A behavioral decision-making model construction system for people at high risk of stroke

Abstract

The present invention discloses a system for constructing a behavioral decision model for people at high risk of stroke. The system comprises a cloud processor, a data import module, a component analysis module and a mandibular recovery detection module. The data import module matches meal categories according to the patient's weight value and the amount of food required for daily activities and records and uploads the meal proportions. The component analysis module receives the food measurement data from the data import module. When the patient's occlusal muscle outward expansion recovery training reaches a cycle, the patient assembles and uses the mandibular recovery detection module, that is, opens the mouth and applies force so that the mandibular support unit applies downward pressure to the mandibular activity measurement unit due to the outward expansion force, so that the downward pressure value generated by the mandibular activity measurement unit when expanding outward is recorded and sent to the cloud processor through the data transmission unit. The cloud processor models the state before and after recovery and the food intake data during recovery.

Description

Technical Field

[0001] The present invention relates to the technical field of patient behavior decision-making, and in particular to a system for constructing a behavior decision-making model for people at high risk of stroke. Background Art

[0002] Stroke is a group of diseases that mainly cause brain tissue damage due to sudden rupture of brain blood vessels or blood vessel blockage that prevents blood from flowing into the brain. After a severe stroke occurs, the patient's local trunk or muscles cannot function normally. When high-risk stroke patients are in the initial treatment state of the hospital, when their oral occlusal muscles cannot open normally and eat due to the impact of stroke, they need to develop external expansion rehabilitation training for the occlusal muscles to achieve the purpose of recovery. However, how to collect the rehabilitation effect data of each training cycle after the patient's periodic rehabilitation training, and how to use the model to extract the impact of the behavioral decision on the recovery trend of the disease, has not been well solved. To this end, we propose a behavioral decision model construction system for high-risk stroke populations. Summary of the invention

[0003] The main purpose of the present invention is to provide a behavioral decision-making model construction system for people at high risk of stroke.

[0004] To achieve the above-mentioned purpose, the technical solution adopted by the present invention is: a system for constructing a behavioral decision model for a high-risk population of stroke, the system comprising a cloud processor, a data import module, a component analysis module and a jaw recovery detection module, the data import module matches the meal categories according to the patient's weight value and the amount of food required for the day's activities and records and uploads the meal ratio, the component analysis module analyzes and processes the components contained in the food after receiving the food measurement data from the data import module, the jaw recovery detection module is used to measure the recovery of the patient's occlusal muscle external expansion state, the jaw recovery detection module comprises a head fixing unit, a side positioning unit, a jaw activity measuring unit, a jaw supporting unit and a data transmission unit, the head fixing unit is worn on the patient's head for position fixing, the side positioning unit is worn on the patient's ear for anti-displacement limiting, the jaw activity measuring unit is arranged between the jaw supporting unit and the head fixing unit for measuring the recovery completion of the patient's mouth closing muscle training, and the data transmission unit is used to upload the measured data to the cloud processor.

[0005] A further improvement of the present invention is that the data import module includes a patient weighing unit for measuring the patient's own weight, a meal quantity counting unit for collecting weight data of various types of food eaten by the patient, a training duration counting unit for collecting the duration of the patient's jaw muscle recovery training, and an information entry unit for centrally processing the collected data and sending it via wireless signals.

[0006] A further improvement of the present invention is that the ingredient analysis module includes a trace element analysis unit for analyzing trace elements contained in food according to the food portion, an organic matter analysis unit for analyzing organic matter contained in the food, a nutritional analysis unit for analyzing the carbohydrate, fat and protein content of the food, and a microprocessor for receiving and analyzing the signal transmitted by the information entry unit.

[0007] A further improvement of the present invention is that the cloud processor is used to receive information sent by the data import module, the component analysis module and the mandibular restoration detection module, and to construct a model after cloud computing processing and upload it to the server.

[0008] A further improvement of the present invention is that a snap-on back neck anti-slip belt is provided at one end of the lower jaw supporting unit.

[0009] A further improvement of the present invention is that a construction process of a system for constructing a behavioral decision-making model for a high-risk population of stroke includes:

[0010] S1. Before high-risk patients with stroke whose occlusal muscle outward expansion state is affected and need recovery training, the mandibular recovery detection module is used to measure the outward expansion force that can be generated by the patient's mouth closing muscles when opening and closing in the current unrecovered state, and the first measurement data is imported into the cloud processor for recording;

[0011] S2. After the patient's occlusal muscle external expansion recovery training, the patient's own weight on each eating day and the duration of the occlusal muscle recovery training are entered into the information input unit, and after the calculation is completed, the daily food intake data for maintaining normal body condition according to the set ratio is sent to the component analysis module through an infinite signal, so that after the component analysis module receives the daily food intake data for maintaining normal body condition according to the set ratio, the component analysis module calculates the components contained in the obtained food amount after the ratio is obtained, and transmits the calculated data to the cloud processor;

[0012] S3. When the degree of recovery is measured after completing the outward expansion recovery training of the occlusal muscle and eating according to the plan for a cycle, the head fixing unit is worn on the head and the side positioning unit is placed on the outside of the patient's ear to prevent it from falling off. At this time, the mandibular support unit is placed on the patient's mandibular part to support it. The patient opens his mouth and applies force so that the mandibular support unit exerts downward pressure on the mandibular activity measurement unit due to the outward expansion force, so that the downward pressure value generated by the mandibular activity measurement unit when expanding outward is recorded and sent to the cloud processor through the data transmission unit. The cloud processor models the data of the state before and after recovery and the amount of food eaten during recovery, and uploads the model data to the server for subsequent processing and analysis of the recovery state of the outward expansion of the occlusal muscle after the recovery training and the eating plan.

[0013] Compared with the prior art, the present invention measures the outward expansion force that can be generated when the patient's mouth closing muscles are opened and closed in the current unrecovered state through a mandibular recovery detection module, imports the first measurement data into the cloud processor for recording, and performs measurement and recording after a cycle of the patient's occlusal muscle outward expansion recovery training. After the patient assembles and uses the mandibular recovery detection module, the mandibular support unit applies force to the mandibular activity measurement unit due to the outward expansion force, so that the downward pressure value generated by the mandibular activity measurement unit when expanding outward is recorded and sent to the cloud processor through the data transmission unit. The cloud processor models the data of the states before and after recovery and the amount of food intake during recovery, and uploads the model data to the server for subsequent processing and analysis of the occlusal muscle outward expansion recovery state after the recovery training plan.

[0014] Compared with the prior art, the present invention records the amount of food required for the patient's weight through the patient weighing unit and the training time counting unit for the day's active rehabilitation occlusal muscle recovery training time, and then sets the food amount ratio that meets the patient's needs for the day based on the above data and enters it through the meal amount counting unit. Thereafter, the information entry unit processes the set food amount data recorded by the meal amount counting unit and sends it to the microprocessor, so that the microprocessor receives the daily food amount set with the ratio to maintain normal body condition, calculates the components contained in the obtained food amount after ratio through the trace element analysis unit, the organic matter analysis unit and the nutrition analysis unit, and uploads them to the cloud processor, so that after completing the cycle training, the cloud processor constructs a model of the influence of the training cycle's eating nutrition data on the patient's recovery speed, which is used to process and analyze the influence of subsequent eating nutrition data on the patient's rehabilitation training. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 This is a composition diagram of a system for building a behavioral decision-making model for people at high risk of stroke according to the present invention.

[0016] Figure 2 The present invention is a schematic diagram of a data import module and a component analysis module in a system for constructing a behavioral decision-making model for a high-risk population of stroke.

[0017] Figure 3 The present invention is a schematic diagram of a mandibular recovery detection module in a system for constructing a behavioral decision-making model for a high-risk population of stroke.

[0018] In the figure: 1. Cloud processor; 2. Data import module; 21. Information entry unit; 22. Patient weight measurement unit; 23. Meal quantity counting unit; 24. Training time counting unit; 3. Component analysis module; 31. Microprocessor; 32. Trace element analysis unit; 33. Organic matter analysis unit; 34. Nutritional analysis unit; 4. Mandibular recovery detection module; 41. Head fixing unit; 42. Side positioning unit; 43. Mandibular movement measurement unit; 44. Mandibular support unit; 441. Clip-on back neck anti-slip belt; 45. Data transmission unit. DETAILED DESCRIPTION

[0019] The present invention is further described below in conjunction with specific embodiments, wherein the accompanying drawings are only used for exemplary descriptions and represent only schematic diagrams rather than actual drawings, and should not be understood as limitations on this patent. In order to better illustrate the specific embodiments of the present invention, some parts of the accompanying drawings may be omitted, enlarged or reduced, and do not represent the size of the actual product. For those skilled in the art, it is understandable that some well-known structures and their descriptions in the accompanying drawings may be omitted. Based on the specific embodiments of the present invention, all other specific embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0020] Example 1

[0021] like Figure 1-3 As shown, a system for constructing a behavioral decision model for people at high risk of stroke includes a cloud processor 1, a data import module 2, a component analysis module 3 and a jaw recovery detection module 4. The data import module 2 is used to record and upload the patient's weight value and the type and ratio information of the meal required for food intake. The component analysis module 3 analyzes and processes the components contained in the food after receiving the food intake information data from the data import module 2. The jaw recovery detection module 4 is used to measure the recovery of the patient's occlusal muscle external expansion state. The jaw recovery detection module 4 includes a head fixing unit 41, a side positioning unit 42, a jaw activity measuring unit 43, a jaw supporting unit 44 and a data transmission unit 45. The head fixing unit 41 is worn on the patient's head for position fixing, the side positioning unit 42 is worn on the patient's ear for anti-displacement limiting, the jaw activity measuring unit 43 is arranged between the jaw supporting unit 44 and the head fixing unit 41 for measuring the recovery completion of the patient's mouth closing muscle training, and the data transmission unit 45 is used to upload the measured data to the cloud processor 1.

[0022] Before a single group of high-risk patients need recovery training due to the influence of the external expansion state of the bite muscle in the stroke state in the hospital, the mandibular recovery detection module 4 can be used to measure the external expansion force that can be generated when the patient's mouth closing muscles are opened and closed in the current unrecovered state, and the external expansion force data measured by the mandibular recovery detection module 4 for the first time is imported into the cloud processor 1 for recording. After the patient's external expansion recovery training of the bite muscle, the patient's own weight on each meal day is entered into the information entry unit 21 through the patient weight measurement unit 22 and the duration of the bite muscle recovery training is entered into the information entry unit 21 through the training duration counting unit 24. After the calculation is completed, the daily consumption data of maintaining normal body condition according to the ratio setting is entered into the information entry unit 21 through the meal quantity counting unit 23, and then sent to the component analysis module 3 through the unlimited WIFI signal, so that after the component analysis module 3 receives the daily consumption data of maintaining normal body condition according to the ratio setting, the component analysis module 3. Calculate the ingredients contained in the food amount after the known proportion, and transmit the calculated data to the cloud processor 1. When the degree of recovery is measured after completing the external expansion recovery training of the occlusal muscle and reaching a cycle, the head fixing unit 41 is worn on the head for fixation, and the side positioning unit 42 is placed on the outside of the patient's ear to achieve anti-falling limit. At this time, the mandibular support unit 44 is placed on the patient's mandibular part to support it. The patient opens his mouth and applies force so that the mandibular support unit 44 exerts downward pressure on the mandibular activity measuring unit 43 due to the external expansion force, so that the downward pressure value generated by the mandibular activity measuring unit 43 when expanding outward is recorded and sent to the cloud processor 1 through the data transmission unit 45. The cloud processor 1 is used to model the data before and after the rehabilitation recovery, and the model data is uploaded to the server for subsequent analysis of the external expansion recovery state of the occlusal muscle of a single patient after the eating plan and the recovery training are carried out simultaneously.

[0023] Among them, the data import module 2 includes a patient weighing unit 22 for measuring the patient's own weight, a meal quantity counting unit 23 for collecting weight data of various types of food eaten by the patient, a training time counting unit 24 for counting and collecting the duration of the patient's occlusal muscle recovery training, and an information entry unit 21 for centrally processing the collected data and sending it via a wireless signal; the patient weighing unit 22 is used to collect and record the patient's own weight during the rehabilitation process, and then upload the data to calculate the normal food supply information required for the patient's weight consumption and upload it to the information entry unit 21; the nutritional analysis unit 34 is used to record the duration of the occlusal muscle opening and closing training during the patient's rehabilitation process and enter the data transmission information into the unit 21, thereby recording the patient's training volume during the rehabilitation cycle for the construction of a subsequent decision-making model.

[0024] The component analysis module 3 includes a trace element analysis unit 32 for analyzing the trace elements contained in the food according to the amount of food, an organic matter analysis unit 33 for analyzing the organic matter contained in the food, a nutritional analysis unit 34 for analyzing the carbon water, fat and protein content of the food, and a microprocessor 31 for receiving and analyzing the signal transmitted by the information input unit 21; after the required amount of food is transmitted to the nutritional analysis unit 34 through the information input unit 21, the potassium and magnesium elements contained in the food can be analyzed by the trace element analysis unit 32 because potassium and magnesium elements can relieve blood vessel pressure and play a role in lowering blood pressure. Rich in content, since vitamin C can regulate cholesterol metabolism and effectively inhibit the development of arteriosclerosis, it can assist in the treatment of stroke to a certain extent. At the same time, vitamin C can also improve blood vessel quality and effectively prevent blood vessel rupture. The vitamin C content in the required food can be analyzed by the organic matter analysis unit 33, and the nutritional analysis unit 34 can analyze the carbohydrate, fat and protein content in the food, so as to know whether the daily food intake reaches the normal supply of carbohydrate, fat and protein. The model constructed by the above data is transmitted to the server for subsequent calculation of the influence of food intake on the recovery effect of patient's occlusal muscle expansion.

[0025] Among them, the cloud processor 1 is used to receive the information sent by the data import module 2, the component analysis module 3 and the mandibular recovery detection module 4, and construct a model after cloud computing processing and upload it to the server; the cloud processor 1 constructs the behavior model by receiving the information sent by the data import module 2, the component analysis module 3 and the mandibular recovery detection module 4, and constructs the model after cloud computing processing, and uploads the constructed model to the server for subsequent comparative analysis of the data.

[0026] Among them, one end of the chin supporting unit 44 is provided with a snap-on back neck anti-slip belt 441; when the chin supporting unit 44 is supported on the bottom of the patient's chin, the snap-on part of the snap-on back neck anti-slip belt 441 is released, and after the chin supporting unit 44 is wrapped around the back of the patient's neck, the snap-on part of the snap-on back neck anti-slip belt 441 is fastened to achieve wearing positioning.

[0027] Example 2

[0028] like Figure 1-3As shown, a system for constructing a behavioral decision model for people at high risk of stroke includes a cloud processor 1, a data import module 2, a component analysis module 3 and a jaw recovery detection module 4. The data import module 2 is used to record and upload the patient's weight value and the type and ratio information of the meal required for food intake. The component analysis module 3 analyzes and processes the components contained in the food after receiving the food intake information data from the data import module 2. The jaw recovery detection module 4 is used to measure the recovery of the patient's occlusal muscle external expansion state. The jaw recovery detection module 4 includes a head fixing unit 41, a side positioning unit 42, a jaw activity measuring unit 43, a jaw supporting unit 44 and a data transmission unit 45. The head fixing unit 41 is worn on the patient's head for position fixing, the side positioning unit 42 is worn on the patient's ear for anti-displacement limiting, the jaw activity measuring unit 43 is arranged between the jaw supporting unit 44 and the head fixing unit 41 for measuring the recovery completion of the patient's mouth closing muscle training, and the data transmission unit 45 is used to upload the measured data to the cloud processor 1.

[0029] Among them, the data import module 2 includes a patient weighing unit 22 for measuring the patient's own weight, a meal quantity counting unit 23 for collecting weight data of various types of food eaten by the patient, a training duration counting unit 24 for collecting the duration of the patient's jaw muscle recovery training, and an information entry unit 21 for centrally processing the collected data and sending it via wireless signals.

[0030] Among them, the ingredient analysis module 3 includes a trace element analysis unit 32 for analyzing the trace elements contained in food according to the food portion, an organic matter analysis unit 33 for analyzing the organic matter contained in the food, a nutritional analysis unit 34 for analyzing the carbohydrate, fat and protein content of the food, and a microprocessor 31 for receiving and analyzing the signal transmitted by the information input unit 21.

[0031] The cloud processor 1 is used to receive information sent by the data import module 2, the component analysis module 3 and the mandibular restoration detection module 4, and to construct a model after cloud computing processing and upload it to the server.

[0032] One end of the lower jaw supporting unit 44 is provided with a buckle-type rear neck anti-slip belt 441

[0033] The parts of this embodiment that are the same as those in Example 1 are not repeated here. The difference from Example 1 is that before a single group of high-risk patients in the hospital with stroke have their occlusal muscle external expansion state affected and need recovery training, the mandibular recovery detection module 4 can be used to measure the external expansion force that can be generated when the patient's mouth closing muscles are opened and closed in the current unrecovered state, and the external expansion force data measured for the first time by the mandibular recovery detection module 4 is imported into the cloud processor 1 for recording. After a cycle of occlusal muscle recovery training for the patient, the degree of recovery is re-measured by the mandibular recovery detection module 4, and the data before and after the measurement are sent to the cloud processor 1. The cloud processor 1 is used to model the data before and after rehabilitation recovery, and the model data is uploaded to the server for subsequent analysis of the occlusal muscle external expansion recovery state of a single patient before and after the simultaneous recovery training.

[0034] Example 3

[0035] like Figure 1-3 As shown, a system for constructing a behavioral decision model for people at high risk of stroke includes a cloud processor 1, a data import module 2, a component analysis module 3 and a jaw recovery detection module 4. The data import module 2 is used to record and upload the patient's weight value and the type and ratio information of the meal required for food intake. The component analysis module 3 analyzes and processes the components contained in the food after receiving the food intake information data from the data import module 2. The jaw recovery detection module 4 is used to measure the recovery of the patient's occlusal muscle external expansion state. The jaw recovery detection module 4 includes a head fixing unit 41, a side positioning unit 42, a jaw activity measuring unit 43, a jaw supporting unit 44 and a data transmission unit 45. The head fixing unit 41 is worn on the patient's head for position fixing, the side positioning unit 42 is worn on the patient's ear for anti-displacement limiting, the jaw activity measuring unit 43 is arranged between the jaw supporting unit 44 and the head fixing unit 41 for measuring the recovery completion of the patient's mouth closing muscle training, and the data transmission unit 45 is used to upload the measured data to the cloud processor 1.

[0036] Among them, the data import module 2 includes a patient weighing unit 22 for measuring the patient's own weight, a meal quantity counting unit 23 for collecting weight data of various types of food eaten by the patient, a training duration counting unit 24 for collecting the duration of the patient's jaw muscle recovery training, and an information entry unit 21 for centrally processing the collected data and sending it via wireless signals.

[0037] Among them, the ingredient analysis module 3 includes a trace element analysis unit 32 for analyzing the trace elements contained in food according to the food portion, an organic matter analysis unit 33 for analyzing the organic matter contained in the food, a nutritional analysis unit 34 for analyzing the carbohydrate, fat and protein content of the food, and a microprocessor 31 for receiving and analyzing the signal transmitted by the information input unit 21.

[0038] The cloud processor 1 is used to receive information sent by the data import module 2, the component analysis module 3 and the mandibular restoration detection module 4, and to construct a model after cloud computing processing and upload it to the server.

[0039] One end of the lower jaw supporting unit 44 is provided with a buckle-type rear neck anti-slip belt 441

[0040] The same parts as those in Example 1 will not be repeated in this embodiment. The difference from Example 1 is that, by adopting the form of group control, the recovery degree of patients in different groups can be measured through the data import module 2 after the patients with symptoms of occlusal muscle expansion treatment who have adopted precise control of diet and the patients with symptoms of occlusal muscle expansion treatment who have not adopted precise control of diet have undergone periodic rehabilitation training, and the measured data of patients in different groups are constructed through data modeling by the cloud processor 1. After the model data is uploaded to the server, it can be used for subsequent analysis of the recovery state of the external expansion of the occlusal muscle after recovery training in different food intake control states, which can be used for control analogy after the later model construction.

[0041] The above shows and describes the basic principles and main features of the present invention and the advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention to be protected. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A system for constructing a behavioral decision-making model for people at high risk of stroke, characterized in that: The system comprises a cloud processor (1), a data import module (2), a component analysis module (3) and a mandibular recovery detection module (4): the data import module (2) is used to record and upload the patient's weight value and the food category ratio information of the required food intake; the component analysis module (3) analyzes and processes the components contained in the food after receiving the food intake information data from the data import module (2); the mandibular recovery detection module (4) is used to measure the recovery of the patient's occlusal muscle external expansion state, and the mandibular recovery detection module (4) comprises a head fixing unit (41), a side positioning unit (42), a mandibular activity measuring unit (43), a mandibular support unit (44) and a data transmission unit (45); the head fixing unit (41) is worn on the patient's head for position fixing, the side positioning unit (42) is worn on the patient's ear for anti-displacement limiting, and the mandibular activity measuring unit (43) is arranged between the mandibular support unit (44) and the head The fixing unit (41) is used to measure the degree of recovery completion of the patient's mouth closing muscle training, and the data transmission unit (45) is used to upload the measured data to the cloud processor (1); the lower jaw recovery detection module (4) performs the measurement through the following steps: by wearing the head fixing unit (41) on the head, and placing the side positioning unit (42) into the outer sleeve of the patient's ear to achieve anti-drop limit, at this time, the lower jaw support unit (44) is placed on the patient's lower jaw to support the lower jaw part, and the patient opens his mouth and applies force so that the lower jaw support unit (44) exerts downward pressure on the lower jaw activity measurement unit (43) due to the outward expansion force, so that the downward pressure value generated by the lower jaw activity measurement unit (43) when expanding outward is recorded and sent to the cloud processor (1) through the data transmission unit (45), and the state before and after recovery and the food intake data during recovery are modeled and constructed by the cloud processor (1), and the model data is uploaded to the server for subsequent processing and analysis of the recovery state of the outer expansion of the occlusal muscle after the recovery training and the food intake plan.

2. A system for constructing a behavioral decision model for a high-risk population of stroke according to claim 1, characterized in that: The data import module (2) comprises a patient weighing unit (22) for measuring the patient's own weight, a meal quantity counting unit (23) for collecting weight data of various types of food eaten by the patient, a training duration counting unit (24) for collecting the duration of the patient's occlusal muscle recovery training, and an information input unit (21) for centrally processing the collected data and sending it via wireless signals.

3. A system for constructing a behavioral decision model for a high-risk population of stroke according to claim 2, characterized in that: The component analysis module (3) comprises a trace element analysis unit (32) for analyzing trace elements contained in food according to the amount of food, an organic matter analysis unit (33) for analyzing organic matter contained in the food, a nutritional analysis unit (34) for analyzing the carbohydrate, fat and protein content of the food, and a microprocessor (31) for receiving and analyzing the signal transmitted by the information input unit (21).

4. A system for constructing a behavioral decision model for a high-risk population of stroke according to claim 1, characterized in that: The cloud processor (1) is used to receive information sent by the data import module (2), the component analysis module (3) and the mandibular restoration detection module (4), and to perform cloud computing processing to form a model and upload it to the server.

5. A system for constructing a behavioral decision model for a high-risk population of stroke according to claim 1, characterized in that: One end of the lower jaw supporting unit (44) is provided with a buckled rear neck anti-slip belt (441).

6. The method for constructing a behavioral decision-making model construction system for a high-risk population of stroke according to claim 1, characterized in that: include: S1. Before a high-risk patient with a stroke whose external expansion state of the occlusal muscles is affected and needs recovery training, the jaw recovery detection module (4) measures the external expansion force generated by the patient's mouth closing muscles when they are opened and closed in the current unrecovered state, and imports the first measurement data into the cloud processor (1) for recording; S2. After the patient has undergone the occlusal muscle external expansion recovery training, the patient's own body weight on each eating day and the duration of the occlusal muscle recovery training are entered into the information input unit (21), and after the calculation is completed, the daily food intake data for maintaining a normal body condition according to the set ratio is sent to the component analysis module (3) through an infinite signal, so that after the component analysis module (3) receives the daily food intake data for maintaining a normal body condition according to the set ratio, the component analysis module (3) calculates the components contained in the obtained food amount after the ratio is obtained, and transmits the calculated data to the cloud processor (1); S3. When the degree of recovery is measured after completing the outward expansion recovery training of the occlusal muscles and eating according to the plan for a period, the head fixing unit (41) is worn on the head for fixation, and the side positioning unit (42) is placed on the outside of the patient's ear to prevent it from falling off. At this time, the mandibular support unit (44) is placed on the patient's mandibular part to support it. The patient opens his mouth and applies force so that the mandibular support unit (44) exerts downward pressure on the mandibular activity measurement unit (43) due to the outward expansion force, so that the downward pressure value generated by the mandibular activity measurement unit (43) when expanding outward is recorded and sent to the cloud processor (1) through the data transmission unit (45). The cloud processor (1) models the state before and after recovery and the amount of food eaten during recovery, and uploads the model data to the server for subsequent processing and analysis of the outward expansion recovery state of the occlusal muscles after the recovery training and the eating plan.

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