Old people thermal health prediction system and prediction method

Abstract

The invention discloses an old people thermal health prediction system and prediction method, and the system comprises a parameter input module which is used for inputting personal parameters and environmental parameters, and determining a thermal environment where an individual is located; the thermal health state judgment module is used for analyzing the input parameters, predicting physiological parameters, thermal sensation and thermal comfort through a thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model, and judging the thermal health state of the individual according to the numerical value of the physiological parameters, the thermal sensation and the thermal comfort; and the thermal health state output module is used for outputting the thermal health state of the individual. By means of the system, prediction of personal physiological parameters, thermal sensation and thermal comfort in different thermal environments can be achieved, and then the thermal health states of the human body in the different thermal environments can be obtained. And then visual thermal health state prompts are provided for the old people through a visual module, so that the old people can conveniently adjust the thermal health state.

Description

Technical Field

[0001] The present invention relates to the technical field of human health management, and in particular to a thermal health prediction system and method for the elderly. Background Art

[0002] Thermal health refers to the state in which the human body is physiologically and psychologically comfortable and satisfied under the premise of satisfying the physiological health of the human body in the thermal environment; thermal sub-health refers to the state in which the thermal environment is physiologically uncomfortable or subjectively uncomfortable without causing substantial physiological diseases; thermal unhealth refers to the state in which the thermal environment can no longer meet the basic requirements of physiological health and thermal comfort, and pathological signs appear physiologically and psychologically. With the aging of the population, the thermal health problems of the elderly in the thermal environment are becoming increasingly prominent. In the increasingly hot summer environment, air conditioning plays an important role in the thermal health of residents. Through the study of the use of air conditioning by the elderly, it is found that high-income elderly people tend to actively use air conditioning equipment to improve the thermal environment, while low-income elderly people often do not pay attention to the comfort of the thermal environment. The proportion of low-income elderly people is relatively high, and some elderly people are still unwilling to turn on the air conditioner to save electricity. In the existing standards, the indoor design temperature for the elderly to stay for a long time is 26.0~28.0℃. Even if the elderly have used air conditioning, they may still set a higher air conditioning temperature to reduce the operating cost of the air conditioning, or set a lower air conditioning temperature because of greed for coolness. In this case, not only can the air conditioning's effect of improving the thermal environment not be achieved, but the elderly, due to their low immunity, are also more likely to catch colds, muscle pain, cardiovascular problems, sleep problems, etc. due to being in a cold or hot environment for a long time.

[0003] Moreover, as they age, the perception and adjustment abilities of the elderly decline, making them more susceptible to changes in the indoor environment. The elderly may not perceive cold or heat as quickly as young people, and their physical reactions may be delayed. Therefore, it is necessary to propose a thermal environment and thermal health prediction method to clarify the real-time thermal environment health level of the elderly, form a reminder, and guide the elderly to turn on and off the air conditioner and adjust the air conditioner set temperature.

[0004] In addition, existing research also focuses on the thermal comfort of the elderly in the indoor environment of buildings. In order to obtain the thermal comfort of the elderly for the environment, the method of combining on-site environmental data testing with subjective voting by the subjects is usually used for evaluation. This traditional method is time-consuming and laborious, and does not take into account the low sensitivity of the elderly to changes in the thermal environment. The subjective description may not necessarily reflect the real thermal feelings of the elderly. At present, there are some studies that combine human thermal perception and physiological parameters to more accurately evaluate different thermal environments and the human comfort of the elderly in these thermal environments, but the amount of data is small and the application is also very limited. The impact of environmental changes on the human body lies not only in its psychological feelings, but more importantly, in the physiological reactions produced by people in the changes. These physiological reactions reflect that the thermal health of the human body is affected to a certain extent. And with the development of the economy and society, people pay more attention to health issues. If a certain method can predict the thermal state in an adverse thermal environment and produce a summary description of the thermal health state in the thermal environment, it will certainly enhance the ability of the elderly to adjust to the adverse thermal environment. However, there are few studies on thermal health evaluation at present, which only involve exploring the temperature threshold and physiological parameter range of different thermal health states, and there is no systematic partitioning and definition of the thermal environment and the thermal health state formed by it. In addition, the existing thermal health evaluation has not focused on the complexity and particularity of the elderly population. After the thermal health prediction is carried out, there is a lack of a method to intuitively present the prediction results. This makes the thermal environment and thermal health system prediction lack the conditions for widespread use and dissemination. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the purpose of the present invention is to provide a thermal health prediction system for the elderly, so as to solve the problems that the existing thermal health evaluation system does not take into account the particularities of the elderly's body, cannot accurately predict the thermal health status of the elderly, and cannot intuitively present the thermal health status of the elderly.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is as follows:

[0007] A thermal health prediction system for the elderly, comprising:

[0008] Parameter input module: used to input personal parameters and environmental parameters to determine the thermal environment of the individual;

[0009] Thermal health status judgment module: Analyze the input parameters and predict the individual's core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal comfort and thermal sensation through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. According to the values ​​of core temperature, skin temperature, heart rate, blood pressure and average body temperature, as well as thermal comfort and thermal sensation, the thermal health status of the individual is judged;

[0010] Thermal health status output module: outputs the thermal health status of the individual.

[0011] Furthermore, in the parameter input module, personal parameters include gender, age, height, weight, activity status, clothing thermal resistance, and environmental parameters include air temperature, relative humidity, and air speed; the activity status includes: lying still, sitting still, standing, and showering.

[0012] Furthermore, the thermal health status output module includes a visualization image production module, which uses small figures of different colors and corresponding English letter grades to display the thermal health status of an individual.

[0013] Furthermore, the thermal health status output module also includes an APP remote signal output module, which converts the visual image into a remote signal and sends it to the mobile phone app to facilitate remote monitoring of the individual's thermal health status.

[0014] Furthermore, the visualization image production module uses figures of different colors and corresponding English letter grades to display the thermal health status of an individual, wherein the thermal health status includes neutral / basic thermal health, thermal health that can be compensated by vasodilation, thermal health that can be compensated by vasoconstriction, hot sub-health, cold sub-health, hot unhealthy and cold unhealthy;

[0015] Specifically include:

[0016] Green represents neutral / basic thermal health, represented by the letter A;

[0017] Light green indicates thermal health that can be compensated by vasodilation, represented by the letter Bh;

[0018] Dark green indicates thermal health that can be compensated by vasoconstriction, represented by the letters Bc;

[0019] Yellow indicates sub-health due to heat, represented by the letter Ch;

[0020] Blue indicates cold sub-health, represented by the letter Cc;

[0021] Red indicates unhealthy heat, represented by the letters Dh;

[0022] Dark blue indicates cold and unhealthy, represented by the letters Dc.

[0023] Furthermore, when the body, heart, and brain of the little man appear in different colors respectively, the priority of judging the thermal health status is uncomfortable thermal health status > comfortable and healthy thermal health status. Under this premise, the priority of evaluation is heart > body > brain.

[0024] Furthermore, the calculation formula for predicting an individual's core temperature, skin temperature, heart rate, blood pressure, and average body temperature through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model is:

[0025] Core Temperature:

[0026]

[0027] In formula (1), α is the mass coefficient of the skin layer, θ is the time [s], and A D is the body area [m 2 ], m is the total body mass, C p,b is the specific heat of human body [J / kg℃], T cr is the core temperature [℃], M is the metabolic rate [W / m 2 ], W is the external work [W / m 2 ], Q cr–sk is the heat exchange heat between the core layer and the skin layer [W / m 2 ], Q res is the heat dissipation from breathing [W / m 2 ];

[0028] Skin temperature:

[0029]

[0030] In formula (2), T sk is the skin temperature [℃], i is the body node, Q conv , Q rad and Q evap They are heat conduction, radiation and evaporation between the skin layer and the environment [W / m 2 ];

[0031] Heart rate:

[0032]

[0033] In formula (3), HR is heart rate [bpm], M0 is basal metabolic rate [W / m 2 ], b is the coefficient related to gender, age, and weight, β is the coefficient of heart rate response to core temperature changes [bpm / ℃], is the average ambient dry-bulb temperature around humans [℃],

[0034] P a is the water vapor partial pressure [kPa], K is the thermal conductivity between the core layer and the skin layer [W / m 2 K],C p,bl is the specific heat of blood [J / kg℃], is the skin blood flow [kg / m 2 s];

[0035] Systolic blood pressure:

[0036]

[0037] In formula (4), SBP is systolic blood pressure [mmHg], CO dil is the diastolic cardiac output [cm 3 / h], CO con is the cardiac output [cm 3 / h], CO0 is based on cardiac output [cm 3 / h],R heart is the resistance of the cardiac circulatory system [mmHg·s / L], R pulmonary is the resistance of the pulmonary circulation system [mmHg·s / L], R head , R upper , R trunk , R lower is the capillary resistance of the head, trunk, upper limbs, and lower limbs [mmHg·s / L], respectively, and PP is the pulse pressure [mmHg);

[0038] Diastolic blood pressure:

[0039]

[0040] In formula (5), DBP is diastolic blood pressure [mmHg];

[0041] Average body temperature:

[0042] T b (θ)=α(θ)T sk (θ)+[1-α(θ)]T cr (θ) (6)

[0043] In formula (6), T b The average body temperature.

[0044] Further, the method for determining the thermal health status of an individual is:

[0045] A: When the individual's heart rate and blood pressure are at a basic neutral level, the core temperature T cr , Skin temperature T sk , average body temperature T b When both are neutral values ​​and the individual's thermal sensation and thermal comfort scores are 0, the individual is in a neutral / basic thermal health state;

[0046] B: When 30%〖HR〗_max<individual heart rate<50%〖HR〗_max, 100 / 60mmHg≤blood pressure<160 / 85mmHg, the individual is in a heat health state with physiological compensation;

[0047] And the neutral value <T cr <37.0℃, neutral value <T sk <38.0℃, t b,c ≤Tb <t b,h , 0< thermal sensation and thermal comfort score ≤ 1, the individual is in a thermal health state with vasodilation compensation;

[0048] Or when 35.0℃ <T cr <Neutral value, 10.2℃ <T sk <Neutral value, 34.3℃≤T b <t b,c , thermal sensation score ≥ -1, thermal comfort score ≤ 1, the individual is in a thermal health state with vasoconstriction compensation;

[0049] C: When 50%〖HR〗_max≤individual heart rate<70%〖HR〗_max, 160 / 85mmHg≤blood pressure<180 / 100mmHg, the individual is in a sub-healthy state;

[0050] And when 37.0≤T cr <37.3℃, neutral value <T sk <38.0℃, t b,h ≤T b <37.3℃, 1<thermal sensation and thermal comfort score ≤2, the individual is in a sub-healthy state of heat;

[0051] Or when 35.0℃ <T cr <Neutral value, 10.2℃ <T sk <Neutral value, 32.1℃<T b <34.3℃, thermal sensation score ≥-2, thermal comfort score ≤2, the individual is in a cold subhealthy state;

[0052] Dh: When the individual heart rate ≥70%〖HR〗_max, blood pressure ≥180 / 100mmHg, T cr ≥37.3℃, T sk ≥38.0℃,T b ≥37.3℃, thermal sensation and thermal comfort score = 3, the individual is in a thermally unhealthy state;

[0053] Dc: When the individual heart rate ≤ 30% 〖HR〗_max, blood pressure ≥ 180 / 100mmHg, T cr ≤35.0℃,T sk ≤10.2℃,T b ≤32.1℃, thermal sensation score = -3, thermal comfort score = 3, the individual is in an unhealthy cold state.

[0054] A method for predicting thermal health of the elderly is implemented using the above prediction system, comprising the following steps:

[0055] (1) Input gender, age, height, weight, activity status, clothing thermal resistance, air temperature, relative humidity, and air speed into the parameter input module;

[0056] (2) The thermal health status judgment module is used to analyze the input parameters, and the core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal sensation and thermal comfort status of the individual are predicted through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. The thermal health status of the individual is judged according to the numerical values ​​of core temperature, skin temperature, heart rate, blood pressure and average body temperature, as well as thermal sensation and thermal comfort status;

[0057] (3) The thermal health status output module is used to output the thermal health status of the individual, and the thermal health status of the individual is displayed using figures of different colors and corresponding English letter grades.

[0058] Compared with the prior art, the present invention has the following beneficial effects:

[0059] 1. The present invention can predict the core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal sensation and thermal comfort of an individual under different thermal environments by constructing a thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model, and substitute the predicted data into the comprehensive evaluation model of thermal health and thermal environment, so as to obtain the thermal health status of the human body under different thermal environments. Then, the thermal health status of the individual is intuitively displayed by the visualization module with small figures of different colors and corresponding English letter grades, so as to realize the visualization of the thermal health status and provide intuitive thermal health status prompts for the elderly. Non-professionals can also quickly understand the individual's thermal environment adaptation. This color and image visualization can be used as an education and training tool to enhance the understanding of thermal health status, help the elderly and caregivers understand the importance of thermal health, and how to take appropriate preventive measures to ensure the thermal health of the human body. In particular, it is applied to some popular science activities for thermal health care of the elderly, so that the audience can easily understand the degree of influence of cold or hot environment on the physiological thermal health of the human body, so as to enhance the motivation for air conditioning regulation and improvement of indoor thermal environment, and provide guidance for the use of air conditioning to increase or decrease the set temperature of air conditioning.

[0060] 2. The method of the present invention can also be used for personalized thermal health monitoring to provide customized care and intervention measures for the elderly. In emergency situations, such as heat waves or extremely cold weather, color coding can help quickly identify elderly people who may be at risk of heat stress or cold stress. For the elderly, especially those with limited mobility, since the visual signals formed by the system can be uploaded to the mobile phone APP, their family members can understand the thermal health environment of the elderly at home and their transient thermal health status anytime and anywhere, so as to play a role in remote monitoring. If the current thermal environment is not a healthy thermal environment, the family members can remind the elderly to turn on and off the air conditioner or adjust the air conditioner temperature, or they can directly control the air conditioner by operating the buttons on the APP to help family members care for the health of the elderly and reduce the risk of accidental touch and miscontrol by the elderly. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] Figure 1 It is a structural schematic diagram of the thermal health prediction system for the elderly of the present invention;

[0062] Figure 2 A schematic diagram of the categories of parameters in the parameter input module of the elderly thermal health prediction system of the present invention;

[0063] Figure 3 This is a schematic diagram of the structure of the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model of the present invention;

[0064] Figure 4 This is a logic judgment diagram for predicting the thermal health status of the elderly according to the present invention;

[0065] Figure 5 It is a visualized schematic diagram of the thermal health status of the elderly according to the present invention;

[0066] Figure 6 is a classification diagram of thermal health status of the present invention;

[0067] Figure 7 The layout diagram of the nursing home room in the embodiment;

[0068] Figure 8 It is a test flow chart of an embodiment;

[0069] Fig. 9 The data diagram of the characteristic data of the sudden change of the ambient temperature when waking up in the embodiment; wherein (a) is the original environment in winter; (b) is the original environment in summer; (c) is the computing environment in winter; (d) is the computing environment in summer;

[0070] Fig.10 The comparison diagram of the measured and predicted core temperature values ​​in the embodiment; (a) winter; (b) summer;

[0071] Fig.11The following is a comparison chart of the measured and predicted skin temperature values ​​in the embodiment; (a) naked in winter; (b) naked in summer; (c) clothed in winter; (d) clothed in summer; (e) whole body in winter; (f) whole body in summer;

[0072] Fig.12 The comparison diagram of the heart rate measurement value and the predicted value in the embodiment; wherein (a) is winter; (b) is summer;

[0073] Fig.13 The comparison diagram of blood pressure measurement value and predicted value in the embodiment; wherein, (a) winter; (b) summer;

[0074] Fig.14 This is a schematic diagram of the thermal health level results of the elderly getting up in winter;

[0075] Fig.15 This is a schematic diagram of the thermal health level results of the elderly when getting up in summer. DETAILED DESCRIPTION

[0076] The specific implementation modes of the present invention are further described in detail below in conjunction with specific examples.

[0077] The present invention invents a thermal health prediction system and prediction method in a thermal environment based on the physiological parameters and thermal perception of the elderly, taking into account the physiological particularities of the elderly. The prediction system summarizes and proposes to express the impact of the thermal environment on human health in terms of thermal health, and further provides a visual prediction method to present the results of the thermal environment and thermal health evaluation, that is, to use different graphics and colors to represent the thermal health status of the body. The method is simple and intuitive. After seeing the results of the thermal health evaluation, if the elderly find that their thermal health status is not good, they can actively switch or adjust the temperature of the air conditioner. In addition, the air conditioner can be remotely controlled through an app, and the elderly’s children can monitor the elderly’s thermal health status in real time. Especially for some elderly people with limited mobility, the elderly’s children can adjust the thermal environment for the elderly and take care of the elderly’s health.

[0078] 1. The present invention provides a thermal health prediction system for the elderly, such as Figure 1 As shown, including:

[0079] Parameter input module: used to input personal parameters and environmental parameters to determine the thermal environment of the individual;

[0080] Thermal health status judgment module: Analyze the input parameters and predict the individual's core temperature, skin temperature, heart rate, blood pressure, thermal comfort and thermal sensation through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. According to the values ​​of core temperature, skin temperature, heart rate and blood pressure, as well as thermal comfort and thermal sensation, the thermal health status of the individual is judged;

[0081] Thermal health status output module: outputs the thermal health status of the individual.

[0082] like Figure 2 As shown, in the parameter input module, personal parameters include gender, age, height, weight, activity status, clothing thermal resistance, and environmental parameters include air temperature, relative humidity, and air speed. The activity status includes: lying still, sitting still, standing, and showering.

[0083] The energy metabolism coefficients corresponding to lying still, sitting still, standing, and showering are 0.7 met, 1.0 met, 1.2 met, and 3.0 met, respectively.

[0084] The air temperature includes the dry bulb temperature and the mean radiant temperature.

[0085] The clothing thermal resistance is obtained by looking up the table in combination with the ASHRAE 55 standard after knowing the clothing type. Specifically, the clothing thermal resistance is equal to the sum of the clothing thermal resistances of the clothing worn by the individual. The clothing type is estimated based on the UCB PMV calculation tool.

[0086] The thermal health status output module includes a visual image production module, which uses small figures of different colors and corresponding English letter grades to display the thermal health status of an individual.

[0087] The thermal health status output module also includes an APP remote signal output module, which converts the visual image into a remote signal and sends it to a mobile phone app to facilitate remote monitoring of the individual's thermal health status.

[0088] 2. The present invention provides a method for predicting thermal health of the elderly, comprising the following steps:

[0089] S1. Model building

[0090] The comprehensive evaluation model of thermal health and thermal environment of the present invention is a thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model constructed based on a thermal physiological prediction model and a thermal comfort prediction model. The thermal physiological dynamic response characteristics and thermal sensation dynamic response characteristics under the characteristic parameters of environmental temperature mutation are respectively collected through the two prediction models to construct a thermal sensation prediction model based on thermal physiological parameters.

[0091] (1) Thermophysiological prediction model

[0092] The thermophysiological prediction model of the present invention is a thermoregulation-cardiovascular regulation model formed by coupling the thermoregulation-cardiovascular (heart rate) model and the simplified cardiovascular regulation model. Figure 3As shown in Figure 1, the human body is divided into three layers, including the central compartment (heart and lungs), the core layer (organs, bones, muscles and tissues) and the skin layer. When exposed to a sudden non-neutral environment, the human body regulates its core temperature through vasoconstriction, vasodilation, shivering heat production and sweating. Among them, in hot and cold environments, the skin vasoconstriction and dilation, blood pressure increases and decreases accordingly, and the heart increases or decreases the core temperature by increasing or decreasing the heart rate; in a cold environment, vasoconstriction and shivering can increase the metabolic rate to increase the body's own heat production and thus increase the body's core temperature, while in a hot environment, vasodilation and sweating can enhance the body's heat dissipation to reduce the core temperature.

[0093] The thermal regulation-cardiovascular (heart rate) model (see the literature: https: / / doi.org / 10.1016 / j.buildenv.2024.112186); the simplified cardiovascular regulation model is the Windkessel model. The Windkessel model divides cardiovascular regulation into the heart, pulmonary circulatory system and systemic circulatory system. The systemic circulatory system is further subdivided into the head, trunk, upper limbs and lower limbs. The heart includes the left atrium, left ventricle, right atrium and right ventricle. In addition to the heart, the systemic circulatory system also includes arteries and veins. This model simplifies the human blood circulation system by analogy with the circuit system: blood pressure corresponds to voltage, blood flow corresponds to current, blood flow resistance is analogous to resistance, and blood volume corresponds to capacitance. The volume change of each capacity system is calculated by combining the inflow and outflow of blood from adjacent systems. The blood flow is determined by the pressure difference between the front and rear systems and the resistance inside the systemic circulatory system.

[0094]

[0095] In formulas (1)-(2), θ represents time [s], the subscripts in and out represent inflow and outflow, respectively, V refers to the blood volume in the blood vessel [L], Q refers to the blood flow [L / s], P refers to blood pressure [mmHg], and R represents blood flow resistance [mmHg·s / L].

[0096] According to Levick's cardiovascular theory, the measurement point of blood pressure in medicine is located at the junction of the left ventricle and the aorta. Due to the contraction and relaxation of the heart, blood pressure fluctuates periodically over time. The average blood pressure during this period is called the mean aortic pressure (MAPao), the highest value is the systolic blood pressure (SBP), and the lowest value is the diastolic blood pressure (DBP). The difference between systolic and diastolic blood pressure is the pulse pressure (PP). The blood flow through the aorta is called cardiac output (CO), and its changes are affected by fluctuations in core temperature (Tcr) and skin temperature (Tsk).

[0097]

[0098] MAP ao(θ)=CO(θ)TPR(θ) (4)

[0099] In formulas (3)-(4), the subscript ao represents the aorta, and CO refers to cardiac output [cm 3 / h], MAP refers to mean arterial pressure [mmHg], and TPR refers to total peripheral vascular resistance [mmHg·s / L].

[0100]

[0101]

[0102] CO0=HR0SV (8)

[0103] In formulas (5)-(8), CO dil and CO con and CO0 represent diastolic, systolic and basal cardiac output [cm 3 / h], SV represents the stroke volume and is assumed to be 70 cm3 for the elderly 3 , T cr represents the core temperature, T sk represents skin temperature, T cr,n represents the neutral core temperature, T sk,n represents neutral skin temperature, CO min represents the minimum cardiac output [cm 3 / h] is 0.95 times of the basal cardiac output, CO max represents the maximum cardiac output [cm 3 / h] is 1.40 times of the basic cardiac output, HR O Stands for basal heart rate [bpm].

[0104] TPR(θ)=R heart +R pulmonary +R body (θ) (9)

[0105] In formula (9), R heart Refers to the resistance of the cardiac circulatory system [mmHg·s / L], R pulmonary Refers to the resistance of the pulmonary circulation system [mmHg·s / L], R body Refers to the body's circulatory system resistance [mmHg·s / L]. They can be calculated by analogizing the blood flow resistance between the systems in Table 1 to the circuit resistance, connecting the resistance in series and in parallel.

[0106] The resistance of the systemic circulatory system consists of the circulation of the head, trunk, upper limbs and lower limbs in parallel. In addition, arteriovenous anastomoses (AVAs) can be observed in the upper and lower limbs (hands and feet). In the hands and feet, blood flows in parallel with the AVA through capillaries. The AVA connects arterioles and veins and regulates heat transfer from the core of the hands and feet to the skin mainly through the contraction or expansion of blood vessels. When the ambient temperature drops suddenly, the AVA closes, increasing the resistance between the arteries and veins, thereby reducing blood flow.

[0107]

[0108] O(θ)=0.148[T sk (θ)-T sk,n ]+0.532[T cr (θ)-T cr,n ]+0.510 (14)

[0109] In formulas (10)-(14), R head , R upper , R trunk , R lower Refers to the capillary resistance of the head, trunk, upper limbs and lower limbs respectively [mmHg·s / L] (see Table 1). s Represents the body's arterial or venous vascular resistance [mmHg·s / L], R suav represents capillary resistance [mmHg·s / L], T cr,n represents the neutral core temperature [℃], T sk,n represents the neutral skin temperature [℃], R AVA,min It represents the minimum resistance of the AVA vascular segment, and O represents the degree of opening of the AVA. If O≥1, then O=1; if O≤0, then O=0.

[0110] In summary, the formula for calculating blood pressure is as follows:

[0111]

[0112] In formulas (15)-(16), SBP and DBP refer to systolic blood pressure and diastolic blood pressure [mmHg], respectively, and PP refers to pulse pressure [mmHg].

[0113] Furthermore, by converting equations (4)-(10) and (15)-(16), we can obtain:

[0114]

[0115] In formulas (17) and (18), SBP is systolic blood pressure [mmHg], DBP is diastolic blood pressure [mmHg], and R heart Refers to cardiac circulatory resistance [mmHg·s / L], R pulmonaryPulmonary circulation resistance [mmHg·s / L], R head , R upper , R trunk , R lower refers to the capillary resistance of the head, trunk, upper limbs and lower limbs [mmHg·s / L] (see Table 1), PP refers to pulse pressure [mmHg], CO dil and CO con and CO0 represent diastolic, systolic and basal cardiac output [cm 3 / h].

[0116] When the heart rate and blood pressure corresponding to the central compartment layer and the core temperature and skin temperature corresponding to the core layer and skin layer are predicted by the above-mentioned thermal physiological prediction model, the thermal regulation system provides the core temperature (T cr ) and skin temperature (T sk ) drives the cardiovascular system, which uses it to calculate the blood flow resistance at the arteriovenous anastomosis (R AVA ) and cardiac output (CO). Blood pressure can be calculated by inputting cardiac output (CO) and total peripheral vascular resistance. The thermal regulation system further calculates blood flow based on cardiac output (CO), and finally calculates heart rate by combining the individual's own state with environmental parameters. This coupling model can predict the real-time dynamic response characteristics of human indicators to the environment based on thermal regulation mechanisms and cardiovascular regulation mechanisms.

[0117] Specifically, the thermal regulation system calculates the core temperature (T cr ) and skin temperature (T sk )

[0118] Core Temperature:

[0119]

[0120] In formula (19), α is the mass coefficient of the skin layer, θ is the time [s], and A D is the body area [m 2 ], m is the total body mass, C p,b is the specific heat of the human body and is assumed to be 3490 (J / kg℃), T cr is the core temperature [°C], M is the metabolic rate [W / m 2 ], W is the work done to the outside [W / m 2 ], Q cr–sk is the heat exchange between the core layer and the skin layer [W / m 2 ], Q res is the heat dissipation from breathing [W / m 2 ].

[0121] Where M is estimated by the activity equivalent based on the basal metabolic rate M0:

[0122] M=Energy metabolism coefficient at different activity states*M0

[0123] In the above formula, the activity states include: lying still, sitting still, standing up, and showering. The energy metabolism coefficients corresponding to lying still, sitting still, standing up, and showering are 0.7 met, 1.0 met, 1.2 met, and 3.0 met, respectively.

[0124]

[0125] In the above formula, M0 is the basal metabolic rate [W / m 2 ], m is body mass (kg), l is height (m), Age is age (y).

[0126]

[0127] In the above formula, K is the thermal conductivity between the core layer and the skin layer and is set to 5.28 W / m 2 K, C p,bl is the specific heat capacity of blood and is set to 4187 J / kg℃, m bl,0 The basic blood flow (kg / m 2 s) and can be calculated from the neutral sitting state, m bl,dil is the blood flow during vasodilation, m bl,con is the blood flow rate during vasoconstriction, m bl,max is the maximum blood flow, m bl,min is the minimum blood flow, and CO is the cardiac output.

[0128] Q res (θ) = M (θ) {0.0014 [34-T a (θ)]+0.0173[5.87-P a (θ)]}

[0129]

[0130] In the above formula, Pa is the partial pressure of water vapor (kPa), RH is the relative humidity of air (%), T a is the dry bulb temperature.

[0131] Skin temperature:

[0132]

[0133] In formula (20), α is the mass coefficient of the body skin layer, θ is the time [s], i is the body node, and A D is the body area [m 2 ], m is the total body mass, Cp,b is the specific heat of the human body and is assumed to be 3490 (J / kg℃), T sk is the skin temperature [°C], Q cr–sk is the heat exchange between the core layer and the skin layer [W / m 2 ], Q conv ,Q rad , and Q evap They are heat conduction, radiation and evaporation between the skin layer and the environment [W / m 2 ].

[0134]

[0135]

[0136] T cl (θ)=35.7-0.028(MW)-0.155I cl (θ){(MW)-3.05×10 -3 ×[5733-6.99(MW)-P a (θ)]-0.42[(MW)-58.15]-1.7×10 -5 M(5867-P a (θ))-0.0014M(34-T a (θ))}

[0137]

[0138] In the above formula, h c is the convective heat transfer coefficient [W / m 2 ℃], T cl is clothing temperature (℃), V a is the air velocity (m / s), I cl is the thermal resistance of clothing (clo), T g is the black globe temperature (℃).

[0139] Q evap (θ) = w(θ)(E sw ) max (θ)

[0140]

[0141] WSIG b (θ)=max{0,T b (θ)-T b,n}

[0142] WSIG sk (θ)=max{0,T sk (θ)-T sk,n}

[0143] T b (θ)=α(θ)T sk (θ)+[1-α(θ)]T cr (θ)

[0144] T b,n =α(θ)T sk,n +[1-α(θ)]T cr,n

[0145] (E sw ) max (θ) = f pcl (θ)h e (θ)[P sk(s) (θ)-P a (θ)

[0146]

[0147] h e (θ)=16.7h c (θ)

[0148] P sk(s) (θ)=0.256T sk (θ)-3.373

[0149] In the above formula, w is the skin moisture, m sw is the skin sweating rate, h fg is the heat of evaporation of water vapor and is assumed to be 2430 J / kg, (Esw)max is the maximum heat dissipation due to sweating (W / m 2 ), WSIG b is the body temperature signal, WSIG is the skin temperature signal, α is the skin layer mass ratio, f pcl is the clothing water vapor content coefficient, h e is the evaporation heat exchange coefficient (W / m 2 ℃), P sk(s) is the absolute pressure of water vapor at skin temperature [kPa], T b,n The neutral average body temperature.

[0150] The cardiovascular regulation system calculates the systolic and diastolic blood pressures using equations (17)-(18), and calculates the heart rate using equation (21).

[0151] Heart rate:

[0152]

[0153] In formula (21), HR is heart rate [bpm], θ is time [s], and M is metabolic rate [W / m 2], M0 is the basal metabolic rate [W / m 2 ], b is the coefficient related to gender, age, and weight, A D is the body area [m 2 ], β is the coefficient of heart rate response to core temperature changes and is assumed to be 33 [bpm / ℃] for adults, α is the body skin mass coefficient, m is the total body mass, C p,b is the specific heat of the human body and is assumed to be 3490 (J / kg℃), Average ambient dry bulb temperature around people [℃], P a is the water vapor partial pressure [kPa], i is the body node, K is the thermal conductivity between the core layer and the skin layer and is assumed to be 5.28 W / m 2 K, C p,bl is the specific heat capacity of blood and is assumed to be 4187 J / (kg℃), is the skin blood flow [kg / m 2 s], T cr and T sk are core temperature and skin temperature [°C].

[0154] Table 1 Blood flow resistance between systems

[0155]

[0156]

[0157] In Table 1, the front system and the rear system indicate the flow direction of blood, and the blood flows from the front system to the rear system.

[0158] (2) Thermal comfort prediction model

[0159] The elderly's evaluation of thermal sensation and thermal comfort can be reflected by two indicators: thermal sensation (TSV) and thermal comfort (TCV). Different thermal sensations and thermal comfort levels also correspond to different thermal health states. The thermal comfort prediction model used in this invention is the thermal sensation and thermal comfort model in the ASHRAE 2021 standard. TSV is defined as the average body temperature (t b ) from the cold and hot set points representing the lower and upper limits of the heat regulating evaporation function: t b,c and t b,h The values ​​of these set points depend on the net rate of internal heat generation. TSV has 7 levels: '-3': cool, '-2': cool, '-1': slightly cool, '0': neutral, '1': slightly warm, '2': warm, '3': hot.

[0160]

[0161] In formulas (22)-(24), t b is the average body temperature, tb,c is the body temperature cold set point, t b,h is the body temperature thermal set point, η e,v is the evaporation efficiency and is assumed to be 0.85, M is the metabolic rate [W / m 2 ], W is the work done to the outside [W / m 2 ].

[0162] When body temperature is below the cold set point, TCV is numerically equal to the thermal sensation vote (TSENS); when body temperature is regulated by sweating, TCV is related to skin wetness:

[0163]

[0164] In formula (25), E rsw is the sweating heat, E rsw,req is the heat of respiration, E max is the maximum heat of evaporation, E dif is the heat of evaporation.

[0165] The TCV level is divided into 4 levels: '0': comfortable, '1': slightly uncomfortable but acceptable, '2': uncomfortable and unacceptable, '3': very uncomfortable. It is calculated by the formula (25) and rounded off.

[0166] In summary, we combine the thermal physiological prediction model with the thermal comfort prediction model to obtain a comprehensive evaluation model for thermal health and thermal environment. The model includes four levels of thermal health, namely neutral / basic thermal health, thermal health with physiological compensation, thermal subhealth, and thermal unhealth, and their thermal health levels show a decreasing trend. Correspondingly, according to the different thermal environments, the thermal health status under the thermal environment is further divided into seven categories, namely neutral / basic thermal health (A), thermal health with vasodilation compensation (Bh), thermal health with vasoconstriction compensation (Bc), thermal subhealth (Ch), cold subhealth (Cc), thermal unhealth (Dh), and cold unhealth (Dc). Among them, h corresponds to a hot environment, and c corresponds to a cold environment. Each type of health status corresponds to a different thermal environment, thermal regulation and cardiovascular regulation strategy, physiological index and psychological index range, and thermal health status.

[0167] The thermal health of an individual's heart, body, and brain is determined as follows:

[0168] A: When the individual's heart rate and blood pressure are at the basic neutral level (HR = HR O ,BP=BP O ), the heart is in a neutral / basal thermal health state; when the core temperature T cr , Skin temperature T sk , average body temperature T b (Same as tb ) are both neutral values, the body is in a neutral / basic thermal health state; when the thermal sensation and thermal comfort scores are both 0, the brain is in a neutral / basic thermal health state;

[0169] Bh: When 30% 〖HR〗_max < individual heart rate < 50% 〖HR〗_max, 100 / 60mmHg ≤ blood pressure < 160 / 85mmHg, and when the neutral value <T cr <37.0℃, neutral value <T sk <38.0℃, t b,c ≤T b <t b,h Or when the thermal sensation and thermal comfort scores are ≤1, the heart is in a thermal health state with vasodilation compensation; when the neutral value <T cr <37.0℃, neutral value <T sk <38.0℃, t b,c ≤T b <t b,h , the body is in a thermal health state with vasodilation compensation; when 0<thermal sensation and thermal comfort score ≤1, the brain is in a thermal health state with vasodilation compensation;

[0170] Bc: When 30%〖HR〗_max<individual heart rate<50%〖HR〗_max, 100 / 60mmHg≤blood pressure<160 / 85mmHg, and when 35.0℃ <T cr < neutral value, 10.2℃ <T sk <Neutral value, 34.3℃≤T b <t b,c Or when the thermal sensation score is ≥ -1 and the thermal comfort score is ≤ 1, the heart is in a thermal health state with vasoconstriction compensation; when 35.0℃ <T cr <Neutral value, 10.2℃ <T sk <Neutral value, 34.3℃≤T b <t b,c , the body is in a thermal health state with vasoconstriction compensation; when the thermal sensation score is ≥-1 and the thermal comfort score is ≤1, the brain is in a thermal health state with vasoconstriction compensation;

[0171] Ch: When 50%〖HR〗_max≤individual heart rate<70%〖HR〗_max, 160 / 85mmHg≤blood pressure<180 / 100mmHg, and when 37.0≤T cr <37.3℃, neutral value <T sk <38.0℃, t b,h ≤T b <37.3℃ or when the thermal sensation and thermal comfort score is ≤2, the heart is in a sub-healthy state of heat; when 37.0≤T cr<37.3℃, neutral value <T sk <38.0℃, t b,h ≤T b When the temperature is less than 37.3℃, the body is in a sub-healthy state of heat; when 1<thermal sensation and thermal comfort score≤2, the brain is in a sub-healthy state of heat;

[0172] Cc: When 50%〖HR〗_max≤individual heart rate<70%〖HR〗_max, 160 / 85mmHg≤blood pressure<180 / 100mmHg, and when 35.0℃ <T cr <Neutral value, 10.2℃ <T sk <Neutral value, 32.1℃<T b <34.3℃ or when the thermal sensation score is ≥-2 and the thermal comfort score is ≤2, the heart is in a cold subhealthy state; when 35.0℃ <T cr <Neutral value, 10.2℃ <T sk <Neutral value, 32.1℃<T b <34.3℃, the body is in a cold subhealthy state; when the thermal sensation score is ≥-2 and the thermal comfort score is ≤2, the brain is in a cold subhealthy state;

[0173] Dh: When the individual heart rate ≥70%〖HR〗_max, blood pressure ≥180 / 100mmHg, the heart is in a thermally unhealthy state; when T cr ≥37.3℃, T sk ≥38.0℃,T b ≥37.3℃, the body is in an unhealthy state of heat; when the thermal sensation and thermal comfort score = 3, the brain is in an unhealthy state of heat;

[0174] Dc: When the individual heart rate ≤ 30% 〖HR〗_max, blood pressure ≥ 180 / 100mmHg, the heart is in a cold and unhealthy state; when T cr ≤35.0℃,T sk ≤10.2℃,T b ≤32.1℃, the body is in an unhealthy cold state; when the thermal sensation score = -3 and the thermal comfort score = 3, the brain is in an unhealthy cold state.

[0175] The output steps of the seven health states of an individual are as follows: Figure 4 As shown:

[0176] A: When the individual's heart rate and blood pressure are at the basic neutral level (HR = HR O ,BP=BP O ), core temperature T cr , Skin temperature T sk , average body temperature T b (Same as t b) are both neutral values, and the individual's thermal sensation and thermal comfort scores are both 0, the individual is in a neutral / basic thermal health state; this is an ideal state, indicating that the individual's physiological response is normal in the current environment;

[0177] The neutral level varies from person to person. When a standard adult is at the neutral level, the core temperature is 36.8°C, the skin temperature is 33.7°C, the average body temperature is 36.5°C, the heart rate is 70bpm, and the blood pressure is 120 / 80mmHg; when an elderly person is at the neutral level, the core temperature is 36.6±0.3°C, the skin temperature is 33.5±0.6°C, the heart rate is 70±8bpm, and the blood pressure is 120 / 80mmHg.

[0178] B: When 30%〖HR〗_max<individual heart rate<50%〖HR〗_max, 100 / 60mmHg≤blood pressure<160 / 85mmHg, the individual is in a thermal health state with physiological compensation; the range of blood pressure refers to the thermal health state when both systolic and diastolic blood pressure meet the corresponding conditions;

[0179] And the neutral value <T cr <37.0℃, neutral value <T sk <38.0℃, t b,c ≤T b <t b,h , 0<thermal sensation and thermal comfort score ≤ 1, the individual is in a thermal health state with vasodilation compensation (Bh);

[0180] Or when 35.0℃ <T cr <Neutral value, 10.2℃ <T sk <Neutral value, 34.3℃≤T b <t b,c , thermal sensation score ≥ -1, thermal comfort score ≤ 1, the individual is in a thermal health state with vasoconstriction compensation (Bc);

[0181] C: When 50%〖HR〗_max≤individual heart rate<70%〖HR〗_max, 160 / 85mmHg≤blood pressure (systolic / diastolic)<180 / 100mmHg, the individual is in a sub-healthy state;

[0182] And when 37.0≤T cr <37.3℃, neutral value <T sk <38.0℃, t b,h ≤T b <37.3℃, 1<thermal sensation and thermal comfort score ≤2, the individual is in a sub-healthy state of heat (Ch); the individual may begin to feel heat stress and need to take measures to prevent further heat damage.

[0183] Or when 35.0℃ <Tcr <Neutral value, 10.2℃ <T sk <Neutral value, 32.1℃<T b <34.3℃, thermal sensation score ≥-2, thermal comfort score ≤2, the individual is in a cold sub-healthy state (Cc); indicating that the individual is in a colder environment and needs to keep warm to prevent hypothermia.

[0184] Dh: When the individual heart rate ≥70%〖HR〗_max, blood pressure ≥180 / 100mmHg, T cr ≥37.3℃, T sk ≥38.0℃,T b ≥37.3℃, thermal sensation and thermal comfort score = 3, the individual is in an unhealthy thermal state; this is a warning sign that the individual may be suffering from severe heat stress and needs to take immediate measures to cool down.

[0185] Dc: When the individual heart rate ≤ 30% 〖HR〗_max, blood pressure ≥ 180 / 100mmHg, T cr ≤35.0℃,T sk ≤10.2℃,T b ≤32.1℃, thermal sensation score = -3, thermal comfort score = 3, the individual is in an unhealthy cold state. This indicates that the individual may be experiencing severe cold stress and needs to take immediate measures to keep warm.

[0186] It should be noted that the maximum heart rate [HR]_max = 208-0.7*age bpm (see reference: Tanaka, H., Monahan, KD, & Seals, DR (2001). Age-predicted maximal heart rate revisited. Journal of the American College of Cardiology, 37 (1), 153-156.)

[0187] S2. Input parameters

[0188] The parameters of this model involve environmental parameters and personal parameters, where environmental parameters include air temperature, relative humidity, and air speed; personal parameters include personal characteristics (gender, age, height, weight), activity status, and clothing thermal resistance. The air temperature includes dry bulb temperature and mean radiation temperature. The dry bulb temperature and relative humidity are monitored by temperature and humidity sensors installed at the return air of the room air conditioner. The mean radiation temperature is measured by a black globe thermometer, and the wind speed is measured by a thermal anemometer. Figure 2 shown.

[0189] S3. Determine thermal health status

[0190] Based on the constructed model, the individual's core temperature, skin temperature, heart rate and blood pressure, as well as thermal sensation and thermal comfort state in a specific thermal environment are predicted to determine the individual's thermal health state.

[0191] S4, output thermal health status

[0192] Output the individual's thermal health status, and use different colored figures and corresponding English letter grades to visually display the evaluation results. Figure 5 , Figure 6 shown.

[0193] Each thermal health status is represented by a specific color and English letter identifier:

[0194] Green: Neutral / basic thermal health (A)

[0195] Light green: thermal health (Bh) with vasodilation compensation.

[0196] Dark green: thermal health with vasoconstriction compensation (Bc).

[0197] Yellow: hot sub-health (Ch).

[0198] Blue: Cold sub-health (Cc).

[0199] Red: Hot and unhealthy (Dh).

[0200] Dark blue: cold and unhealthy (Dc).

[0201] Among them, neutral / basic thermal health and thermal health states that can be compensated physiologically are comfortable and healthy thermal health states, while sub-health and unhealthy are uncomfortable thermal health states.

[0202] In the output visualization image, the body, heart, and brain of the little man may appear in different colors. This indicates that in a transient or temperature environment, the thermal health status of different parts of the body may be different. The evaluation priority is uncomfortable thermal health status > comfortable and healthy thermal health status. Under this premise, the evaluation priority is heart > body > brain. The thermal health of the heart is in a more sensitive position. The combination of these colors and letters provides an intuitive and visual way to quickly identify and distinguish different thermal health states. The corresponding letters and visualization images will be sent to the mobile phones of the elderly and their families as remote signals on the APP to facilitate monitoring and understanding of the thermal health status of the elderly.

[0203] It should be noted that the body of the little man represents the core temperature, skin temperature and average body temperature, the heart represents heart rate and blood pressure, and the brain represents thermal sensation and thermal comfort state. It is expressed intuitively by organ analogy and does not represent specific parts. The definition of sub-thermal health and unhealthy is the judgment when there is physiological or psychological discomfort. Taking into account the dynamic transition, the response speed of the human heart, psychology or body temperature is inconsistent. It may happen that the human body temperature or heart responds but the psychology does not respond in time, or the elderly have inaccurate cognition. Therefore, physiological discomfort is given priority when judging the level, but it may also happen that the human body feels uncomfortable first and then the physiology responds. Therefore, discomfort is the priority judgment condition, but physiological discomfort takes precedence over psychological discomfort, and heart discomfort takes precedence over temperature discomfort.

[0204] 3. Embodiment

[0205] (I) Sample selection

[0206] This study selected a nursing home in Chongqing, a hot summer and cold winter climate zone, as the field research site. The nursing home has 150 elderly people with an average age of 85. All the elderly people live in rooms of the same size and layout. The room layout structure is as follows: Figure 7 As shown. Each room is equipped with the same split-type air conditioner, and the use is adjusted by the elderly according to their personal needs. The study selected February 23 to March 12, 2023 (a total of 18 days) and August 25 to September 4 (a total of 11 days) as winter and summer test conditions. The average outdoor temperature during the test was 12.0℃ and 30.0℃, respectively. 30 healthy elderly people over 60 years old were selected as the research sample (15 men: 83.6±9.5 years old, BMI21.9±1.9; 15 women: 83.7±4.8 years old, BMI24.5±3.6). They all lived in the local area for at least one year, had no history of cardiovascular disease, and had no caffeine, alcohol or smoking habits.

[0207] (II) Testing process

[0208] The test is divided into two phases and two types of working conditions: the dynamic main phase of getting up and the steady-state auxiliary phase of sitting still, as well as winter and summer working conditions. Figure 8 As shown. To ensure the accuracy and completeness of the data, the subjects wore the monitoring equipment before going to bed at night. After waking up in the morning, the tester entered the bedroom to start the test. The process of getting up includes three typical actions: lying down, sitting down, and standing up. The elderly can choose whether to wear clothes when sitting or standing up according to their personal wishes, and the tester will record and estimate it. HR, BP and T sk The cochlear temperature (T crThe whole process of getting up lasts for 5 minutes. After getting up, an auxiliary sitting phase is arranged to obtain the basal metabolic rate (M0) and neutral core temperature (T cr,n ), neutral skin temperature (T sk,n )、Blood flow(m bl ), basal heart rate (HR0), heart rate response core temperature coefficient (β) and basal blood pressure (BP0) to provide support for model construction. The sitting phase lasted for 10 minutes.

[0209] Thermal environment monitoring parameters include dry bulb temperature (T a ), black globe temperature (T g ), bed temperature (T cover ) and relative humidity (RH), while physiological monitoring parameters include T cr , T sk , HR and BP. 30 temperature and humidity sensors (AqaraR3, ±0.3℃, 1min) and black ball thermometers (HQZY-1, ±0.3℃, real-time) were installed on the bedside tables (0.6m high) in each room. cover The measuring point was between the bottom of the quilt and the top of the abdomen (iBotton DS1922L, ±0.3℃, 1min). During the test, the bedroom doors and windows were kept closed and the air conditioner was not used. During the sitting phase, the seats were arranged in a position to avoid air conditioning convection. The wind speed was close to 0, so no measurement was performed. HR and BP were measured by a physiological monitoring bracelet worn on the wrist (ADZAL16, ±1bpm / ±7mmHg, 2min). T sk The temperature was measured by placing button thermometers (Botton DS1922L, ±0.3℃, 1min) at seven parts of the body, including the forehead, chest, arm, back of hand, thigh, calf and instep. cr The temperature was measured with the help of a cochlear thermometer (SWK, ±0.3°C, real-time).

[0210] (III) Data processing

[0211] Since the thermal resistance of clothing changes during waking up, especially in winter, the thermal resistance of clothing (I cl ), the average temperature of the human body's surrounding environment and standard effective temperature (SET*) to better compare different I cl Transient thermal environment. Human bare skin temperature (T sk,br ), clothing skin temperature (T sk,cl ), and the average skin temperature (T sk,ov) were calculated with reference to the previous thermal regulation-cardiovascular (heart rate) model. The mean absolute error (MAE) of the predicted and measured values ​​was used to evaluate the absolute error of the model, and the mean absolute percentage error (MAPE) of the predicted and measured values ​​was used to evaluate the relative error. Data processing was done using Python and PyCharm to build the numerical model, and Origin for graphics. The normality test was used to verify whether the data were normally distributed, and the two-sample t-test was used to analyze whether there were significant differences between the data groups.

[0212] (IV) Results

[0213] 1. Characteristics of sudden changes in ambient temperature when waking up

[0214] Characteristics of sudden changes in ambient temperature when waking up: Fig. 9 As shown by Fig. 9 It can be seen that the average winter T a The average summer temperature is 17.4℃. a T is 26.5℃ g With T a Similar, and both remain relatively stable. Winter and summer T cover About 32.0℃. To highlight the sudden temperature difference, based on each action and I cl , and further calculated and SET*. Due to the cold winter and the elderly wearing 0.42clo thick pajamas, and SET* dropped by 14.2℃ and 8.0℃ respectively. In the comfortable summer, the elderly wore 0.23clo thin pajamas. and SET* decreased by 3.9℃ and 2.7℃, respectively. In summary, the elderly experience a large temperature drop when getting up in winter, which may cause significant fluctuations in their thermal physiological indicators.

[0215] 2. Thermal health response and prediction of the elderly

[0216] Before building the thermal regulation-cardiovascular regulation coupling model, it is necessary to determine the basic boundary conditions of the model. The data were collected during the sitting stage and have an important impact on the calculation of heat production, heat dissipation and blood flow in the thermal regulation and cardiovascular regulation mechanism of the human body. Through statistical analysis (the basic boundary conditions of the model are shown in Table 2), the M0 and m bl Threshold, β, T cr,n , T sk,n They are 40%, 50%, 60%, 0.2℃ and 0.2℃ lower than those of standard adults, respectively. This reflects the weakening of thermal regulation and cardiovascular regulation in the elderly.

[0217] Table 2 Basic boundary conditions of the model

[0218]

[0219] (1) Core temperature

[0220] like Fig.10 As shown in the actual measurement value: In winter, due to the large temperature difference experienced by the body, T cr From 36.6℃, it gradually drops by 0.3℃. In summer, due to the small temperature difference, T cr A slight decrease of 0.1℃ from 36.5℃. Combined with the mechanism model, although the activity intensity during waking up increased by 0.5met, which in turn increased the core heat production, the temperature difference between the core layer and the skin layer increased, and the heat dissipation of the skin layer decreased with T sk decreases and increases, which in turn leads to T cr Prediction value: calculated by using the algebraic formula of core temperature in the thermal physiological prediction model of the present invention. In winter, T cr is 36.7-36.8℃; in summer, T cr The temperature of the vasoconstriction and relaxation of the elderly is 36.5-37.2℃. Since this model takes into account the differences in the vasoconstriction and relaxation regulation thresholds and individual factors of the elderly, T cr The mean absolute error (MAE) between the predicted value and the measured value is ±0.3° C. It can be seen that the error is within an acceptable range, and the thermophysiological parameter prediction model of the present invention is accurate enough to be used for the prediction of human physiological parameters.

[0221] (2) Skin temperature

[0222] like Fig.11 As shown in the actual measurement value: In winter, when the elderly wake up, only their heads are exposed to low temperature environment, T sk,ov is 34.8℃, where T sk,br After sitting up, because the upper body is partially exposed, T sk,br decreased by 0.7℃, resulting in T sk,ov After adding a top and pants, T sk,ov remained relatively stable, dropping only 0.3℃. sk,br In summer, even if T sk,ov also dropped by 0.3℃, but T sk,br It only dropped by 0.2℃. Combined with the mechanism model, cold stimulation can cause skin blood vessels to contract, leading to T sk Similarly, due to the thermal insulation effect of clothing, T sk,br T sk,cl Lower and faster decline rate. In addition, since metabolic rate is the main parameter in the clothing temperature calculation formula, clothing temperature will change during waking up. Prediction value: calculated using the algebraic formula of skin temperature in the thermal physiological prediction model of the present invention, in winter, T skis 34.5-34.0℃; in summer, T sk is 33.9-33.8℃. This model is not only suitable for aging but also reduces m bl and the heat it transfers, and the clothing temperature is increased by the previous model to increase the heat dissipation, so T sk The MAE is ±1.3° C. It can be seen that the error is within an acceptable range, and the thermophysiological parameter prediction model of the present invention is accurate enough to be used for the prediction of human physiological parameters.

[0223] (3) Heart rate

[0224] like Fig.12 As shown in the figure, the actual measured values ​​are: in winter, HR is lower when waking up (66bpm), and it suddenly accelerates to 70bpm after sitting up. After standing up again, HR accelerates to 74bpm. HR in summer is similar to that in winter, but only slightly increases by 4bpm after standing up. Combined with the cardiovascular regulation mechanism model, it can be seen that the fluctuation of HR is affected by metabolic rate, environmental parameters, and T cr With T sk The HR when standing exceeds HR0 (70 bpm), which indicates that the increase in HR is not only due to the increase in activity metabolic rate, but also related to the decrease in ambient temperature and the increase in body temperature difference. In addition, the contraction of skin blood vessels caused by cold stimulation will slightly increase CO to maintain adequate blood circulation, resulting in a 5.0-10.0% increase in HR reflex. This model adjusts M0, T through personality aging adaptation cr,n After β, the predicted value is calculated by using the algebraic formula of the heart rate in the thermophysiological prediction model of the present invention. In winter, HR is 68-72bpm; in summer, HR is 62-70bpm. The MAE of the HR prediction value is ±5bpm, and the MAPE is ±5.8%. It can be seen that the error is within the acceptable range, and the thermophysiological parameter prediction model of the present invention is accurate enough to be used for the prediction of human physiological parameters.

[0225] (4) Blood pressure

[0226] like Fig.13 As shown in the figure, the actual measured values ​​are: the systolic blood pressure fluctuations are not obvious under cold stimulation in either winter or summer. Only after standing up, there is a small fluctuation in diastolic blood pressure (5 mmHg), and the response in winter and summer is similar. Combined with the cardiovascular regulation mechanism model, due to the process of getting up, T cr and T sk,ov The measured value only decreased by 0.1-0.3°C, so the increase in CO caused by vasoconstriction was small, which in turn caused MAP aoThe fluctuation is not large. Only the sharp drop in the exposed parts of the extremities after standing up will cause a sharp increase in vascular resistance here, which will in turn cause a small sudden increase in diastolic pressure. This model takes into account the influence of body thermal regulation by combining the AVA model, so that the BP prediction accuracy is relatively high. The predicted value: calculated by using the algebraic formula of blood pressure in the thermal physiological prediction model of the present invention, in winter, BP is 122 / 76-126 / 82mmHg; at the same time, in summer, BP is 122 / 80-124 / 82mmHg. MAE is ±5mmHg, and MAPE is ±5.2%. It can be seen that the error is within an acceptable range, and the thermal physiological parameter prediction model of the present invention is accurate enough to be used for the prediction of human physiological parameters.

[0227] Combined with the above prediction data, after evaluation by the comprehensive evaluation model of thermal health and thermal environment of the present invention, the thermal health level of the elderly during getting up in winter is as follows: Fig.14 As shown in Figure 2, the elderly can better protect their body temperature due to the adaptive behavior of self-dressing when getting up in winter. Therefore, the process of getting up in winter is thermal health (Bh) with vasodilation compensation. The thermal health level of the process of getting up in summer is as follows: Fig.15 As shown, in summer, since the bedroom temperature is suitable, the elderly do not wear clothes, so the process of getting up in summer is neutral or thermal health with vasoconstriction compensation (Bc).

[0228] By using the comprehensive evaluation model of thermal health and thermal environment of the present invention, real-time prediction of the thermal health status of the human body can be achieved by only inputting environmental and individual parameters, which can play a role in early warning and timely regulating the environment to protect the thermal health of the elderly.

[0229] In summary, in order to reveal and predict the response mechanism of thermal health of the elderly under sudden changes in ambient temperature, this study proposed a coupled mechanism model of human thermal regulation-cardiovascular regulation-thermal comfort, and verified the model using 30 elderly people in the typical daily sudden changes in ambient temperature in winter and summer as examples, and the following main conclusions were drawn:

[0230] (1) A coupled mechanism model of human thermal regulation and cardiovascular regulation suitable for dynamic non-uniform thermal environments was established, which can reveal and predict the real-time response of human core temperature, skin temperature, heart rate and blood pressure to thermal environment parameters.

[0231] (2) The model boundary condition parameters were adjusted, including basal metabolic rate, neutral core temperature, neutral skin temperature, blood flow regulation threshold, and heart rate response core temperature coefficient, to make it suitable for the elderly population.

[0232] (3) When getting up, the elderly may face cardiovascular health risks such as thermal discomfort in the extremities and sudden increases in heart rate and diastolic blood pressure, and the model can better predict their thermal health response (MAPE < 6.0%).

[0233] (4) This study can provide a reference for creating a comfortable and healthy bedroom thermal environment suitable for the elderly in future residences and nursing homes.

[0234] Potential Applications: Although the adaptive behavior of adding clothing in winter and the passive thermal regulation of the body make T sk,ov and T sk,cl The decrease in T sk,br and T cr The cooling rate is faster. This may cause thermal discomfort in the exposed parts of the elderly. In addition, the increase in M, respiratory heat dissipation and vasoconstriction in the exposed parts aggravate the mutation of HR and DBP. Increased heart rate or blood pressure will increase the cardiovascular health risk of the elderly, which may lead to health accidents such as dizziness, palpitations or falls. Combined with previous research results, when the bedroom temperature is low, that is, the temperature difference between the quilt and the bedroom is greater than 15.0℃, as the ambient temperature difference increases, even if the elderly add more clothes, T cr and T sk The decrease in temperature and the increase in HR will also increase significantly. To ensure the thermal comfort and thermal health of the elderly, this study recommends raising the bedroom temperature to above 17.0℃ in the late sleep period and before getting up in winter. Although the temperature difference in summer is less than 15.0℃, the elderly should slow down the changes in posture when getting up. In addition, it is recommended that the elderly can adapt to the bedroom environment by adding clothes in both winter and summer.

[0235] Compared with existing studies, the innovations of this study are as follows: (1) Modeling and revealing how thermal environment parameters affect the human body's thermal regulation-cardiovascular coupling mechanism; (2) The coupling mechanism model was modified for aging; (3) Focusing on the less explored daily sleep-wake-up mutation scenario. Due to the poor thermal sensitivity of the elderly, it is difficult to clearly distinguish the skin temperature of different parts of the body. Therefore, the complex design and large amount of time required to accurately predict the local skin temperature of each node and its corresponding local thermal sensation have limited research significance for the elderly. The simplified model proposed in this study not only reduces the complexity of the numerical simulation process, but also can accurately predict T cr , T sk , HR and BP respond to environmental parameters in real time (MAPE is less than 6.0%). This thermal regulation-cardiovascular regulation coupling mechanism model can be widely used to predict the thermal health response of the human body in a dynamic non-uniform thermal environment. In addition to the process of getting up, it can also be expanded to other scenarios.

[0236] It should be noted that the actual measured values ​​of temperature, heart rate, blood pressure, etc. in the embodiments of the present invention are all average values.

[0237] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit the technical solution. Those skilled in the art should understand that those modifications or equivalent substitutions of the technical solution of the present invention that do not depart from the purpose and scope of the technical solution should be included in the scope of the claims of the present invention.

Claims

1. A thermal health prediction system for the elderly, characterized in that: include: Parameter input module: used to input personal parameters and environmental parameters to determine the thermal environment of the individual; Thermal health status judgment module: Analyze the input parameters and predict the individual's core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal comfort and thermal sensation through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. According to the values ​​of core temperature, skin temperature, heart rate, blood pressure and average body temperature, as well as thermal comfort and thermal sensation, the thermal health status of the individual is judged; Thermal health status output module: outputs the thermal health status of the individual.

2. The thermal health prediction system for the elderly according to claim 1 is characterized in that: In the parameter input module, personal parameters include gender, age, height, weight, activity status, clothing thermal resistance, environmental parameters include air temperature, relative humidity and air speed; the activity status includes: lying still, sitting still, standing and showering.

3. The thermal health prediction system for the elderly according to claim 1 is characterized in that: The thermal health status output module includes a visual image production module, which uses small figures of different colors and corresponding English letter grades to display the thermal health status of an individual.

4. The thermal health prediction system for the elderly according to claim 3 is characterized in that: The thermal health status output module also includes an APP remote signal output module, which converts the visual image into a remote signal and sends it to a mobile phone app to facilitate remote monitoring of the individual's thermal health status.

5. The thermal health prediction system for the elderly according to claim 3 is characterized in that: The visualization image production module uses figures of different colors and corresponding English letter grades to display the thermal health status of an individual, wherein the thermal health status includes neutral / basic thermal health, thermal health that can be compensated by vasodilation, thermal health that can be compensated by vasoconstriction, hot sub-health, cold sub-health, hot unhealthy and cold unhealthy; Specifically include: Green represents neutral / basic thermal health, represented by the letter A; Light green indicates thermal health that can be compensated by vasodilation, represented by the letter Bh; Dark green indicates thermal health that can be compensated by vasoconstriction, represented by the letters Bc; Yellow indicates sub-health due to heat, represented by the letter Ch; Blue indicates cold sub-health, represented by the letter Cc; Red indicates unhealthy heat, represented by the letters Dh; Dark blue indicates cold and unhealthy, represented by the letters Dc.

6. The thermal health prediction system for the elderly according to claim 5 is characterized in that: When the body, heart, and brain of the little man show different colors respectively, the priority of judging the thermal health status is uncomfortable thermal health status > comfortable and healthy thermal health status. Under this premise, the priority of evaluation is heart > body > brain.

7. The thermal health prediction system for the elderly according to claim 1 is characterized in that: The calculation formula for predicting an individual's core temperature, skin temperature, heart rate, blood pressure, and average body temperature through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model is: Core Temperature: In formula (1), α is the mass coefficient of the skin layer, θ is the time [s], and A D is the body area [m 2 ], m is the total body mass, C p,b is the specific heat of human body [J / kg℃], T cr is the core temperature [℃], M is the metabolic rate [W / m 2 ], W is the external work [W / m 2 ], Q cr–sk is the heat exchange heat between the core layer and the skin layer [W / m 2 ], Q res is the heat dissipation from breathing [W / m 2 ]; Skin temperature: In formula (2), T sk is the skin temperature [℃], i is the body node, Q conv , Q rad and Q evap They are heat conduction, radiation and evaporation between the skin layer and the environment [W / m 2 ]; Heart rate: In formula (3), HR is heart rate [bpm], M0 is basal metabolic rate [W / m 2 ], b is the coefficient related to gender, age, and weight, β is the coefficient of heart rate response to core temperature changes [bpm / ℃], is the average ambient dry-bulb temperature around humans [℃], P a is the water vapor partial pressure [kPa], K is the thermal conductivity between the core layer and the skin layer [W / m 2 K],C p,bl is the specific heat of blood [J / kg℃], is the skin blood flow [kg / m 2 s]; Systolic blood pressure: In formula (4), SBP is systolic blood pressure [mmHg], CO dil is the diastolic cardiac output [cm 3 / h], CO con is the cardiac output [cm 3 / h], CO0 is based on cardiac output [cm 3 / h],R heart is the resistance of the cardiac circulatory system [mmHg·s / L], R pulmonary is the resistance of the pulmonary circulation system [mmHg·s / L], R head , R upper , R trunk , R lower is the capillary resistance of the head, trunk, upper limbs, and lower limbs [mmHg·s / L], respectively, and PP is the pulse pressure [mmHg); Diastolic blood pressure: In formula (5), DBP is diastolic blood pressure [mmHg]; Average body temperature: T b (θ)=α(θ)T sk (θ)+[1-α(θ)]T cr (i) (6) In formula (6), T b The average body temperature.

8. The elderly thermal health prediction system according to claim 1 is characterized in that: The method to determine the thermal health status of an individual is: A: When the individual's heart rate and blood pressure are at a neutral baseline, the core temperature T cr , Skin temperature T sk , average body temperature T b When both are neutral values ​​and the individual's thermal sensation and thermal comfort scores are 0, the individual is in a neutral / basic thermal health state; B: When 30%〖HR〗_max<individual heart rate<50%〖HR〗_max, 100 / 60mmHg≤blood pressure<160 / 85mmHg, the individual is in a heat health state with physiological compensation; And the neutral value <T cr <37.0℃, neutral value <T sk <38.0℃, t b,c ≤T b <t b,h , 0< thermal sensation and thermal comfort score ≤ 1, the individual is in a thermal health state with vasodilation compensation; Or when 35.0℃ <T cr < neutral value, 10.2℃ <T sk <Neutral value, 34.3℃≤T b <t b,c , thermal sensation score ≥ -1, thermal comfort score ≤ 1, the individual is in a thermal health state with vasoconstriction compensation; C: When 50%〖HR〗_max≤individual heart rate<70%〖HR〗_max, 160 / 85mmHg≤blood pressure<180 / 100mmHg, the individual is in a sub-healthy state; And when 37.0≤T cr <37.3℃, neutral value <T sk <38.0℃, t b,h ≤T b <37.3℃, 1<thermal sensation and thermal comfort score ≤2, the individual is in a sub-healthy state of heat; Or when 35.0℃ <T cr < neutral value, 10.2℃ <T sk <Neutral value, 32.1℃<T b <34.3℃, thermal sensation score ≥-2, thermal comfort score ≤2, the individual is in a cold subhealthy state; Dh: When the individual heart rate ≥70%〖HR〗_max, blood pressure ≥180 / 100mmHg, T cr ≥37.3℃, T sk ≥38.0℃,T b ≥37.3℃, thermal sensation and thermal comfort score = 3, the individual is in a thermally unhealthy state; Dc: When the individual heart rate ≤ 30% 〖HR〗_max, blood pressure ≥ 180 / 100mmHg, T cr ≤35.0℃,T sk ≤10.2℃,T b ≤32.1℃, thermal sensation score = -3, thermal comfort score = 3, the individual is in an unhealthy cold state.

9. A method for predicting thermal health of the elderly, characterized in that: The prediction system according to any one of claims 1 to 8 is used to implement the method, comprising the following steps: (1) Input gender, age, height, weight, activity status, clothing thermal resistance, air temperature, relative humidity, and air speed into the parameter input module; (2) The thermal health status judgment module is used to analyze the input parameters, and the core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal sensation and thermal comfort status of the individual are predicted through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. The thermal health status of the individual is judged according to the numerical values ​​of core temperature, skin temperature, heart rate, blood pressure and average body temperature, as well as thermal sensation and thermal comfort status; (3) The thermal health status output module is used to output the thermal health status of the individual, and the thermal health status of the individual is displayed using figures of different colors and corresponding English letter grades.

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