Intelligent dinner plate health management device and method supporting BMI linkage

Abstract

The invention relates to the technical field of intelligent health equipment and digital nutriology, in particular to an intelligent dinner plate health management device and method supporting BMI linkage, and the device comprises a dinner plate main body, and also comprises a user body data collection module which is integrated on the dinner plate main body and is used for collecting BMI and body fat rate data of a user; the multispectral food ingredient analysis system is integrated in the dinner plate main body and is used for analyzing food nutritional ingredients in real time; according to the invention, accurate analysis of nutritional ingredients is realized: through a multispectral food ingredient analysis system (the wavelength range is 400-1000nm, the resolution ratio of a spectral sensor is 1nm, and the processing rate reaches 1 million times per second), the limitation that the nutritional ingredients are estimated only by image recognition traditionally is broken through, and the accuracy of the analysis of the nutritional ingredients is improved. The laboratory-level real-time component analysis capability is provided, and the data is accurate and comprehensive.

Description

Technical Field

[0001] This invention relates to the field of intelligent health devices and digital nutrition technology, specifically to an intelligent plate health management device and method that supports BMI linkage. Background Technology

[0002] With the increasing awareness of health management, various smart health devices are emerging, but they still have obvious shortcomings in terms of the accuracy and systematic nature of diet management.

[0003] In existing technologies, such as the intelligent health machine, health data generation method, and health data management system proposed in patent publication number CN112669963B, health data is collected and analyzed by integrating multiple modules such as camera, voice, and physiological feature acquisition, and health plans are generated. However, this technology focuses on comprehensive health analysis and does not provide specific real-time guidance for dietary behavior. Moreover, due to the large number of modules and complex structure, the cost is high, making it difficult to popularize in daily dining scenarios.

[0004] In summary, existing health management devices often focus on single functions, such as only measuring the body or recording diet, lacking integration of the entire process of body data, dietary behavior, and nutritional recommendations, and thus failing to guarantee data accuracy and timely recommendations in various scenarios. Summary of the Invention

[0005] The purpose of this invention is to provide a smart plate health management device and method that supports BMI linkage, so as to solve the problems mentioned in the background art.

[0006] The objective of this invention can be achieved through the following technical solutions: A smart plate health management device supporting BMI linkage includes a plate body and also includes: The user body data collection module is integrated into the main body of the plate and is used to collect the user's BMI and body fat percentage data. A multispectral food composition analysis system is integrated inside the main body of the plate for real-time analysis of food nutrients. The dietary behavior monitoring module is used to monitor the user's chewing frequency, eating speed, and emotional state; The central processing unit (CPU) is used to receive and integrate data from various modules. The personalized nutrition recommendation module is used to generate dynamic nutrition recommendation plans; The main body of the plate, the user's body data collection module, and the dietary behavior monitoring module are all electrically connected to the central processing unit to send the collected data to the central processing unit. The personalized nutrition recommendation module is also electrically connected to the central processing unit to generate dynamic nutrition recommendation plans based on the data and send them to the central processing unit.

[0007] Preferably, the user body data acquisition module includes electrode pads and an infrared sensor integrated on the plate handle.

[0008] Preferably, the dietary behavior monitoring module includes: Miniature accelerometers are used to detect chewing frequency; The feeding speed detection unit is used to calculate the feeding speed; An emotion state monitoring system, including a heart rate sensor and a facial expression recognition module.

[0009] Preferably, the operating wavelength range of the multispectral food composition analysis system is 400-1000 nm.

[0010] Preferably, the user body data acquisition module further includes a dynamic weight compensation system for correcting measurement errors.

[0011] Preferably, the central processing unit includes an artificial intelligence model module and an adaptive learning algorithm module. The adaptive learning algorithm module is a neural network model that is updated daily based on user historical data. The personalized nutrition recommendation module includes a dynamic adjustment mechanism that adjusts the recommendations within a range of ±10% every minute.

[0012] Preferably, the intelligent plate health management device further includes: The user interface is electrically connected to the central processing unit and is used to display recommended solutions and receive user feedback.

[0013] A smart plate health method that supports BMI linkage includes the following steps: S1. Collect and dynamically compensate user body composition data through the user body data collection module; S2. Real-time collection of dietary components and behavioral data through a multispectral food composition analysis system and a dietary behavior monitoring module; S3. The artificial intelligence model module of the central processing unit performs fusion analysis on body composition data, dietary composition data and behavioral data; S4. Based on the analysis results, the personalized nutrition recommendation module generates a dynamic nutrition recommendation plan that is linked to the user's real-time BMI. S5. Feedback the recommended solution to the user through the user interface and collect feedback information for optimization of the adaptive learning algorithm module.

[0014] Preferably, in step S2, the user's emotional data collected by the emotional state monitoring system is used as the input dimension for generating a nutrition recommendation plan.

[0015] Preferably, in step S4, the dynamic nutrition recommendation scheme is dynamically adjusted based on the eating progress and real-time user data.

[0016] The beneficial effects of this invention are: This invention enables precise analysis of nutritional components: through a multispectral food component analysis system (wavelength range 400-1000nm, spectral sensor resolution 1nm, processing rate up to 1 million times / second), it breaks through the limitations of traditional methods that rely solely on image recognition to estimate nutritional components, providing laboratory-level real-time component analysis capabilities with accurate and comprehensive data.

[0017] This invention improves the reliability of body data detection: the dynamic weight compensation system corrects errors caused by grip posture in real time through an algorithm module, so that the error of the BMI value measured in dynamic scenarios is less than ±0.5%, solving the global problem that the measurement accuracy of portable devices is affected by the way they are used.

[0018] This invention enables real-time dynamic optimization of recommendation schemes: an adaptive learning algorithm module trained on more than 1 million dietary and health data points can update the user model daily, and the dynamic adjustment mechanism can fine-tune the recommendations by ±10% every minute, completely overcoming the lag and unsuitability of traditional static nutrition recommendation schemes.

[0019] This invention pioneers a new dimension of emotion and diet management: by using an emotion state monitoring system (facial expression recognition accuracy of 95%, heart rate detection accuracy of ±1 beat / minute) as a key input dimension for nutrition recommendations, it provides unprecedented personalization depth and achieves unexpectedly good user experience results. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort. Figure 1 This is a schematic diagram of the overall structure of the present invention; Figure 2 yes Figure 1 A schematic diagram of the main structure of the intelligent dining plate; Figure 3 yes Figure 2 A schematic diagram of the structure of a multispectral food composition analysis system. Figure 4 yes Figure 1 A schematic diagram of the user body data collection module in the middle section; Figure 5 yes Figure 4A schematic diagram of the dynamic weight compensation system in the middle section; Figure 6 yes Figure 1 A schematic diagram of the structure of the dietary behavior monitoring module; Figure 7 yes Figure 6 A schematic diagram of the structure of the emotional state monitoring system. Figure 8 yes Figure 1 A schematic diagram of the central processing unit (CPU). Figure 9 yes Figure 1 A schematic diagram of the personalized nutrition recommendation module in the image; Figure 10 yes Figure 1 A schematic diagram of the structure of the user interface section.

[0021] The attached figures are labeled as follows: 1. Plate body; 2. User body data acquisition module; 3. Dietary behavior monitoring module; 4. Central processing unit; 5. Personalized nutrition recommendation module; 6. User interface; 11. Weight sensor array; 12. Image recognition camera; 13. Temperature sensor; 14. Pressure distribution sensor; 15. Multispectral food composition analysis system; 21. BMI detection unit; 22. Body fat percentage detection unit; 23. Dynamic weight compensation system; 31. Chewing frequency detection unit; 32. Eating speed detection unit; 3. Emotional state monitoring system; 41. Data processing chip; 42. Artificial intelligence model module; 43. Adaptive learning algorithm module; 51. Nutrition database; 52. Recommendation algorithm module; 53. Dynamic adjustment mechanism; 61. Touch screen; 62. Voice prompt module; 63. Signal transceiver; 151. Multispectral light source; 152. Spectral sensor; 153. Data analysis module; 231. Pressure sensor; 232. Compensation algorithm module; 331. Heart rate sensor; 332. Facial expression recognition module. Detailed Implementation

[0022] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] like Figures 1-10 The intelligent plate health management device of the present invention adopts a modular design, with each component working together. The specific structure includes: The main body of the plate consists of: 1. User body data collection module; 2. Dietary behavior monitoring module; 3. Central processing unit; 4. Personalized nutrition recommendation module; 5. User interaction interface; 6.

[0024] The main body of the plate 1 includes: The weight sensor array 11 is distributed and embedded in each compartment of the bottom of the plate to detect the weight of the food placed on it and its changes.

[0025] Image recognition camera 12, mounted on a miniature bracket above or to the side of the plate, is used to capture images of food to identify its type and location.

[0026] Temperature sensor 13 is embedded in the inner wall of the plate to detect the temperature of the food.

[0027] Pressure distribution sensors 14 are distributed around the bottom of the plate to monitor the pressure distribution when the user holds the plate.

[0028] The multispectral food composition analysis system 15, integrated inside the plate, includes a multispectral light source 151, a spectral sensor 152, and an embedded data analysis module 153, which is used to emit light of a specific wavelength and receive the reflected spectrum to analyze the macro-nutrients in the food (such as protein, fat, and carbohydrate content).

[0029] User body data acquisition module 2 is integrated into the tray handle or a separate handheld unit, including: The BMI detection unit 21 uses electrode pads on the handle to detect the user's weight, estimate height, and calculate BMI value through bioelectrical impedance analysis (BIA).

[0030] The body fat percentage detection unit 22 uses an infrared sensor inside the handle to assist in measuring body fat percentage.

[0031] The dynamic weight compensation system 23 includes a pressure sensor 231 integrated into the handle and a built-in compensation algorithm module 232, which is used to correct the BMI measurement value in real time according to the user's grip strength and posture, and eliminate measurement errors.

[0032] Dietary behavior monitoring module 3 includes: The chewing frequency detection unit 31 uses a miniature accelerometer installed at the bottom of the plate to estimate the chewing frequency by detecting the tiny vibrations transmitted from the jawbone.

[0033] The eating speed detection unit 32 is connected to the weight sensor array 11 and determines the eating speed by calculating the rate at which the weight of food decreases per unit time.

[0034] The emotional state monitoring system 33 includes a heart rate sensor 331 (such as a PPG optical heart rate sensor) integrated on a plate or handle and a user-facing miniature camera (as a facial expression recognition module 332) for collecting user heart rate data and facial images and analyzing their emotional state while eating.

[0035] Central processing unit 4 is a main control circuit board integrated inside the device, including: The data processing chip 41 is used to receive, preprocess, and integrate raw data from the above modules.

[0036] Artificial intelligence model module 42, with built-in deep learning algorithms, is used to model and analyze the fused multimodal data and identify dietary patterns.

[0037] The adaptive learning algorithm module 43 uses neural network technology to continuously optimize model parameters based on long-term historical data and real-time feedback from users.

[0038] Personalized nutrition recommendation module 5 runs as software on central processing unit 4 or in the cloud, including: Nutrition Database 51 stores detailed nutritional information for food.

[0039] The recommendation algorithm module 52, based on the analysis results of the AI ​​model, calls the nutrition database 51 and combines it with the user's real-time BMI and body fat percentage target to generate personalized dietary recommendations.

[0040] The dynamic adjustment mechanism 53 can adjust the recommended content in real time based on the progress of eating and user behavior feedback.

[0041] User interface 6 includes: The touch display screen 61, integrated into the surface of the plate or used as a mobile terminal application, is used to visually display nutritional data and recommended solutions.

[0042] The voice prompt module 62 is used to broadcast prompts and suggestions.

[0043] The transceiver 63, such as a Wi-Fi / Bluetooth module, is used for data transmission and synchronization with smartphones and cloud servers.

[0044] The workflow of this invention includes four core steps: (1) Multi-source data acquisition stage The sensor array on the main body of the plate 1 collects data on food weight, image, temperature and composition in real time; The user's body data acquisition module 2 is automatically activated when the hand is held, and completes the BMI and body fat percentage detection within 30 seconds. The dietary behavior monitoring module 3 tracks and records chewing frequency, eating speed, and mood changes throughout the entire process.

[0045] (2) Data fusion and processing stage Data processing chip 41 performs spatiotemporal alignment and standardization processing on multi-source data; Artificial intelligence model module 42 uses deep learning algorithms to establish a model linking diet, body, and behavior. The adaptive learning algorithm module 43 combines historical data to update model parameters in real time.

[0046] (3) Intelligent recommendation generation stage Recommendation algorithm module 52 extracts baseline data from nutrition database 51; Personalized nutrition plans are generated based on users' real-time BMI and body fat percentage data. The dynamic adjustment mechanism 53 optimizes the plan every minute based on the progress of eating.

[0047] (4) Human-computer interaction feedback stage The touch display screen 61 presents recommended content in a visual manner; The voice prompt module 62 provides real-time dining guidance; User feedback data is uploaded to the cloud via transceiver 63 for algorithm iteration and optimization.

[0048] Technical effects of the present invention: 1. Specific technological achievements Through laboratory testing and user trials, the present invention has achieved the following quantitative results: (1) Improved accuracy of nutrient composition analysis The multispectral analysis system enables precise identification of major nutrients such as proteins, fats, and carbohydrates within the 400-1000nm wavelength range. Verified by a third-party testing agency, the error rate of nutrient composition analysis is less than 3%, which is more than 40% higher than that of traditional image recognition methods. (2) Enhanced reliability of body data measurement The dynamic weight compensation system monitors the grip status in real time through the pressure sensor 231, and the compensation algorithm module 232 controls the BMI measurement error to within ±0.5%. Compared to traditional handheld devices, measurement stability is improved by 60%; (3) The intelligence level of the recommendation system has been improved. The adaptive learning algorithm is trained on more than 1 million dietary health data points, and the model is automatically updated daily. The dynamic adjustment mechanism achieves precise control of ±10% per minute, with a 92% compliance rate with recommended solutions; (4) Expansion of behavioral monitoring dimensions The emotion state monitoring system achieves an emotion recognition accuracy rate of 95% through multimodal data fusion. Heart rate detection accuracy is ±1 beat / minute, providing a new data dimension for nutrition recommendations; 2. Advantages in Technological Innovation (1) Breaking through the limitations of traditional detection The multispectral food composition analysis system 15 uses composite spectral technology to overcome the shortcomings of single visual recognition in composition analysis and realizes laboratory-level real-time nutrient composition detection. (2) Solving the problem of dynamic measurement The dynamic weight compensation system 23, through innovative algorithm design, effectively eliminates the influence of the user's holding posture on the measurement, and solves the measurement accuracy problem of portable devices in dynamic scenarios; (3) Achieve truly personalized recommendations The collaborative work of the adaptive learning algorithm module 43 and the dynamic adjustment mechanism 53 ensures that the recommendation scheme can adapt to changes in user status in real time, overcoming the lag of traditional static recommendation schemes. (4) Pioneering emotional diet management The introduction of the emotional state monitoring system 33 provides an innovative data dimension for nutrition recommendations, realizes the coordinated management of physiological indicators and psychological state, and achieves an unexpected improvement in user satisfaction.

[0049] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention.

Claims

1. A smart plate health management device supporting BMI linkage, comprising a plate body (1), characterized in that, Also includes: User body data acquisition module (2) integrated on the tray body (1) for collecting user's BMI and body fat data; Multispectral food composition analysis system (15) integrated inside the tray body (1) for real-time analysis of food nutrients; Dietary behavior monitoring module (3) for monitoring user's chewing frequency, eating speed and emotional state; Central processing unit (4) for receiving and fusing data from various modules; Personalized nutrition recommendation module (5) for generating dynamic nutrition recommendation scheme; Wherein, the tray body (1), the user body data acquisition module (2), the dietary behavior monitoring module (3) are electrically connected with the central processing unit (4) for sending the detected and collected data to the central processing unit, the personalized nutrition recommendation module (5) and the central processing unit (4) are electrically connected for producing dynamic nutrition recommendation scheme according to the data and sending to the central processing unit (4).

2. The intelligent plate management device according to claim 1, wherein, The user body data acquisition module (2) includes electrode pieces (21) and infrared sensors (22) integrated on the tray handle.

3. The BMI-linked intelligent plate management device and method according to claim 2, wherein, The dietary behavior monitoring module (3) includes: Micro-accelerometer (31) for detecting chewing frequency; Eating speed detection unit (32) for calculating eating speed; Emotional state monitoring system (33) including heart rate sensor (331) and facial expression recognition module (332).

4. The intelligent plate management device and method supporting BMI linkage according to claim 1, wherein, The working wavelength range of the multispectral food composition analysis system (15) is 400-1000nm.

5. The intelligent plate management device according to claim 2, wherein, The user body data acquisition module (2) further includes a dynamic weight compensation system (23) for correcting measurement error.

6. The intelligent plate management device according to claim 3, wherein, The central processing unit (4) includes an artificial intelligence model module (42) and an adaptive learning algorithm module (43), which is a neural network model, updated daily based on user's historical data; The personalized nutrition recommendation module (5) contains a dynamic adjustment mechanism (53) for adjusting the recommended content by ±10% every minute.

7. The intelligent plate management system according to claim 1, wherein, The intelligent tray health management device further includes: User interaction interface (6) electrically connected with the central processing unit (4) for displaying the recommended scheme and receiving user feedback.

8. A smart plate health management method supporting BMI linkage, characterized in that, The intelligent tray health management device supporting BMI linkage based on claim 1, the intelligent tray health management method supporting BMI linkage, comprising the following steps: S1, acquiring user's body composition data through user body data acquisition module (2) and after dynamic compensation; S2, real-time collection of dietary composition and behavior data through multispectral food composition analysis system (15) and dietary behavior monitoring module (3); S3, fusion analysis of body composition data, dietary composition data and behavior data through artificial intelligence model module (42) of central processing unit (4); S4, generating dynamic nutrition recommendation scheme linked with user's real-time BMI based on analysis results through personalized nutrition recommendation module (5); S5, feedback the recommended scheme to the user through the user interaction interface (6), and collect the feedback information for optimization of the adaptive learning algorithm module (43).

9. The BMI-linked intelligent plate management method according to claim 8, wherein, In the step S2, the user emotion data collected by the emotion state monitoring system (33) is used as an input dimension for generating the nutrition recommended scheme.

10. The intelligent plate management method supporting BMI linkage according to claim 8, wherein, In the step S4, the dynamic nutrition recommended scheme is dynamically adjusted according to the eating schedule and the real-time data of the user.

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