Active intelligent bed interaction system and method based on biofeedback technology

Abstract

The present invention discloses an active smart bed interaction system and method based on biofeedback technology, which belongs to the field of smart home, and includes a physiological data acquisition module, an intelligent data analysis module and a personalized feedback control module; the physiological data acquisition module is communicatively connected to the intelligent data analysis module; the intelligent data analysis module is communicatively connected to the personalized feedback control module. The active smart bed interaction system and method based on biofeedback technology of the present invention, through data acquisition and intelligent analysis, feedbacks health risks, sleep quality, emotional state, and then takes personalized sleep intervention, mattress adjustment, sound and light feedback, personalized health advice and other feedback control measures to improve the user's sleep quality and physiological state, and has good practical value and application prospects.

Description

Technical Field

[0001] The present invention belongs to the field of smart home, and in particular relates to an active smart bed interaction system and method based on biofeedback technology. Background Art

[0002] With the acceleration of the pace of human life and the increase in work pressure, various stress-related diseases are on the rise, such as heart disease, cerebral hemorrhage, depression, anxiety, etc. In the field of disease treatment and health management, how to timely detect health risks and issue warnings to help users relieve stress and adjust their physical and mental state has become an urgent need. At present, traditional ways to cope with stress include diet adjustment, physical exercise, and psychotherapy. Due to the user's level of awareness of the problem, the above methods are difficult to be effectively implemented. Therefore, the development of an active and automated health management system is of great significance to user health.

[0003] In the field of smart beds, existing products and applications focus more on health data collection, with functions concentrated on sleep monitoring and reminders, vital sign data reporting, etc. Although they are helpful to human health, their functions are still relatively simple and lack systematic and proactive health intervention capabilities. For example, smart beds can collect data such as heart rate, respiration, body temperature, and acceleration, and calculate sleep quality based on these data, and send vital sign data to users to help them manage their health. In this process, health information is not actively processed, and it is difficult for users to get advice on stress management and other aspects. In addition, most products mainly provide health feedback on the user's autonomous sleep stage, and lack health intervention functions before sleep.

[0004] In view of the above problems, the present invention proposes an active smart bed interaction system based on biofeedback technology and an implementation method thereof to solve the above problems. Summary of the invention

[0005] In response to one or more of the above defects or improvement needs in the prior art, the present invention provides an active smart bed interaction system and method based on biofeedback technology, which uses data collection and intelligent analysis to feedback health risks, sleep quality, and emotional state, and then adopts personalized sleep intervention, mattress adjustment, sound and light feedback, personalized health advice and other feedback control measures to improve the user's sleep quality and physiological state, and has good practical value and application prospects.

[0006] To achieve the above-mentioned object, the present invention provides an active intelligent bed interaction system based on biofeedback technology, which includes a physiological data acquisition module, an intelligent data analysis module and a personalized feedback control module;

[0007] The physiological data acquisition module includes one or more of an integrated heart rate sensor, a respiration sensor, a skin resistance sensor, a body temperature sensor, and an acceleration sensor, and is used to monitor the user's heart rate, respiration, skin resistance, body temperature, acceleration and other physiological data in real time; and

[0008] The physiological data acquisition module is communicatively connected to the intelligent data analysis module, and is used to process the collected physiological data, and identify health risks and optimize sleep cycles according to the data processing results;

[0009] At the same time, the intelligent data analysis module is communicatively connected with the personalized feedback control module to take multi-dimensional feedback control measures according to the data processing results.

[0010] As a further improvement of the present invention, the physiological data acquisition module also includes an age analysis module for dividing the data according to people of different ages and different physiological states, and thereby optimizing the data acquisition plan in real time.

[0011] As a further improvement of the present invention, the intelligent data analysis module includes a deep learning algorithm module, which builds a data analysis model based on a large amount of users' physiological data, outputs key health information, and performs intelligent prediction based on historical data and user context information to provide decision-making basis for the personalized feedback control module.

[0012] As a further improvement of the present invention, the personalized feedback control module is used to take multi-dimensional feedback actions according to health risks, sleep optimization and emotion regulation information.

[0013] On this basis, a method for realizing active intelligent bed interaction based on biofeedback technology is provided, the method comprising the following steps:

[0014] S100 uses a heart rate sensor, breathing sensor, skin resistance sensor, body temperature sensor, and acceleration sensor embedded in the mattress to obtain physiological data of the human body;

[0015] S200 uses machine learning algorithms and big data analysis technology to deeply mine and analyze the data in step 1, identify health risks, sleep cycles, emotional states, and predict health problems;

[0016] S300: Based on the analysis results of S200, the smart bed takes multi-dimensional feedback control measures.

[0017] As a further improvement of the present invention, in step S100, the physiological data collected includes heart rate, respiration, skin resistance, body temperature and acceleration.

[0018] At the same time, the collection time of physiological data is dynamically adjusted according to the user's sleep state to reduce the impact of data collection on sleep.

[0019] As a further improvement of the present invention, in step S200, the following steps are specifically included:

[0020] S201, constructing a sleep cycle recognition module based on machine learning: using a supervised learning algorithm to establish a mapping relationship between user-specific physiological data and sleep states;

[0021] S202, construct an emotion recognition module based on Bayesian network: use supervised and unsupervised machine learning algorithms to link the user's psychological state with physiological indicators; based on the sleep cycle, realize the time series analysis and evaluation of the emotional state;

[0022] S203, constructing a health status prediction module based on deep learning: using a multimodal deep learning algorithm to associate multiple physiological parameters, establish a prediction model, identify the user's health problems, and take real-time intervention measures.

[0023] As a further improvement of the present invention, in step S201, the following steps are also included:

[0024] Clean and identify abnormal data to ensure data quality.

[0025] As a further improvement of the present invention, in step S300, the multi-dimensional feedback control includes mattress adjustment, sound feedback, light feedback and smart home linkage;

[0026] Mattress feedback adjustment process: Based on the S200's health risk, sleep cycle and emotional state information, the mattress airbag adjustment module adjusts the hardness or temperature in real time;

[0027] Sound feedback and light feedback process: according to different situations, play healthy music, sounds or show relaxing pictures and dynamic images to relieve stress and enhance emotional regulation;

[0028] Smart home linkage process: the user's physiological data and personalized health suggestions are directly fed back to the smart home system to adjust the bedroom temperature, humidity, light and humidification;

[0029] As a further improvement of the present invention, in step S300, the multi-dimensional feedback control also includes a personalized health advice process, and the system analyzes the user's physiological data and gives personalized health advice feedback based on the individual's health status.

[0030] The above-mentioned improved technical features can be combined with each other as long as they do not conflict with each other.

[0031] In general, compared with the prior art, the above technical solution conceived by the present invention has the following beneficial effects:

[0032] (1) The active smart bed interaction system and method based on biofeedback technology of the present invention can provide feedback on health risks, sleep quality, and emotional state through data collection and intelligent analysis, and then take feedback control measures such as personalized sleep intervention, mattress adjustment, sound and light feedback, and personalized health advice to improve the user's sleep quality and physiological state, and has good practical value and application prospects.

[0033] (2) The active smart bed interaction system based on biofeedback technology and its method of the present invention, combined with physiological data collection, intelligent data analysis and personalized feedback control, constructs an active smart bed interaction system based on biofeedback technology. The smart bed can actively sense the user's status, identify health problems, and provide multi-dimensional real-time feedback during the user's sleep to help the user achieve adequate rest and relaxation. The system can automatically interact with the smart home to provide people with healthy and comfortable sleep and home space in a more convenient and comprehensive way. The physiological data collection module is used to monitor the user's dynamic health status, provide health warnings and personalized health suggestions, and combine the machine learning and deep learning algorithms in the intelligent data analysis module to identify health problems, sleep cycles and emotional states, providing decision-making basis for the personalized feedback control module. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 is a composition diagram of an active intelligent bed interaction system based on biofeedback technology in an embodiment of the present invention;

[0035] Figure 2 is a flow chart of an active smart bed interaction method based on biofeedback technology in an embodiment of the present invention; DETAILED DESCRIPTION

[0036] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments.

[0037] It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention. In addition, the technical features involved in each embodiment of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0038] See also Figure 1In the preferred embodiment of the present invention, an active smart bed interaction system based on biofeedback technology includes: To achieve the above-mentioned purpose, the present invention provides an active smart bed interaction system based on biofeedback technology, which includes a physiological data acquisition module, an intelligent data analysis module and a personalized feedback control module; wherein the physiological data acquisition module includes one or more of an integrated heart rate sensor, a respiration sensor, a skin resistance sensor, a body temperature sensor, and an acceleration sensor, which is used to monitor the user's heart rate, respiration, skin resistance, body temperature, acceleration and other physiological data in real time; and the physiological data acquisition module is communicatively connected to the intelligent data analysis module, which is used to process the collected physiological data, and identify health risks and optimize sleep cycles according to the data processing results; at the same time, the intelligent data analysis module is communicatively connected to the personalized feedback control module, which is used to take multi-dimensional feedback control measures based on the data processing results.

[0039] More specifically, the physiological data collection module also includes an age analysis module for dividing the data according to people of different ages and different physiological states, and thereby optimizing the data collection plan in real time.

[0040] In more detail, the intelligent data analysis module uses association rule mining technology of physiological data to analyze health problems, sleep cycles and emotional states from historical data and user context information, and provides health management suggestions through methods such as sleep cycle recognition, emotion recognition and health status prediction, supporting continuous and adaptive personalized health management services.

[0041] Furthermore, the personalized feedback control module in the preferred embodiment of the present invention includes six modules: mattress adjustment, sound feedback, light feedback, smart home linkage, health advice and health advice. The mattress adjustment module provides differentiated and intelligent mattress hardness and temperature control based on health risks, sleep cycles and emotional state information to create the best sleeping environment. The sound feedback module and the light feedback module support healthy music, relaxing pictures and dynamic images to improve the health level of users. The smart home linkage module enables the user's physiological data to be directly fed back to the home system through the seamless docking function with the smart home system, and timely adjusts the indoor environmental parameters such as light, temperature and humidity. The personalized feedback control module combines the health advice module with the health advice module to provide a personalized health management plan.

[0042] In a specific embodiment, the smart home system includes smart speakers, lighting systems, air conditioning temperature regulators (smart water heaters, etc.) modules, provides voice interaction functions for smart beds, and directly feeds back physiological data collection and health advice information to the home system, thereby making timely changes to environmental parameters.

[0043] On this basis, see Figure 2The present invention also provides a method for realizing active intelligent bed interaction based on biofeedback technology, the method comprising the following steps:

[0044] S100 uses a heart rate sensor, breathing sensor, skin resistance sensor, body temperature sensor, and acceleration sensor embedded in the mattress to obtain physiological data of the human body;

[0045] S200 uses machine learning algorithms and big data analysis technology to deeply mine and analyze the data in step 1, identify health risks, sleep cycles, emotional states, and predict health problems;

[0046] S300: Based on the analysis results of S200, the smart bed takes multi-dimensional feedback control measures.

[0047] In step S100, the physiological data collected include heart rate, respiration, skin resistance, body temperature and acceleration.

[0048] At the same time, the collection time of physiological data is dynamically adjusted according to the user's sleep state to reduce the impact of data collection on sleep. Differentiated data collection strategies are adopted for users with different physiological states. In the initial stage of collection, the user's heart rate, respiration, body temperature and acceleration data are monitored in real time to evaluate the user's physiological state. Then, according to the physiological state, the collection time of heart rate, respiration, body temperature and acceleration data is adjusted to achieve the best data collection effect.

[0049] In the collection phase, the collection points of heart rate, respiration, body temperature and acceleration data are optimized. Specifically, the user's sleep state is monitored in real time, the signal strength (i.e., RMS value) of heart rate, respiration, body temperature and acceleration data is calculated, and the time interval of data collection points is adjusted based on the strength.

[0050] In step S200, the following steps are specifically included:

[0051] S201, constructing a sleep cycle recognition module based on machine learning: using a supervised learning algorithm to establish a mapping relationship between user-specific physiological data and sleep states;

[0052] In step S201, the following steps are also included: cleaning and identifying abnormal data to ensure data quality.

[0053] S202, construct an emotion recognition module based on Bayesian network: use supervised and unsupervised machine learning algorithms to link the user's psychological state with physiological indicators; based on the sleep cycle, realize the time series analysis and evaluation of the emotional state;

[0054] S203, constructing a health status prediction module based on deep learning: using a multimodal deep learning algorithm to associate multiple physiological parameters, establish a prediction model, identify the user's health problems, and take real-time intervention measures.

[0055] In the above steps, the quality of the collected raw data is monitored, and abnormal data is screened using anomaly detection technology. Then, feature extraction is performed on heart rate, respiration, body temperature, and acceleration data. The identification of health risks, sleep cycles, and emotional states involves time series analysis and association mining of multi-dimensional data, such as the association between heart rate and blood pressure. At the same time, it is necessary to consider data quality deviations and user context information, such as age, gender, race, medical history, etc. Therefore, in the process of time series analysis and association mining, it is necessary to use deep learning algorithms and reinforcement learning algorithms to conduct deep mining of multi-dimensional data.

[0056] Finally, through personalized data collection strategies, the collected physiological data is more suitable for different users, which improves the data quality and provides better data support for health problem prediction. Through intelligent data analysis, the user's health status information is obtained, and then a multimodal health problem prediction model is built to predict potential health risks and take corresponding risk reduction measures.

[0057] In step S300, the multi-dimensional feedback control includes mattress adjustment, sound feedback, light feedback and smart home linkage; mattress feedback adjustment process: according to the health risk, sleep cycle and emotional state information of S200, the mattress airbag adjustment module adjusts the hardness or temperature in real time; sound feedback and light feedback process: for different situations, play healthy music, sounds or display relaxing pictures and dynamic images to relieve stress and enhance emotional regulation; smart home linkage process: the user's physiological data and personalized health suggestions are directly fed back to the smart home system to adjust the bedroom temperature, humidity, light and humidification; in step S300, the multi-dimensional feedback control also includes a personalized health suggestion process, the system analyzes the user's physiological data, and gives personalized health suggestion feedback according to the individual's health status.

[0058] In the above steps, the mattress airbag adjustment module will adjust the hardness or temperature in real time according to the health risk, sleep cycle and emotional state information to enhance the user experience and optimize sleep. The execution actions of mattress adjustment include the following three aspects: changing the gas volume inside the airbag, changing the mattress material, and adjusting the pressure distribution. The sound feedback and light feedback systems play healthy music, sounds or display relaxing pictures and dynamic images for different situations to relieve user stress and enhance emotional regulation. The system automatically provides appropriate music, such as fast-paced music to increase excitement, white noise to relieve stress, etc. In the sleep optimization scenario, white noise and progressive music can be played according to the user's sleep cycle to help users fall asleep and avoid difficulties in waking up in the morning. The smart home linkage user's physiological data and personalized health suggestions will be directly fed back to the smart home system to adjust environmental parameters such as bedroom temperature, humidity, light and humidification.

[0059] Finally, the personalized health advice system analyzes the user's physiological data and provides personalized health advice feedback based on the individual's health status, such as fitness plans based on heart rate changes and methods to improve sleep quality.

[0060] During the data collection process, first determine whether the user intends to fall asleep. If the intention is to sleep, perform data collection and quality monitoring. If the intention is not to sleep, continue data collection and judgment until there is an intention to sleep, and then perform health management collection and evaluation. In the health management collection and evaluation link, first use multi-module-level feature extraction and association mining methods to achieve sleep cycle recognition, emotional state recognition, and health problem prediction. Then, based on the results of health management collection and evaluation, take multi-dimensional feedback control measures. If the health status is normal, execute the health status optimization and health knowledge push link. If there is a health problem, execute the health problem handling link.

[0061] In the health problem handling stage, a health risk level assessment is provided based on the health monitoring results to determine its urgency. If the health risk is not serious, health status optimization and health advice push are implemented; if the health risk is serious, health intervention is implemented. Health problems can be alleviated through personalized adjustment of bed hardness and temperature, healthy music and video push, and one-click call for help. In the health status optimization and health advice push stage, personalized health advice is taken and implemented based on the health status assessment results. If the user feedback shows that health has been improved, health status optimization and health advice push will continue to be implemented until the user confirms that it has returned to a comfortable state, ending the health management stage. If it has not been optimized, it will return to the health problem handling and take better health intervention measures. The system generates personalized feedback plans through the data analysis engine for feedback, and supports user voice interaction and control of smart homes.

[0062] (1) The active smart bed interaction system and method based on biofeedback technology of the present invention can provide feedback on health risks, sleep quality, and emotional state through data collection and intelligent analysis, and then take feedback control measures such as personalized sleep intervention, mattress adjustment, sound and light feedback, and personalized health advice to improve the user's sleep quality and physiological state, and has good practical value and application prospects.

[0063] (2) The active smart bed interaction system based on biofeedback technology and its method of the present invention, combined with physiological data collection, intelligent data analysis and personalized feedback control, constructs an active smart bed interaction system based on biofeedback technology. The smart bed can actively sense the user's status, identify health problems, and provide multi-dimensional real-time feedback during the user's sleep to help the user achieve adequate rest and relaxation. The system can automatically interact with the smart home to provide people with healthy and comfortable sleep and home space in a more convenient and comprehensive way. The physiological data collection module is used to monitor the user's dynamic health status, provide health warnings and personalized health suggestions, and combine the machine learning and deep learning algorithms in the intelligent data analysis module to identify health problems, sleep cycles and emotional states, providing decision-making basis for the personalized feedback control module.

[0064] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. An active smart bed interactive system based on biofeedback technology, characterized in that: It includes a physiological data acquisition module, an intelligent data analysis module and a personalized feedback control module; The physiological data acquisition module includes one or more of an integrated heart rate sensor, a respiration sensor, a skin resistance sensor, a body temperature sensor, and an acceleration sensor, and is used to monitor the user's heart rate, respiration, skin resistance, body temperature, acceleration and other physiological data in real time; and The physiological data acquisition module is communicatively connected to the intelligent data analysis module, and is used to process the collected physiological data, and identify health risks and optimize sleep cycles according to the data processing results; At the same time, the intelligent data analysis module is communicatively connected with the personalized feedback control module to take multi-dimensional feedback control measures according to the data processing results.

2. The active smart bed interactive system based on biofeedback technology according to claim 1, wherein: The physiological data collection module also includes an age analysis module, which is used to divide the data according to people of different ages and different physiological states, and thereby optimize the data collection plan in real time.

3. The active smart bed interactive system based on biofeedback technology according to claim 1, wherein: The intelligent data analysis module includes a deep learning algorithm module, which builds a data analysis model based on a large amount of user physiological data, outputs key health information, and performs intelligent prediction based on historical data and user context information to provide decision-making basis for the personalized feedback control module.

4. The active smart bed interactive system based on biofeedback technology according to claim 1, wherein: The personalized feedback control module is used to take multi-dimensional feedback actions according to health risks, sleep optimization and emotion regulation information.

5. A method for implementing active intelligent bed interaction based on biofeedback technology, characterized in that: The method comprises the following steps: S100 uses a heart rate sensor, breathing sensor, skin resistance sensor, body temperature sensor, and acceleration sensor embedded in the mattress to obtain physiological data of the human body; S200 uses machine learning algorithms and big data analysis technology to deeply mine and analyze the data in step 1, identify health risks, sleep cycles, emotional states, and predict health problems; S300: Based on the analysis results of S200, the smart bed takes multi-dimensional feedback control measures.

6. The active smart bed interaction implementation method based on biofeedback technology according to claim 5, wherein: In step S100, the physiological data collected includes heart rate, respiration, skin resistance, body temperature and acceleration. At the same time, the collection time of physiological data is dynamically adjusted according to the user's sleep state to reduce the impact of data collection on sleep.

7. The active smart bed interaction implementation method based on biofeedback technology according to claim 5, wherein: In step S200, the following steps are specifically included: S201, constructing a sleep cycle recognition module based on machine learning: using a supervised learning algorithm to establish a mapping relationship between user-specific physiological data and sleep states; S202, construct an emotion recognition module based on Bayesian network: use supervised and unsupervised machine learning algorithms to link the user's psychological state with physiological indicators; based on the sleep cycle, realize the time series analysis and evaluation of the emotional state; S203, constructing a health status prediction module based on deep learning: using a multimodal deep learning algorithm to associate multiple physiological parameters, establish a prediction model, identify the user's health problems, and take real-time intervention measures.

8. The active smart bed interaction implementation method based on biofeedback technology according to claim 7, wherein: In step S201, the following steps are also included: Clean and identify abnormal data to ensure data quality.

9. The active smart bed interaction implementation method based on biofeedback technology according to claim 5, wherein: In step S300, the multi-dimensional feedback control includes mattress adjustment, sound feedback, light feedback and smart home linkage; Mattress feedback adjustment process: Based on the S200's health risk, sleep cycle and emotional state information, the mattress airbag adjustment module adjusts the hardness or temperature in real time; Sound feedback and light feedback process: according to different situations, play healthy music, sounds or show relaxing pictures and dynamic images to relieve stress and enhance emotional regulation; Smart home linkage process: The user's physiological data and personalized health recommendations are directly fed back to the smart home system to adjust the bedroom temperature, humidity, light and humidification.

10. The method for implementing active intelligent bed interaction based on biofeedback technology according to claim 9, wherein: In step S300, the multi-dimensional feedback control also includes a personalized health advice process, in which the system analyzes the user's physiological data and gives personalized health advice feedback based on the individual's health status.

Images