodiment of step S720 of FIG. 7.

[0140] Fig. 11 is a flowchart for a detailed description when the measurement data is acoustic signal data. Referring to Fig. 11, step S720 of Fig. 7 may include steps S1110 to S1150.

[0141] First, the first estimated time calculation unit (32) can generate an acoustic pattern of acoustic signal data based on acoustic information included in the acoustic signal data (S1110).

[0142] Next, the first estimated time calculation unit (32) can calculate the similarity by comparing the sound pattern with a preset reference sound pattern (S1120).

[0143] Next, the first estimated time calculation unit (32) can determine whether the similarity is greater than or equal to the second threshold (S1130).

[0144] Next, the first estimated time calculation unit (32) can calculate a time interval in which the similarity is greater than or equal to the second threshold as the first estimated time (S1140).

[0145] On the other hand, if the similarity is less than the second threshold, the first estimated time calculation unit (32) can calculate the time interval as not being the first estimated time (S1150).

[0146] Fig. 12 is a flowchart showing a fourth embodiment for step S720 of Fig. 7.

[0147] Fig. 12 is a flowchart for a detailed description when the measurement data is pressure signal data. Referring to Fig. 12, step S720 of Fig. 7 may include steps S1210 to S1250.

[0148] First, the first estimated time calculation unit (32) can calculate power and the amount of change in power using the pressure information included in the pressure signal data (S1210).

[0149] And, the first estimated time calculation unit (32) can determine whether the magnitude of the force is greater than or equal to the third threshold (S1220).

[0150] If the magnitude of the force is greater than or equal to the third threshold, the first estimated time calculation unit (32) can determine whether the amount of change in the force is included within the second threshold range (S1230).

[0151] If the magnitude of the force is greater than or equal to the third threshold and the amount of change in the force is within the second threshold range, the first estimated time calculation unit (32) can calculate the corresponding time section as the first estimated time (S1240).

[0152] On the other hand, if the magnitude of the force is less than the third threshold or the similarity is less than a predetermined threshold or if the magnitude of the force is greater than the third threshold but the amount of change in the force is not included in the second threshold range, the first estimated time calculation unit (32) can calculate the corresponding time section as not being the first estimated time (S1250).

[0153] For convenience of explanation, step S1230 is described as being performed after step S1220, but this is not limited to this. In other examples, step S1230 may be performed before step S1220, or steps S1220 and S1230 may be performed substantially simultaneously.

[0154] Figure 13 is a drawing for explaining a process for calculating meal times according to an embodiment of the present invention.

[0155] According to an embodiment of the present invention, the meal time calculation unit (34) detects the meal time of the target object using the first estimated time and the second estimated time. At this time, the first and second estimated times are used together because it is difficult to ensure the accuracy of the calculated meal time if only the first estimated time or only the second estimated time is used.

[0156] As previously discussed, since the first estimated time utilizes measurement data of ear-related parameters, the time intervals associated with various non-eating behaviors of the target object may also be calculated as the first estimated time. Accordingly, if the mealtime of the target object is calculated solely using the first estimated time, the non-eating behaviors of the target object may be calculated as the mealtime. In other words, it may be difficult to ensure the accuracy of information regarding mealtimes.

[0157] Furthermore, while the second estimated time utilizes blood sugar data, it cannot be determined that a rise in blood sugar is necessarily related to a meal. Blood sugar elevations can be caused by a variety of factors, including not only food intake but also viral infections, sleep deprivation, dehydration, smoking, and medication intake. Therefore, calculating a subject's mealtime solely based on the second estimated time may not guarantee the accuracy of the calculated mealtime.

[0158] Therefore, the present invention provides high accuracy by having the meal time calculation unit (34) calculate the meal time of the target object by using the first estimated time and the second estimated time together.

[0159] Meanwhile, according to one embodiment of the present invention, the first estimated time and the second estimated time may be used to predict various blood sugar-related symptoms. For example, if the first estimated time is determined to be a time zone estimated as a non-meal activity time, or if the second estimated time is determined to be a glucose absorption time, the meal monitoring device (30) can predict and provide information on the possibility of onset of diabetic ketoacidosis, which can cause a rapid rise in blood sugar levels even when the subject has not eaten.

[0160] Below, we detail an example of the process of calculating the meal time of a target object using the first estimated time and the second estimated time.

[0161] First, Fig. 13 (a) shows an example of the first estimated time calculated by the first estimated time calculation unit (32). Fig. 13 (a) shows a total of 12 first estimated times (A1 to A12) as examples.

[0162] Figure 13 (b) shows an example of the second estimated time (B1) calculated by the second estimated time calculation unit (33). Figure 13 (b) shows the time from t3 to t4 as the second estimated time.

[0163] Referring to (b) of Fig. 13, the reference time range (T range ) is calculated based on the second estimated time. In (b) of Fig. 13, the time from t5 to t6 is shown as the second estimated time. According to one embodiment, as shown in (b) of Fig. 13, the reference time range (T range ) is the starting time (t5) of the first time interval (T) earlier than the starting time (t3) of the second estimated time (B1). interval1) can be as fast as the reference time range (T range ) is greater than the end time (t4) of the second estimated time (B1) by a second time interval (T interval2 ) can be as fast as the set time interval (T interval1 and T interval2 ) to determine the reference time range (T range ) is because blood sugar levels do not rise immediately after consuming food. It varies depending on the person, type of food, and food consumption pattern, but blood sugar levels generally rise 10 minutes after consuming food, and reach their highest levels after 60 minutes. Therefore, the present invention provides a reference time range (T) that reflects these phenomena. range ) improves the accuracy of meal time calculation. Meanwhile, the above reference time range (T range ) Time interval used for calculation (T interval1 and T interval2 ) can be set differently depending on the situation of the target object. For example, the reference time range can be calculated using different random times for a person without diabetes, a person with type 1 diabetes, and a person with type 2 diabetes.

[0164] Figure 13(c) shows an example of meal times calculated based on the first estimated times (A1 to A12) and the second estimated time (B1). Figure 13(c) shows the times t1 to t2 as meal times.

[0165] The first estimated time of A1 to A12 shown in (a) of Fig. 13 and the reference time range (T) shown in (b) of Fig. 13 range ), in the case of A1 to A8 among the first estimated times, the reference time range (T range ) is included, but in the case of A9 to A12, the reference time range (T range) is not included. Therefore, the meal time calculation unit (34) calculates the meal time based on the first estimated times of A1 to A8 among the first estimated times of A1 to A12. Specifically, the start time t1 of the first estimated time A1, which is arranged at the earliest time among the first estimated times of A1 to A8, is determined as the start time of the meal. In addition, the end time t2 of the first estimated time A8, which is arranged at the latest time among the first estimated times of A1 to A8, is determined as the end time of the meal.

[0166] Meanwhile, for the first estimated times A9 and A10, although they overlap with the second estimated time (B1), they are not considered mealtimes for the target object. This is because they are judged to be non-mealtime activities that occurred after the meal ended, based on the reference time range calculated using the second estimated time (B1).

[0167] The term '~ part' used in this embodiment means a software or hardware component such as an FPGA (field-programmable gate array) or an ASIC, and the '~ part' performs certain roles. However, the '~ part' is not limited to software or hardware. The '~ part' may be configured to be on an addressable storage medium and may be configured to play one or more processors. Thus, as an example, the '~ part' includes components such as software components, object-oriented software components, class components, and task components, processes, functions, attributes, procedures, subroutines, segments of program code, drivers, firmware, microcode, circuits, data, databases, data structures, tables, arrays, and variables. The functionality provided within the components and '~ parts' may be combined into a smaller number of components and '~ parts' or further separated into additional components and '~ parts'. In addition, the components and '~parts' may be implemented to play one or more CPUs within the device or secure multimedia card. Although the above has been described with reference to embodiments, these are merely examples and do not limit the present invention. Those skilled in the art to which the present invention pertains will appreciate that various modifications and applications not exemplified above are possible without departing from the essential characteristics of the present embodiment. For example, each component specifically shown in the embodiment can be modified and implemented. And, the differences related to such modifications and applications should be interpreted as being included in the scope of the present invention defined in the appended claims.

Claims

1. A data receiving unit that receives measurement data of parameters related to the ears of a target object and blood sugar data of the target object; A first estimated time calculation unit that calculates a first estimated time estimated as the food intake activity time of the target object based on the above measurement data; A second estimated time calculation unit that calculates a second estimated time estimated as the glucose absorption time of the target object based on the blood sugar data; and A meal monitoring device comprising a meal time calculation unit that calculates the meal time of the target object based on the first estimated time and the second estimated time.

2. In paragraph 1, The above first estimated time calculation unit, Compare the feature value calculated from the above measurement data with the first reference value corresponding to the feature value; A meal monitoring device that calculates the first estimated time based on the comparison result between the above characteristic value and the first reference value.

3. In paragraph 1, The second estimated time calculation unit above, Compare the blood sugar value included in the blood sugar data with a second reference value corresponding to the blood sugar value; A meal monitoring device that calculates the second estimated time based on the comparison result between the blood sugar value and the second reference value.

4. In paragraph 1, The above meal time calculation section Calculate a reference time range based on the second estimated time; Select a third estimated time that exists within the reference time range among the first estimated times; Detecting the start estimated time, which is the earliest time among the above third estimated times, and the end estimated time, which is the latest time; A meal monitoring device that calculates the meal time based on the above estimated start time and estimated end time.

5. In paragraph 1, A meal monitoring device further comprising a meal amount calculation unit that calculates the amount of meal consumed by the subject at the meal time based on the measurement data and the blood sugar data.

6. In paragraph 1, Measurement data of the parameters related to the above ears, A meal monitoring device comprising at least one of vibration signal data, optical signal data, acoustic signal data and pressure signal data.

7. In paragraph 6, The above first estimated time calculation unit, Generating a harmonic pattern of the vibration signal data based on frequency information included in the vibration signal data; Determine whether the similarity between the above harmonic pattern and the preset reference harmonic pattern is greater than or equal to a first threshold; Determine whether the above frequency information is included in a predetermined frequency range; A meal monitoring device that calculates a time interval in which the similarity is greater than a predetermined threshold and the frequency information is included in a predetermined range as the first estimated time.

8. In paragraph 6, The above first estimated time calculation unit, Calculating the amount of change in light using information about the amount of light included in the above optical signal data; Compare the above light change amount with the first critical range; A meal monitoring device that calculates a time interval in which the amount of change in the light falls within the first threshold range as the first estimated time.

9. In paragraph 6, The above first estimated time calculation unit, Generating an acoustic pattern of the acoustic signal data based on acoustic information included in the acoustic signal data; Comparing the above sound pattern with a preset reference sound pattern to calculate the similarity; A meal monitoring device that calculates a time interval in which the above similarity is greater than or equal to a second threshold as the first estimated time.

10. In paragraph 6, The above first estimated time calculation unit, Calculate power and the amount of change in power using the pressure information included in the above pressure signal data; Determine whether the magnitude of the above force is greater than or equal to the third threshold; Determine whether the change in the above force is within the second critical range; A meal monitoring device that calculates a time interval in which the magnitude of the force is greater than or equal to the third threshold and the amount of change in the force is included within the second threshold range as the first estimated time.

11. A step of receiving measurement data of parameters related to the ears of a target object and blood sugar data of the target object; A step of calculating a first estimated time, which is estimated to be the food intake activity time of the target object, based on the above measurement data; A step of calculating a second estimated time, which is estimated to be the glucose absorption time of the target object, based on the blood sugar data; and A meal monitoring method comprising: a step of calculating a meal time of the target object based on the first estimated time and the second estimated time.

12. In paragraph 11, The step of calculating the above first estimated time is: A step of comparing a feature value calculated from the above measurement data with a first reference value corresponding to the feature value; and A meal monitoring method comprising: a step of calculating the first estimated time based on a comparison result between the above characteristic value and the first reference value.

13. In paragraph 11, The step of calculating the second estimated time is: A step of comparing a blood sugar value included in the blood sugar data with a second reference value corresponding to the blood sugar value; and A meal monitoring method comprising: a step of calculating the second estimated time based on a comparison result between the blood sugar value and the second reference value.

14. In paragraph 11, The step of calculating the meal time of the above target object is: A step of calculating a reference time range based on the second estimated time; A step of selecting a third estimated time that exists within the reference time range among the first estimated times; A step of detecting a start estimated time, which is the earliest time among the above third estimated times, and an end estimated time, which is the latest time; and A meal monitoring method comprising: a step of calculating the meal time based on the estimated start time and estimated end time.

15. In paragraph 11, A meal monitoring method further comprising a step of calculating the amount of food consumed by the subject at the meal time based on the measurement data and the blood sugar data.

16. In paragraph 11, Measurement data of the parameters related to the above ears, A meal monitoring method comprising at least one of vibration signal data, optical signal data, acoustic signal data and pressure signal data.

17. In paragraph 16, The step of calculating the above first estimated time is: A step of generating a harmonic pattern of the vibration signal data based on frequency information included in the vibration signal data; A step of determining whether the similarity between the above harmonic pattern and the preset reference harmonic pattern is greater than or equal to a first threshold; A step of determining whether the above frequency information is included in a predetermined frequency range; and, A meal monitoring method comprising: a step of calculating a time interval in which the similarity is greater than a predetermined threshold and the frequency information is included in a predetermined range as the first estimated time.

18. In paragraph 16, The step of calculating the above first estimated time is: A step of calculating the amount of change in light using information about the amount of light included in the above optical signal data; A step of comparing the above light change amount with the first threshold range; and A meal monitoring method comprising a step of calculating a time interval in which the amount of change in the light is included in the first threshold range as the first estimated time.

19. In paragraph 16, The step of calculating the above first estimated time is: A step of generating an acoustic pattern of the acoustic signal data based on acoustic information included in the acoustic signal data; A step of calculating similarity by comparing the above sound pattern with a preset reference sound pattern; and A meal monitoring method comprising a step of calculating a time interval in which the similarity is greater than or equal to a second threshold as the first estimated time.

20. In paragraph 16, The step of calculating the above first estimated time is: A step of calculating power and the amount of change in the power using pressure information included in the above pressure signal data; A step of determining whether the magnitude of the above force is greater than or equal to the third threshold; A step of determining whether the change in the above force is within a second critical range; and A meal monitoring method comprising: a step of calculating a time interval in which the magnitude of the force is greater than or equal to the third threshold and the amount of change in the force is included within the second threshold range as the first estimated time.

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