Home monitoring system and method for chronic diseases of old people based on Internet of Things

Abstract

The invention discloses an old people chronic disease home monitoring system and method based on the Internet of Things, and relates to the technical field of old people chronic diseases, and the system obtains the diet data of the old people home through an Internet of Things device, and determines the cognitive problem of the old people based on the diet data of the old people home; acquiring living activity data of the old people, and determining a mode for intervening cognition of the old people based on the living activity data of the old people; intervening the cognitive problem of the old people according to the cognitive problem of the old people; interference is carried out through the Internet of Things equipment, the elderly can obtain the nutritional ingredients, health benefits and related information of influence on the illness state of the elderly, wrong cognition is broken through through the information, the elderly can be helped to intake enough nutrients such as protein, carbohydrate, fat, vitamins and mineral substances, and the possibility that the problem of insufficient nutrition occurs is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of chronic diseases in the elderly, and in particular to a home monitoring system for chronic diseases in the elderly based on the Internet of Things and a method thereof. Background Art

[0002] Chronic diseases in the elderly are often lifelong, prone to recurrence and slow progression, requiring long-term monitoring. They are characterized by persistence, long-term duration, and the coexistence of multiple diseases. In recent years, with the development of smart wearable devices and internet healthcare, home monitoring technology has gradually become intelligent and convenient. The elderly can use smart devices to understand their health status in real time. Using various medical equipment and technologies, they can monitor the vital signs, disease indicators, and living conditions of elderly chronic disease patients at home, so as to promptly identify problems, adjust treatment plans, and improve the quality of life of elderly patients.

[0003] However, for elderly people with chronic diseases, due to their own erroneous cognition, they may have a wrong rejection mentality towards food and excessively reject certain types of food. For example, they mistakenly believe that all meats will aggravate high blood pressure, and that diabetics cannot eat any fruits, etc.; the elderly only accept a small amount of food, resulting in insufficient nutrition intake, which may lead to malnutrition, decreased immunity and other problems. In severe cases, the risk of complications will increase. Summary of the Invention

[0004] The technical problem solved by the present invention is to correct the cognitive misunderstandings of the elderly through scientific intervention and improve the quality of life of elderly patients.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a home monitoring system for chronic diseases in the elderly based on the Internet of Things, comprising a device management module, a data analysis module and a data storage module; the device management module and the data storage module are interconnected to obtain the activity data of the elderly and intervene in the cognitive problems of the elderly; the output end of the data storage module and the input end of the data analysis module are interconnected to store the activity data of the elderly and the information data obtained by the elderly from Internet of Things devices and real life; the data analysis module and the device management module are interconnected to determine the cognitive problems of the elderly and the information presented to the elderly by the Internet of Things devices.

[0006] Specifically, the device management module also includes an input unit, a data acquisition unit, an intervention unit and a network analysis unit; the input unit is used to input home diet data and chronic disease data of the elderly; the data acquisition unit is used to obtain information data and activity data obtained by the elderly from real life; the intervention unit intervenes in the cognitive problems of the elderly; the network analysis unit is used to determine the dietary taboo information of the elderly based on the input chronic disease data, and at the same time extract the information obtained by the elderly from the Internet of Things devices to determine the information content obtained by the elderly.

[0007] Specifically, the data analysis module also includes a cognitive analysis unit, an intervention analysis unit and an optimization unit; the cognitive analysis unit obtains information on chronic diseases suffered by the elderly, determines dietary taboos for chronic diseases, obtains information on components of elderly people's resistance behaviors other than dietary taboos, and determines cognitive problems of the elderly; the intervention analysis unit is used to determine the impact of information presented by IoT devices to the elderly on the elderly; the optimization unit determines the information presented by IoT devices to the elderly through genetic algorithms.

[0008] Specifically, the cognitive analysis unit extracts the elderly’s intake rate of food i based on their home diet data. i , f i =n1 / n2; where n1 represents the number of times the elderly consume the i-th food, and n2 represents the number of times the i-th food appears in the family diet; obtain the elderly's intake rate of all foods, and determine the elderly's intake rate of the i-th food f i Is there an abnormally low situation? If so, it is judged that the elderly have aversion to the i-th food. If not, the elderly do not have aversion to the i-th food. Determine all foods that the elderly have aversion to, and let f j Indicates the intake rate of the jth food that the elderly resist; let Aj represent the jth food that the elderly resist. According to the main component information contained in Aj, obtain the intake rate of other foods with the same main components as Aj, and judge f j Is there an abnormally low situation? If f j If there is no abnormally low situation, it is determined that the elderly have resistance to the main components contained in Aj; if f j If there is an abnormally low level, it is determined that the elderly have no resistance to the main components contained in Aj; obtain information on chronic diseases suffered by the elderly, determine the dietary taboos for chronic diseases, and for information on components that the elderly have resistance to other than the dietary taboos, determine the cognitive problems of the elderly.

[0009] As a preferred solution of the method for home monitoring of chronic diseases in the elderly based on the Internet of Things described in the present invention, the method comprises: obtaining the dietary data of the elderly at home through the Internet of Things devices, and determining the cognitive problems of the elderly based on the dietary data of the elderly at home; obtaining the activity data of the elderly at home, and determining the way to intervene in the cognition of the elderly based on the activity data of the elderly at home; and intervening in the cognitive problems of the elderly according to the cognitive problems of the elderly.

[0010] Specifically, determining the cognitive problems of the elderly based on their home dietary data also includes the following steps:

[0011] According to the dietary data of the elderly at home, the intake rate of the elderly for the i-th food f is extracted i , f i =n1 / n2; where n1 represents the number of times the elderly consume the i-th food, and n2 represents the number of times the i-th food appears in the family diet; obtain the elderly's intake rate of all foods, and determine the elderly's intake rate of the i-th food f i Is there an abnormally low situation? If so, it is judged that the elderly have aversion to the i-th food. If not, it means that the elderly do not have aversion to the i-th food.

[0012] Identify all foods that the elderly have resistance to, and let f j Indicates the intake rate of the jth food that the elderly resist; let Aj represent the jth food that the elderly resist. According to the main component information contained in Aj, obtain the intake rate of other foods with the same main components as Aj, and judge f j Is there an abnormally low situation? If f j If there is no abnormally low situation, it is determined that the elderly have resistance to the main components contained in Aj; if f j If there is an abnormally low level, it is determined that the elderly do not have any resistance to the main ingredients contained in Aj;

[0013] Obtain information on chronic diseases suffered by the elderly, determine dietary taboos for chronic diseases, obtain information on components of elderly people's resistance behaviors beyond dietary taboos, and determine cognitive problems of the elderly.

[0014] Specifically, intervention for the cognitive problems of the elderly according to their cognitive problems also includes the following steps:

[0015] S10, obtaining the cognitive problems of the elderly and determining the component-related information that needs to be presented to the elderly; encoding the component-related information that needs to be presented to the elderly in binary form to obtain a binary string, where each bit in the binary string corresponds to a data, and each binary string corresponds to an individual; if the IoT device presents the component-related information to the elderly, the value of the binary string bit corresponding to the component-related information that needs to be presented to the elderly is 1, otherwise the value of the corresponding binary string bit is 0;

[0016] S11, create the initial population and set the number of iterations to zero;

[0017] S12, calculating the fitness value corresponding to the individual binary string;

[0018] S13, select individuals from the population based on their fitness values;

[0019] S14, randomly select some individuals from the selected individuals to perform crossover operations, exchange parts of the chromosomes to create new individuals, and determine whether the new individuals meet the constraints. If so, the new individuals are retained; if not, the new individuals are discarded;

[0020] S15, performing mutation operation on individuals in the population to determine whether the mutated individuals meet the constraint conditions. If so, the mutated individuals are retained; if not, the mutated individuals are discarded.

[0021] S16, add 1 to the number of iterations to determine whether the number of iterations has reached the termination condition. If so, stop the algorithm and obtain the individual binary string with the highest fitness value; if not, return to step S12;

[0022] S17, based on the individual binary string with the highest fitness value, the elderly’s cognition is intervened.

[0023] Specifically, calculating the fitness value corresponding to the binary string also includes the following steps:

[0024] Get home diet information in the future time, and get future food information based on the home diet information in the future time; get individual binary string information, and get the fitness value component fit based on the individual binary string information, ; where sign m Represents the sign coefficient. If there is no main component of food in the future food information that is the mth component presented to the elderly by the IoT device, then sign m is 0. If the main ingredient of the food in the future food information is the mth ingredient presented to the elderly by the IoT device, then sign m is 1; It represents the subjective degree of the elderly towards the food whose main ingredient is the mth ingredient presented to the elderly by the IoT device after the IoT device presents information related to the mth ingredient to the elderly.

[0025] Specifically, the subjective degree of the elderly to the food whose main ingredient is the mth ingredient presented to the elderly by the IoT device is determined by the following steps:

[0026] Get the time t at which the IoT device presents information about the mth ingredient to the elderly in the future. Let B represent the random event that the elderly consume food whose main ingredient is the mth ingredient presented to the elderly by the IoT device. Then P m =P{B|T=t0,ΔT=t}=num1 / num2; where t0 represents the time when the elderly has received the information related to the mth component; T represents the time when the current IoT device presents the information related to the mth component to the elderly; ΔT represents the change value of T after the IoT device presents the information related to the mth component to the elderly in the future; num2 represents the number of times the IoT device presents information to the elderly for a time not less than L×T+ΔT; num1 represents the number of times the elderly have feedback on the information in the data in which the IoT device presents the information for a time not less than L×T+ΔT; L represents the forgetting coefficient.

[0027] The beneficial effects of the present invention are as follows: Through the intervention of Internet of Things devices, the elderly can obtain relevant information about the nutritional content, health benefits and impact of food on their own conditions, use information to break down misconceptions, and help the elderly take in enough nutrients such as protein, carbohydrates, fat, vitamins and minerals, thereby reducing the possibility of malnutrition problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 A schematic diagram of the structure of an IoT-based home monitoring system for chronic diseases in the elderly provided by one embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of the embodiments.

[0030] Example 1, with reference to Figure 1, is an embodiment of the present invention, which provides a home monitoring system for chronic diseases in the elderly based on the Internet of Things, including: a device management module, a data analysis module and a data storage module; the device management module is interconnected with the data storage module, and is used to obtain activity data of the elderly and intervene in the cognitive problems of the elderly; the output end of the data storage module is interconnected with the input end of the data analysis module, and is used to store the activity data of the elderly and information data obtained by the elderly from Internet of Things devices and real life; the data analysis module is interconnected with the device management module, and is used to determine the cognitive problems of the elderly and the information presented to the elderly by the Internet of Things devices.

[0031] The device management module also includes an input unit, a data acquisition unit, an intervention unit, and a network analysis unit; the input unit is used to input home dietary data and chronic disease data of the elderly; the data acquisition unit is used to obtain information data and activity data obtained by the elderly from real life; the intervention unit intervenes in the cognitive problems of the elderly; the network analysis unit is used to determine the dietary taboo information of the elderly based on the input chronic disease data, and at the same time extract the information obtained by the elderly from the Internet of Things devices to determine the content of the information obtained by the elderly. The data analysis module also includes a cognitive analysis unit, an intervention analysis unit, and an optimization unit; the cognitive analysis unit obtains chronic disease information suffered by the elderly, determines dietary taboo information for chronic diseases, and determines the cognitive problems of the elderly for the components of the elderly's resistance behavior other than dietary taboo information; the intervention analysis unit is used to determine the impact of the information presented by the Internet of Things devices to the elderly on the elderly; the optimization unit determines the information presented by the Internet of Things devices to the elderly through genetic algorithms. The cognitive analysis unit extracts the elderly's intake rate f of the i-th food based on the elderly's home dietary data. i , f i =n1 / n2; where n1 represents the number of times the elderly consume the i-th food, and n2 represents the number of times the i-th food appears in the family diet; obtain the elderly's intake rate of all foods, and determine the elderly's intake rate of the i-th food f i Is there an abnormally low situation? If so, it is judged that the elderly have aversion to the i-th food. If not, the elderly do not have aversion to the i-th food. Determine all foods that the elderly have aversion to, and let f j Indicates the intake rate of the jth food that the elderly resist; let Aj represent the jth food that the elderly resist. According to the main component information contained in Aj, obtain the intake rate of other foods with the same main components as Aj, and judge f j Is there an abnormally low situation? If f j If there is no abnormally low situation, it is determined that the elderly have resistance to the main components contained in Aj; if fj If there is an abnormally low level, it is determined that the elderly have no resistance to the main components contained in Aj; obtain information on chronic diseases suffered by the elderly, determine the dietary taboos for chronic diseases, and for information on components that the elderly have resistance to other than the dietary taboos, determine the cognitive problems of the elderly.

[0032] Example 2 is another embodiment of the present invention. This embodiment is different from the first embodiment in that it provides a home monitoring method for chronic diseases in the elderly based on the Internet of Things. In order to verify and illustrate the technical effects used in this method, this embodiment uses traditional technical solutions and the method of the present invention for comparative testing, and compares the test results by means of scientific demonstration to verify the actual effect of this method.

[0033] Obtain the elderly’s dietary data at home through IoT devices, and determine the elderly’s cognitive problems based on the elderly’s dietary data at home; obtain the elderly’s activity data at home, and determine the way to intervene in the elderly’s cognition based on the elderly’s activity data at home; intervene in the elderly’s cognitive problems based on the elderly’s cognitive problems.

[0034] Determining cognitive problems in older adults based on their home dietary data also involves the following steps:

[0035] According to the dietary data of the elderly at home, the intake rate of the elderly for the i-th food f is extracted i , f i =n1 / n2; where n1 represents the number of times the elderly consume the i-th food, and n2 represents the number of times the i-th food appears in the family diet; obtain the elderly's intake rate of all foods, and determine the elderly's intake rate of the i-th food f i Is there an abnormally low situation? If so, it is judged that the elderly have aversion to the i-th food. If not, it means that the elderly do not have aversion to the i-th food.

[0036] Identify all foods that the elderly have resistance to, and let f j Indicates the intake rate of the jth food that the elderly resist; let Aj represent the jth food that the elderly resist. According to the main component information contained in Aj, obtain the intake rate of other foods with the same main components as Aj, and judge f j Is there an abnormally low situation? If f j If there is no abnormally low situation, it is determined that the elderly have resistance to the main components contained in Aj; if f j If there is an abnormally low level, it is determined that the elderly do not have any resistance to the main ingredients contained in Aj;

[0037] Obtain information on chronic diseases suffered by the elderly, determine dietary taboos for chronic diseases, obtain information on components of elderly people's resistance behaviors beyond dietary taboos, and determine cognitive problems of the elderly.

[0038] Due to chronic diseases, the elderly may be affected by some misconceptions, develop unnecessary fear or prejudice towards food, and lead to unbalanced nutritional intake. For example, elderly people with diabetes rarely eat staple foods because they are worried that carbohydrates will cause blood sugar to rise. However, carbohydrates are the main source of energy for the human body. Long-term deficiency may lead to insufficient energy in the body, affect the normal functioning of organs, and reduce the elderly's immunity. In addition, the elderly may also have misunderstandings about the ingredients in food. For example, some elderly people believe that fat is bad and will induce cardiovascular disease, and thus completely refuse to eat meat, egg yolks and other fat-containing foods. First of all, a moderate amount of healthy fats, such as the unsaturated fatty acids in olive oil, play a certain role in maintaining normal body functions and protecting the cardiovascular and cerebrovascular systems. Secondly, for common soy products such as tofu and soy milk, their fat content is actually not high, but some elderly people may worry about their fat content due to misunderstandings about soy products and refuse to eat them. In fact, the elderly can consume a moderate amount of tofu or soy milk to maintain their health.

[0039] Here, we first determine whether there is a resistance to food based on the elderly's food intake rate. The intake rate can be calculated based on home dietary data over a fixed time period, such as obtaining the elderly's food intake rate based on home dietary data within a month. At the same time, due to the elderly's misunderstanding of ingredient information, there may be erroneous rejection behavior. For example, the elderly may mistakenly believe that tofu or soy milk are high in fat and therefore refuse to eat them. However, the main components of tofu and soy milk are protein and carbohydrates, and the fat content is relatively low. Directly based on the elderly's resistance to tofu and soy milk, the erroneous conclusion that the elderly have a resistance to protein or carbohydrates will be drawn.

[0040] The analysis was conducted using the intake rates of other foods with similar main ingredients to tofu or soy milk. Elderly people's intake rates of other foods with similar protein and carbohydrate content were significantly higher than those of tofu or soy milk, indicating that the elderly do not have any aversion to protein and carbohydrates, nor do they have cognitive problems with protein and carbohydrates. It is simply a misconception of the ingredients of tofu or soy milk that leads to aversion to tofu or soy milk.

[0041] As for the fat component, since it is not the main component of tofu or soy milk, the intake rate of tofu or soy milk by the elderly is not analyzed; because if the elderly have resistance to fat components, then this can be reflected in the intake rate of other foods with fat as the main content, so there is no need to judge the fat in tofu or soy milk to avoid duplication.

[0042] To determine whether the intake rate is abnormally low, the box plot method can be used to obtain the lower limit of the intake rate data. If the intake rate is lower than the lower limit, it is judged to be abnormal.

[0043] Determining how to intervene in the elderly's cognition based on their home activity data also includes the following steps:

[0044] The IoT device obtains the sound information received by the elderly and determines the source of the sound based on the sound information; analyzes the impact of sounds from different sources on the elderly, and lets infk represent the impact of the k-th source of sound on the elderly, infk=lt k / t k , where t k represents the time when the elderly receive the sound from the kth source, lt k It indicates the number of times the elderly utter the same words as the sounds received from the k-th source; the sound source that has the greatest impact on the elderly is used as a way to intervene in the elderly's cognition.

[0045] IoT devices can use voice assistants to identify the sounds around the elderly and determine the source of the sounds. The sources of sounds include, but are not limited to, family members, other elderly people online, neighbors, and online experts. IoT devices can directly identify the source of online sounds using online tools, without the need for voice assistants. For sounds received by the elderly in real life, the frequency of voiceprint features can be identified by the voice assistant to determine whether the sound is from a family member or neighbor. The device can also obtain the time when the elderly received the sound from the kth source, such as the time when the elderly received the sound from other elderly people online, which is the time when the elderly watched other elderly people's videos or live broadcasts. The device can also use the voice assistant to identify the elderly's voiceprint information and determine whether the elderly uttered the same words as the sound information in the video or live broadcast, thereby determining the impact of other elderly people online on the elderly's cognition. Voice assistants can implement relevant voice recognition modules on mobile phones, smart bracelets, and other devices that the elderly can carry with them.

[0046] Intervention for cognitive problems in the elderly also includes the following steps:

[0047] S10, obtaining the cognitive problems of the elderly and determining the component-related information that needs to be presented to the elderly; encoding the component-related information that needs to be presented to the elderly in binary form to obtain a binary string, where each bit in the binary string corresponds to a data, and each binary string corresponds to an individual; if the IoT device presents the component-related information to the elderly, the value of the binary string bit corresponding to the component-related information that needs to be presented to the elderly is 1, otherwise the value of the corresponding binary string bit is 0;

[0048] S11, create the initial population and set the number of iterations to zero;

[0049] S12, calculating the fitness value corresponding to the individual binary string;

[0050] S13, select individuals from the population based on their fitness values;

[0051] S14, randomly select some individuals from the selected individuals to perform crossover operations, exchange parts of the chromosomes to create new individuals, and determine whether the new individuals meet the constraints. If so, the new individuals are retained; if not, the new individuals are discarded;

[0052] S15, performing mutation operation on individuals in the population to determine whether the mutated individuals meet the constraint conditions. If so, the mutated individuals are retained; if not, the mutated individuals are discarded.

[0053] S16, add 1 to the number of iterations to determine whether the number of iterations has reached the termination condition. If so, stop the algorithm and obtain the individual binary string with the highest fitness value; if not, return to step S12;

[0054] S17, based on the individual binary string with the highest fitness value, the elderly’s cognition is intervened.

[0055] After determining the cognitive intervention method that the elderly can accept, cognitive intervention that can solve the cognitive problem is obtained according to the determined cognitive problems of the elderly. For example, the elderly have a misunderstanding about fruits and believe that diabetic patients cannot eat any fruit. If the cognitive intervention method that the elderly can accept is intervention by other elderly people, then other elderly people share their experiences, videos, pictures and other information about eating low-sugar fruits as the ingredient-related information that needs to be presented to the elderly. After determining all the ingredient-related information that needs to be presented to the elderly, since the elderly have limited time to use the Internet of Things, such as only using Internet of Things devices to view information at noon or in the evening, the ingredient-related information that needs to be presented to the elderly is selected through genetic algorithms to determine the ingredient-related information presented to the elderly by the Internet of Things devices. The constraint condition is that the total viewing time of the ingredient-related information presented to the elderly by the Internet of Things devices does not exceed the time the elderly use the Internet of Things devices.

[0056] Calculating the fitness value corresponding to the binary string also includes the following steps:

[0057] Get home diet information in the future time, and get future food information based on the home diet information in the future time; get individual binary string information, and get the fitness value component fit based on the individual binary string information, ; where sign m Represents the sign coefficient. If there is no main component of food in the future food information that is the mth component presented to the elderly by the IoT device, then sign m is 0. If the main ingredient of the food in the future food information is the mth ingredient presented to the elderly by the IoT device, then sign m is 1; It represents the subjective degree of the elderly towards the food whose main ingredient is the mth ingredient presented to the elderly by the IoT device after the IoT device presents information related to the mth ingredient to the elderly.

[0058] The elderly person's family can upload home dietary information for the future. The future time can be the next day or the time of the next family meal. It only needs to be determined that there is a time between the future time and the current time when the elderly person can use the IoT device so that the elderly person can obtain information from the IoT device.

[0059] Based on the home diet information in the future, it is possible to predict whether the elderly's eating behavior will change after obtaining information from the IoT device, such as changing from not eating fruit to being willing to eat fruit, and this is measured through subjectivity; at the same time, the fruit-related information obtained by the elderly from the IoT device will only be effective if the home diet in the future includes fruit, so a symbolic coefficient is set for measurement; the fitness value of the binary string is obtained based on the elderly's home diet in the future.

[0060] The subjective degree of the elderly to the food whose main ingredient is the mth ingredient presented to the elderly by the IoT device is determined by the following steps:

[0061] Get the time t at which the IoT device presents information about the mth ingredient to the elderly in the future. Let B represent the random event that the elderly consume food whose main ingredient is the mth ingredient presented to the elderly by the IoT device. Then P m=P{B|T=t0,ΔT=t}=num1 / num2; where t0 represents the time when the elderly has received the information related to the mth component; T represents the time when the current IoT device presents the information related to the mth component to the elderly; ΔT represents the change value of T after the IoT device presents the information related to the mth component to the elderly in the future; num2 represents the number of times the IoT device presents information to the elderly for a time not less than L×T+ΔT; num1 represents the number of times the elderly have feedback on the information in the data in which the IoT device presents the information for a time not less than L×T+ΔT; L represents the forgetting coefficient.

[0062] Due to the poor memory of the elderly, information may need to be presented to them multiple times before they can remember it, which in turn affects their perception of food. T is updated in the following manner: the time t at which the IoT device presents information related to the mth ingredient to the elderly in the future has no forgetting coefficient, while the time at which the IoT device presents information related to the mth ingredient to the elderly in the past is attenuated by the forgetting coefficient, that is, T'=L×T+ΔT, where T' is the updated T. The elderly's influence on the IoT device is related to the time when the information is received. The known conditions are the time at which the IoT device presents the mth ingredient to the elderly in the future and the time at which the IoT device has already presented the mth ingredient to the elderly. T is updated based on these two times. After updating T, there is no substantial difference between food-related information and other information. Therefore, the time at which all IoT devices present information to the elderly is used to analyze the impact on the elderly. In the historical data, the information presented to the elderly by the IoT device is not limited to food but can also be people's names. The IoT device extracts keywords from the presented information and then uses a voice assistant to determine whether the elderly respond to the keywords, such as by saying the keywords during communication. The IoT device can be a mobile device such as a mobile phone, which keeps close contact with the elderly to obtain voice information.

[0063] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code. The storage medium may be implemented by any type of volatile or non-volatile storage device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0064] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for monitoring chronic diseases in the elderly at home based on the Internet of Things, characterized by: The following steps are involved: Obtain the elderly’s dietary data at home through IoT devices, and determine the elderly’s cognitive problems based on the elderly’s dietary data at home; obtain the elderly’s activity data at home, and determine the way to intervene in the elderly’s cognition based on the elderly’s activity data at home; intervene in the elderly’s cognitive problems based on the elderly’s cognitive problems.

2. The method for monitoring chronic diseases in the elderly at home based on the Internet of Things according to claim 1, characterized in that: The method of determining cognitive problems of the elderly based on their home dietary data further includes the following steps: According to the dietary data of the elderly at home, the intake rate of the elderly for the i-th food f is extracted i , f i =n1 / n2; where n1 represents the number of times the elderly consume the i-th food, and n2 represents the number of times the i-th food appears in the family diet; obtain the elderly's intake rate of all foods, and determine the elderly's intake rate of the i-th food f i Is there an abnormally low situation? If so, it is judged that the elderly have aversion to the i-th food. If not, it means that the elderly do not have aversion to the i-th food. Identify all foods that the elderly have resistance to, and let f j Indicates the intake rate of the jth food that the elderly resist; let Aj represent the jth food that the elderly resist. According to the main component information contained in Aj, obtain the intake rate of other foods with the same main components as Aj, and judge f j Is there an abnormally low situation? If f j If there is no abnormally low situation, it is determined that the elderly have resistance to the main components contained in Aj; if f j If there is an abnormally low level, it is determined that the elderly do not have any resistance to the main ingredients contained in Aj; Obtain information on chronic diseases suffered by the elderly, determine dietary taboos for chronic diseases, obtain information on components of elderly people's resistance behaviors beyond dietary taboos, and determine cognitive problems of the elderly.

3. The method for monitoring chronic diseases in the elderly at home based on the Internet of Things according to claim 2, characterized in that: The method of intervening in the cognitive problems of the elderly according to the cognitive problems of the elderly also includes the following steps: S10, obtaining the cognitive problems of the elderly and determining the component-related information that needs to be presented to the elderly; encoding the component-related information that needs to be presented to the elderly in binary form to obtain a binary string, where each bit in the binary string corresponds to a data, and each binary string corresponds to an individual; if the IoT device presents the component-related information to the elderly, the value of the binary string bit corresponding to the component-related information that needs to be presented to the elderly is 1, otherwise the value of the corresponding binary string bit is 0; S11, create the initial population and set the number of iterations to zero; S12, calculating the fitness value corresponding to the individual binary string; S13, select individuals from the population based on their fitness values; S14, randomly select some individuals from the selected individuals to perform crossover operations, exchange parts of the chromosomes to create new individuals, and determine whether the new individuals meet the constraints. If so, the new individuals are retained; if not, the new individuals are discarded; S15, performing mutation operation on individuals in the population to determine whether the mutated individuals meet the constraint conditions. If so, the mutated individuals are retained; if not, the mutated individuals are discarded. S16, add 1 to the number of iterations to determine whether the number of iterations has reached the termination condition. If so, stop the algorithm and obtain the individual binary string with the highest fitness value; if not, return to step S12; S17, based on the individual binary string with the highest fitness value, the elderly’s cognition is intervened.

4. The method for monitoring chronic diseases in the elderly at home based on the Internet of Things as claimed in claim 3, characterized in that: Calculating the fitness value corresponding to the binary string also includes the following steps: Get home diet information in the future time, and get future food information based on the home diet information in the future time; get individual binary string information, and get the fitness value component fit based on the individual binary string information, ; where sign m Represents the sign coefficient. If there is no main component of food in the future food information that is the mth component presented to the elderly by the IoT device, then sign m is 0. If the main ingredient of the food in the future food information is the mth ingredient presented to the elderly by the IoT device, then sign m is 1; It represents the subjective degree of the elderly towards the food whose main ingredient is the mth ingredient presented to the elderly by the IoT device after the IoT device presents information related to the mth ingredient to the elderly.

5. The method for home monitoring of chronic diseases in the elderly based on the Internet of Things according to claim 4, characterized in that: The subjective degree of the elderly to the food whose main ingredient is the mth ingredient presented to the elderly by the IoT device is determined by the following steps: Get the time t at which the IoT device presents the information about the mth ingredient to the elderly in the future. Let B represent the random event that the elderly consume food whose main ingredient is the mth ingredient presented to the elderly by the IoT device. Then P m =P{B|T=t0, ΔT=t}=num1 / num2; where t0 represents the time when the elderly have received the information related to the mth component; T represents the time when the current IoT device presents the information related to the mth component to the elderly, ΔT represents the change value of T after the IoT device presents the information related to the mth component to the elderly in the future; num2 represents the number of times the IoT device presents information to the elderly for a time not less than L×T+ΔT, num1 represents the number of times the elderly provide feedback on the information in the data where the IoT device presents the information for a time not less than L×T+ΔT; L represents the forgetting coefficient.

6. An IoT-based home monitoring system for chronic diseases in the elderly, using the IoT-based home monitoring method for chronic diseases in the elderly as described in any one of claims 1 to 5, characterized in that: It includes a device management module, a data analysis module and a data storage module; the device management module and the data storage module are interconnected to obtain activity data of the elderly and intervene in the cognitive problems of the elderly; The output end of the data storage module is interconnected with the input end of the data analysis module, and is used to store the activity data of the elderly and the information data obtained by the elderly from the Internet of Things devices and real life; the data analysis module is interconnected with the device management module, and is used to determine the cognitive problems of the elderly and the information presented to the elderly by the Internet of Things devices.

7. The home monitoring system for chronic diseases in the elderly based on the Internet of Things as claimed in claim 6, characterized in that: The device management module also includes an input unit, a data acquisition unit, an intervention unit and a network analysis unit; the input unit is used to input home diet data and chronic disease data of the elderly; the data acquisition unit is used to obtain information data and activity data obtained by the elderly from real life; the intervention unit intervenes in the cognitive problems of the elderly; the network analysis unit is used to determine the dietary taboo information of the elderly based on the input chronic disease data, and at the same time extract the information obtained by the elderly from the Internet of Things devices to determine the information content obtained by the elderly.

8. The home monitoring system for chronic diseases in the elderly based on the Internet of Things as claimed in claim 7, characterized in that: The data analysis module also includes a cognitive analysis unit, an intervention analysis unit, and an optimization unit. The cognitive analysis unit obtains information about chronic diseases suffered by the elderly, determines dietary taboos related to the chronic diseases, and determines cognitive problems of the elderly based on information about components of the elderly's resistant behaviors other than dietary taboos. The intervention analysis unit is used to determine the impact of information presented to the elderly by the IoT device on the elderly. The optimization unit determines the information presented to the elderly by the IoT device through a genetic algorithm.

9. The home monitoring system for chronic diseases in the elderly based on the Internet of Things as claimed in claim 8, characterized in that: The cognitive analysis unit extracts the elderly's intake rate of food i based on the elderly's home diet data. i , f i =n1 / n2; where n1 represents the number of times the elderly consume the i-th food, and n2 represents the number of times the i-th food appears in the family diet; Obtain the intake rate of all foods by the elderly and determine the intake rate f of the i-th food by the elderly i Is there an abnormally low situation? If so, it is judged that the elderly have aversion to the i-th food. If not, it means that the elderly do not have aversion to the i-th food. Identify all foods that the elderly have resistance to, and let f j Indicates the intake rate of the jth food that the elderly resist; let Aj represent the jth food that the elderly resist. According to the main component information contained in Aj, obtain the intake rate of other foods with the same main components as Aj, and judge f j Is there an abnormally low situation? If f j If there is no abnormally low situation, it is determined that the elderly have resistance to the main components contained in Aj; if f j If there is an abnormally low level, it is determined that the elderly have no resistance to the main components contained in Aj; obtain information on chronic diseases suffered by the elderly, determine the dietary taboos for chronic diseases, and for information on components that the elderly have resistance to other than the dietary taboos, determine the cognitive problems of the elderly.

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