A heat health prediction system and method for the elderly

Abstract

The application discloses an old person thermal health prediction system and a 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; a thermal health state judgment module, which 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 a thermal health state where the individual is located according to the numerical value of the physiological parameters and the thermal sensation and the thermal comfort; and a thermal health state output module, which is used for outputting the thermal health state of the individual. Through the system, the prediction of personal physiological parameters, thermal sensation and thermal comfort under different thermal environments can be realized, and then the thermal health state of the human body under different thermal environments can be obtained. Then, an intuitive thermal health state prompt is provided for the old people through a visual module, and the old people are beneficial to 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 a state of physiological and psychological comfort in a thermal environment, while ensuring physiological health. Thermal subhealth refers to a state of physiological discomfort or subjective discomfort in a thermal environment, without causing substantial physiological illness. Thermal unhealth refers to a state in which a thermal environment no longer meets the basic requirements for physiological health and thermal comfort, resulting in the development of physiological and psychological pathological symptoms. With the aging population, thermal health issues facing the elderly in hot environments are becoming increasingly prominent. In the increasingly intense summer heat, air conditioning plays a crucial role in improving residents' thermal health. Research on air conditioning use among the elderly has found that high-income elderly people tend to actively use air conditioning to improve their thermal environment, while lower-income elderly people tend to be less concerned about thermal comfort. Furthermore, the proportion of low-income elderly people is higher, and some elderly people are reluctant to use air conditioning to save electricity costs. Existing standards stipulate that the design indoor temperature for elderly people staying long-term is between 26.0 and 28.0°C. Even if elderly people use air conditioning, they may still set the temperature too high to reduce operating costs or too low to avoid cooling down. In this case, not only can the air conditioner's effect of improving the thermal environment not be achieved, but the elderly, due to their low immunity, are also prone to colds, muscle pain, cardiovascular problems, sleep problems, etc. due to being in a cold or hot environment for a long time.

[0003] Furthermore, as they age, the elderly's perception and regulatory abilities decline, making them more susceptible to changes in the indoor environment. Seniors may not perceive cold or heat as quickly as younger people, and their physical reactions may be delayed. Therefore, it is necessary to develop a thermal environment and thermal health prediction method that can clearly identify the elderly's real-time thermal health status, provide a reminder, and guide their behavior in turning on and off air conditioning and adjusting the set temperature.

[0004] Existing research also focuses on the thermal comfort of elderly individuals in indoor building environments. To assess their perceived thermal comfort, this approach typically combines on-site environmental data testing with subjective voting by participants. This traditional method is time-consuming and labor-intensive, and fails to account for the elderly's low sensitivity to thermal changes. Subjective descriptions may not necessarily reflect their true thermal perceptions. Some studies have combined human thermal perception with physiological parameters to more accurately assess different thermal environments and the comfort of elderly individuals within those environments. However, these studies have limited data and their application. Environmental changes affect the human body not only through psychological perception but, more importantly, through physiological responses to these changes. These physiological responses reflect the extent to which thermal health is affected. Furthermore, with economic and social development, people are placing greater emphasis on health. A method that can predict thermal conditions in adverse thermal environments and provide a comprehensive description of thermal health status within these environments would undoubtedly enhance the ability of elderly individuals to adapt to these conditions. However, current research on thermal health assessment is limited, focusing solely on exploring temperature thresholds and physiological parameter ranges that contribute to different thermal health states. There is no systematic categorization or definition of thermal environments and the resulting thermal health states. Furthermore, existing thermal health assessments fail to address the complexities and unique characteristics of the elderly population. After thermal health predictions are conducted, there is a lack of a method to intuitively present the results. This hinders the widespread application and dissemination of thermal environment and thermal health system predictions. 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 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 solutions adopted by the present invention are 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 assessment module: This module analyzes the input parameters and predicts the individual's core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal comfort, and thermal sensation through a thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. Based on the values ​​of core temperature, skin temperature, heart rate, blood pressure, average body temperature, as well as thermal comfort and thermal sensation, the module assesses the individual's thermal health status.

[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, and clothing thermal resistance; environmental parameters include air temperature, relative humidity, and air speed; and the activity status includes: lying still, sitting still, standing, and showering.

[0012] Furthermore, 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.

[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 generation module uses figures of different colors and corresponding English letter grades to display the individual's thermal health status, wherein the thermal health status includes neutral / basic thermal health, thermal health with vasodilation compensation, thermal health with vasoconstriction compensation, sub-health due to heat, sub-health due to cold, unhealthy due to heat, and unhealthy due to cold.

[0015] Specifically include:

[0016] Green indicates 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 evaluation priority 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 as follows:

[0025] Core Temperature:

[0026]

[0027] In formula (1), α is the mass coefficient of the skin layer, θ is the time [s], A D is the body area [m 2 ], m is the total body mass, C p,b is the human body specific heat [J / kg℃], T cr is the core temperature [°C], 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 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 dry-bulb temperature of the environment around people [℃],

[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 capacity 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; 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] Furthermore, the method for determining the thermal health status of an individual is as follows:

[0045] A: When the individual's heart rate and blood pressure are at the 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 / 60 mmHg ≤ blood pressure < 160 / 85 mmHg, the individual is in a heat-healthy 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 sub-health status;

[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 subhealth state;

[0052] Dh: When the individual's 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's heart rate is ≤30%〖HR〗_max, blood pressure is ≥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, implemented using the above-mentioned prediction system, comprises 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 predict the individual's core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal sensation and thermal comfort status through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. The individual's thermal health status is judged based on 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. By constructing a coupled mechanism model for thermal regulation, cardiovascular regulation, and thermal comfort, this invention can predict an individual's core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal sensation, and thermal comfort under different thermal environments. By substituting this predicted data into a comprehensive evaluation model for thermal health and the thermal environment, the thermal health status of the human body under different thermal environments can be determined. A visualization module then visually displays the assessment results using colored figures and corresponding alphabetical grades. This visualized thermal health status provides intuitive thermal health information for the elderly, enabling even non-professionals to quickly understand an individual's thermal adaptation. This color and image visualization can serve as an educational and training tool to enhance understanding of thermal health status, helping the elderly and caregivers understand the importance of thermal health and how to take appropriate preventative measures to maintain thermal well-being. In particular, this visualization can be applied to popular science activities focused on thermal health care for the elderly. It can help audiences easily understand the impact of cold or hot environments on physiological thermal health, thereby increasing motivation for air conditioning regulation and improving indoor thermal environments, and providing guidance on increasing or decreasing air conditioning setpoint temperatures.

[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 generated by the system can be uploaded to the APP on the mobile phone, 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 of remote monitoring. If the current thermal environment is not a healthy thermal environment, the family members can remind the elderly to turn on or 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 protect 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 This is a schematic structural diagram of the elderly thermal health prediction system 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 Schematic diagram 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 A visual diagram of the thermal health status of the elderly according to the present invention;

[0066] Figure 6 A classification diagram of thermal health status according to the present invention;

[0067] Figure 7 This is a layout diagram of a nursing home room in an embodiment;

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

[0069] Figure 9 This is a data diagram showing the characteristic data of the sudden change in the ambient temperature when waking up in an 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] Figure 10 The comparison chart of the core temperature measurement and prediction values ​​in the embodiment; (a) winter; (b) summer;

[0071] Figure 11: This is a comparison chart of the skin temperature measurement and prediction values ​​in the embodiment; where (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] Figure 12 A comparison chart of the heart rate measurement and prediction values ​​in the embodiment; wherein (a) is winter; (b) is summer;

[0073] Figure 13 The comparison chart of blood pressure measurement and prediction values ​​in the embodiment; (a) winter; (b) summer;

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

[0075] Figure 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 methods of the present invention are further described in detail below with reference to specific examples.

[0077] The present invention invents a thermal health prediction system and method for thermal environments based on the physiological parameters and thermal perception of the elderly, taking into account their physiological characteristics. 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, using different graphics and colors to represent the body's thermal health status. This method is simple and intuitive. After seeing the thermal health evaluation results, if the elderly find that their thermal health status is not good, they can actively turn on or off 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 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 assessment module: This module analyzes the input parameters and predicts the individual's core temperature, skin temperature, heart rate, blood pressure, thermal comfort, and thermal sensation through a thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. Based on the values ​​of core temperature, skin temperature, heart rate, and blood pressure, as well as thermal comfort and thermal sensation, the module assesses the individual's thermal health status.

[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, and clothing thermal resistance, and environmental parameters include air temperature, relative humidity, and air speed. The activity status includes: lying down, sitting down, 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 determined by looking up the ASHRAE 55 standard table after knowing the clothing type. Specifically, the clothing thermal resistance is equal to the sum of the clothing thermal resistances of all clothing items worn by an individual. The clothing type is estimated using 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 the 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. Build the model

[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 the thermal physiological prediction model and the 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 collected through the two prediction models respectively, and a thermal sensation prediction model based on thermal physiological parameters is constructed.

[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, the human body is divided into three layers: the central compartment (heart and lungs), the core (organs, bones, muscles, and tissues), and the skin. When exposed to sudden, non-neutral environments, the human body regulates its core temperature through vasoconstriction, vasodilation, shivering, and sweating. In hot and cold environments, skin vasoconstriction and dilation increase and decrease blood pressure, and the heart increases or decreases heart rate to increase or decrease core temperature. In cold environments, vasoconstriction and shivering increase metabolic rate, increasing heat production and thus raising core temperature. In hot environments, vasodilation and sweating enhance heat dissipation, lowering 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 circulation system, and systemic circulation system. The systemic circulation system is further divided 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 circulation 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 within the systemic circulation system.

[0094]

[0095] In formulas (1) and (2), θ represents time [s], the subscripts in and out represent inflow and outflow, respectively, V refers to the intravascular blood volume [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, blood pressure is measured medically at the junction of the left ventricle and the aorta. Due to the heart's contraction and relaxation, blood pressure fluctuates cyclically over time. The average blood pressure during this period is called mean aortic pressure (MAPao), with the highest value being the systolic blood pressure (SBP) and the lowest being the diastolic blood pressure (DBP). The difference between the systolic and diastolic blood pressures is the pulse pressure (PP). The amount of blood flowing through the aorta is called cardiac output (CO), and its changes are influenced 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 cardiac output 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 basal 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 resistances in series and in parallel.

[0106] The resistance of the systemic circulatory system is composed of parallel circulation in the head, trunk, upper limbs, and lower limbs. Furthermore, arteriovenous anastomoses (AVAs) can be observed in the upper and lower limbs (hands and feet). In the hands and feet, blood flows parallel to the AVAs through capillaries. The AVAs connect arterioles and veins and regulate heat transfer from the core of the hands and feet to the skin primarily through vasoconstriction or dilation. When the ambient temperature drops sharply, the AVAs close, increasing the resistance between the arteries and veins and thus 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 [mmHg·s / L] respectively (see Table 1). s Represents the body's arterial or venous resistance [mmHg·s / L], R suav represents capillary resistance [mmHg·s / L], T cr,n represents the neutral core temperature [°C], 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) and (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 circulation resistance [mmHg·s / L], R pulmonaryRefers to pulmonary 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 in turn uses it to calculate the blood flow resistance (R AVA ) and cardiac output (CO). Blood pressure can be calculated by inputting CO and total peripheral vascular resistance. The thermal regulation system further calculates blood flow based on CO, and ultimately calculates heart rate by combining the individual's own state with environmental parameters. This coupled model can predict the real-time dynamic response characteristics of human indicators to the environment based on thermal 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 body 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 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 dissipated by 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), and 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 5.28W / m 2 K, C p,bl is the specific heat of blood and is set to 4187 J / kg℃, m bl,0 Basal blood flow (kg / m 2 s) and can be calculated from neutral sitting, m bl,dil is the blood flow during vasodilation, m bl,con is the blood flow 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 water vapor partial pressure (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 time [s], i is the body node, 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 heat 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 speed (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 water vapor content coefficient of clothing, 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 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 dry-bulb temperature of the environment 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 various systems

[0155]

[0156]

[0157] In Table 1, the front system and the rear system indicate the direction of blood flow, 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. The definition of TSV is the average body temperature (t b ) from the cold set point and the hot set point representing the lower and upper limits of the heat regulation evaporation function: t b,c and t b,h The values ​​of these set points depend on the net rate of internal heat production. 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 external work [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, and 3: very uncomfortable. The value is calculated by rounding off according to formula (25).

[0166] In summary, by combining the thermal physiology prediction model with the thermal comfort prediction model, we can develop a comprehensive evaluation model for thermal health and the thermal environment. This model includes four levels of thermal health: neutral / basic thermal health, thermal health with physiological compensation, thermal subhealth, and thermal unhealth. These levels of thermal health follow a decreasing trend. Correspondingly, based on the thermal environment, thermal health states are further divided into seven categories: 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). h corresponds to a warmer environment, and c corresponds to a cooler environment. Each health state corresponds to a different thermal environment, thermal and cardiovascular regulation strategies, physiological and psychological indicator ranges, 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 / basic 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 <37.3℃, the body is in a sub-healthy state due to heat; when 1 < thermal sensation and thermal comfort score ≤ 2, the brain is in a sub-healthy state due to 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 sub-healthy state; when the thermal sensation score is ≥-2 and the thermal comfort score is ≤2, the brain is in a cold sub-healthy state;

[0173] Dh: When the individual's heart rate is ≥70%〖HR〗_max, blood pressure is ≥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 a thermally unhealthy state; when the thermal sensation and thermal comfort score = 3, the brain is in a thermally unhealthy state;

[0174] Dc: When the individual's heart rate is ≤30%〖HR〗_max, blood pressure is ≥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 a cold and unhealthy state; when the thermal sensation score = -3, the thermal comfort score = 3, the brain is in a cold and unhealthy state.

[0175] The steps for outputting 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 a 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 a 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, and blood pressure 100 / 60 mmHg ≤ < 160 / 85 mmHg, the individual is in a thermally healthy state with physiological compensation. The blood pressure range refers to the situation where both systolic and diastolic blood pressures meet the corresponding conditions, and the individual is considered to be in a thermally healthy state.

[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 pressure / diastolic pressure) < 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 needs 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-health state (Cc); indicating that the individual is in a cold environment and needs to keep warm to prevent hypothermia.

[0184] Dh: When the individual's 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's heart rate is ≤30%〖HR〗_max, blood pressure is ≥180 / 100mmHg, T cr ≤35.0℃,T sk ≤10.2℃, T b ≤32.1°C, Thermal Perception Score = -3, Thermal Comfort Score = 3, indicating an individual is experiencing an unhealthy cold state. This indicates that the individual may be experiencing severe cold stress and needs to take immediate steps to keep warm.

[0186] It should be noted that the maximum heart rate [HR]_max = 208 - 0.7 * age bpm (see 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 radiant 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 radiant 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 status in a specific thermal environment are predicted to determine the individual's thermal health status.

[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 alphabetical identifier:

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

[0195] Light green: Thermal health (Bh) with compensatory vasodilation.

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

[0197] Yellow: Sub-health due to heat (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 physiologically compensatory thermal health states are comfortable and healthy thermal health states, while sub-health and unhealthy are uncomfortable thermal health states.

[0202] In the output visualization, the figure's body, heart, and brain may appear in different colors. This indicates that in transient or temperature environments, the thermal health status of different parts of the body may vary. The evaluation priority is uncomfortable thermal health > comfortable thermal health. Under this premise, the evaluation priority is heart > body > brain. The heart's thermal health is more sensitive. The combination of these colors and letters provides an intuitive visualization method to quickly identify and distinguish different thermal health states. The corresponding letters and visualization images will be sent as remote signals on the app to the elderly person and their family's mobile phones, allowing for easy monitoring and understanding of the elderly person's thermal health status.

[0203] It should be noted that the figure's body represents 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. Organ analogies are used for intuitive expression and do not represent specific parts of the body. The definition of sub-thermal health and unhealthiness is based on the determination of physiological or psychological discomfort. Considering the dynamic transitions in which the response speeds of the human heart, psychology, or body temperature are inconsistent, it is possible that the body temperature or heart responds but the psychology does not respond in time, or that the elderly may have inaccurate cognition. Therefore, physiological discomfort is prioritized when determining the level. However, it is also possible that the body feels discomfort first and then the physiology responds. Therefore, discomfort is the priority criterion, but physiological discomfort takes precedence over psychological discomfort, and cardiac discomfort takes precedence over temperature discomfort.

[0204] 3. Examples

[0205] (1) Sample selection

[0206] This study selected a nursing home in Chongqing, a city with a hot summer and cold winter climate, as the field research site. The nursing home has 150 elderly people with an average age of 85 years. All the elderly people live in rooms with 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 usage 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, 2023 (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 research samples (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] (2) 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 To ensure data accuracy and completeness, subjects wore the monitoring device before going to bed. After waking up in the morning, the tester entered the bedroom to start the test. The process of getting up includes three typical movements: lying down, sitting down, and standing up. The elderly can choose whether to wear clothes when sitting or standing according to their personal wishes, and the tester will record and estimate them. HR, BP and T sk The cochlear temperature (T crThe whole process of getting up lasted for 5 minutes. After getting up, an auxiliary sitting phase was 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 to core temperature coefficient (β), and basal blood pressure (BP0) to support model construction. The sitting phase lasted 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 cover 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 located between the bottom of the bedding and the top of the abdomen (iBotton DS1922L, ±0.3°C, 1 min). During the test, the bedroom doors and windows were kept closed and no air conditioning was 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 using a physiological monitoring bracelet worn on the wrist (ADZAL16, ±1bpm / ±7mmHg, 2min). sk The temperature was measured by placing a button thermometer (Botton DS1922L, ±0.3℃, 1min) on seven parts of the body, including the forehead, chest, arm, back of the hand, thigh, calf and instep. cr The temperature was measured with the help of a cochlear thermometer (SWK, ±0.3°C, real-time).

[0210] (3) Data processing

[0211] Since the thermal resistance of clothing changes during getting up, especially in winter, the thermal resistance of clothing (I cl ), average temperature of the environment around the human body and standard effective temperature (SET*) to better compare different I cl Transient thermal environment. Human naked skin temperature (T sk,br ), clothing skin temperature (T sk,cl ), and the average skin temperature (T sk,ov) were calculated using the previously described thermal regulation-cardiovascular (heart rate) model. Model validation used the mean absolute error (MAE) metric to assess absolute error between predicted and measured values, while the mean absolute percentage error (MAPE) metric to assess relative error between predicted and measured values ​​was used. Data processing used Python and PyCharm to construct the numerical model, and Origin for graphics. Normality tests were performed to verify normal distribution of the data, and two-sample t-tests were used to analyze whether there were significant differences between data groups.

[0212] (4) Results

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

[0214] Characteristics of sudden changes in ambient temperature when waking up Figure 9 As shown by Figure 9 It can be seen that the average winter T a The average temperature in summer is 17.4℃. a T is 26.5℃ g With T a Similar, and both remain relatively stable. cover All about 32.0℃. In order 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°C and 2.7°C, respectively. In summary, the elderly experience a significant temperature drop when getting up in winter, which may lead to 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 phase and have an important impact on the calculation of heat production, heat dissipation, and blood flow in the human body's thermal regulation and cardiovascular regulation mechanisms. Through statistical analysis (see Table 2 for the basic boundary conditions of the model), 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, which reflects the weakening of thermal regulation and cardiovascular regulation functions in the elderly.

[0217] Table 2 Basic boundary conditions of the model

[0218]

[0219] (1) Core temperature

[0220] like Figure 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 getting 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 The decrease of T cr Decrease. Prediction value: calculated 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 T is 36.5-37.2℃. Since this model takes into account the vasoconstriction and relaxation regulation thresholds and individual factors of the elderly, cr The mean absolute error (MAE) between the predicted and measured values ​​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 sufficiently accurate and can be used to predict human physiological parameters.

[0221] (2) Skin temperature

[0222] like Figure 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, with a drop of only 0.3℃. However, T sk,br In summer, even if T sk,ov It 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 constrict, leading to T sk Similarly, due to the thermal insulation effect of clothing, T sk,br T sk,cl Lower and faster decline. In addition, since metabolic rate is the main parameter in the clothing temperature calculation formula, clothing temperature will change during the process of getting 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 Figure 12 As shown in the figure, the actual measured values ​​are: in winter, HR is low when waking up (66 bpm), but it suddenly accelerates to 70 bpm after sitting up. After standing up again, HR accelerates to 74 bpm. HR in summer is similar to that in winter, but only increases slightly by 4 bpm after standing up. Combined with the cardiovascular regulation mechanism model, it can be seen that HR fluctuations are caused by metabolic rate, environmental parameters, and T cr With T sk The HR is determined by the temperature difference between the body and the surrounding environment. The fact that HR exceeds HR0 (70 bpm) when standing suggests that the increase in HR is not only due to the increased metabolic rate of activity, 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 personalized aging. cr,n After calculating β, the predicted values ​​are calculated using the algebraic formula for heart rate in the thermophysiological prediction model of the present invention. In winter, the HR is 68-72 bpm; in summer, the HR is 62-70 bpm. The MAE of the predicted HR values ​​is ±5 bpm, and the MAPE is ±5.8%. This indicates that the errors are within acceptable ranges. The thermophysiological parameter prediction model of the present invention is sufficiently accurate and can be used to predict human physiological parameters.

[0225] (4) Blood pressure

[0226] like Figure 13 As shown in the figure, the actual measured values ​​are: the systolic blood pressure fluctuations under cold stimulation are not obvious in both winter and summer. Only a small fluctuation (5mmHg) in diastolic blood pressure occurs after standing, and the responses in winter and summer are 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 led to a decrease in 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 there, which in turn causes a small sudden increase in diastolic blood pressure. This model takes into account the influence of thermal regulation of the body by combining the AVA model, so that the BP prediction accuracy is relatively high. The predicted value: calculated using the algebraic formula of blood pressure in the thermal physiological prediction model of the present invention, in winter, BP is 122 / 76-126 / 82 mmHg; at the same time, in summer, BP is 122 / 80-124 / 82 mmHg. MAE is ±5 mmHg, 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 thermal health and thermal environment comprehensive evaluation model of the present invention, the thermal health level of the elderly during getting up in winter is as follows: Figure 14 As shown in Figure 2, the elderly can better protect their body temperature when getting up in winter due to the adaptive behavior of self-dressing. 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: Figure 15 As shown in the figure, 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 inputting only environmental and individual parameters, which can play a role in early warning and timely regulation of the environment to protect the thermal health of the elderly.

[0229] In summary, to reveal and predict the response mechanism of thermal health in the elderly under sudden changes in ambient temperature, this study proposed a coupled mechanism model of human thermal regulation, cardiovascular regulation, and thermal comfort. The model was validated using 30 elderly people during typical daily waking-up and sudden changes in ambient temperature in winter and summer. 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 to 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 increasing clothing in winter and the passive thermal regulation measures of the body make T sk,ov and T sk,cl The degree of reduction is similar to that in summer, but 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 the results of previous studies, 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 HR and the increase in HR will also increase significantly. To ensure thermal comfort and thermal health in older adults, this study recommends raising bedroom temperature to above 17.0°C during the late stages of sleep and before waking up in winter. Although the temperature difference in summer is less than 15.0°C, older adults should slow down their posture changes when waking up. Furthermore, it is recommended that older adults adjust their bedroom environment by adding more clothing 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) Making aging-friendly corrections to the coupling mechanism model; (3) Focusing on the less explored daily sudden change scenario of waking up after sleep. 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 are of limited significance for the study of 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 less than 6.0%). This thermal regulation-cardiovascular regulation coupling mechanism model can be widely used to predict human thermal health responses in dynamic non-uniform thermal environments. 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 solutions of the present invention rather than to limit the technical solutions. Those skilled in the art should understand that modifications or equivalent replacements of the technical solutions of the present invention that do not depart from the purpose and scope of the technical solutions of the present invention should be included in the scope of the claims of the present invention.

Claims

1. A thermal health prediction system for the elderly, characterized by: include: Parameter input module: used to input personal parameters and environmental parameters to determine the thermal environment of the individual; Thermal health status assessment module: This module analyzes the input parameters and predicts the individual's core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal comfort, and thermal sensation through a thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. Based on the values ​​of core temperature, skin temperature, heart rate, blood pressure, average body temperature, as well as thermal comfort and thermal sensation, the module assesses the individual's thermal health status. Thermal health status output module: outputs the thermal health status of the individual; The thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model is constructed based on a thermophysiological prediction model and a thermal comfort prediction model. The thermophysiological dynamic response characteristics and thermal sensation dynamic response characteristics under the characteristic parameters of the environmental temperature mutation are respectively collected through the two prediction models to construct a thermal sensation prediction model based on thermophysiological parameters. The elderly's evaluation of thermal sensation and thermal comfort can be reflected by two indicators: thermal sensation and thermal comfort. Different thermal sensation and thermal comfort levels also correspond to different thermal health states. The thermal comfort prediction model is the thermal sensation and thermal comfort model in the ASHRAE 2021 standard. The definition of thermal sensation is the average body temperature t b Deviation from the cold and hot set points representing the lower and upper limits of the heat regulation evaporation function: t b,c and t b,h .

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

3. The elderly thermal health prediction system 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 elderly thermal health prediction system 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 the mobile phone app to facilitate remote monitoring of the individual's thermal health status.

5. The elderly thermal health prediction system 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 individual's thermal health status, wherein the thermal health status includes neutral / basic thermal health, thermal health with vasodilation compensation, thermal health with vasoconstriction compensation, sub-health due to heat, sub-health due to cold, unhealthy due to heat, and unhealthy due to cold; Specifically include: Green indicates 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 elderly thermal health prediction system according to claim 5, characterized in that: 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.

7. The elderly thermal health prediction system according to claim 1, 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], A D is the body area [m 2 ], m is the total body mass, C p,b is the human body specific heat [J / kg℃], T cr is the core temperature [°C], 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 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 / ℃], P is the average dry-bulb temperature of the environment around people [℃], 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 capacity 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; 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 is 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 the 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; B: When 30% HR max < individual heart rate < 50% HR max, 100 / 60 mmHg ≤ blood pressure < 160 / 85 mmHg, the individual is in a heat-healthy 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 sub-health status; 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 subhealth state; Dh: When the individual's 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's heart rate is ≤30%〖HR〗_max, blood pressure is ≥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 implemented, 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 predict the individual's core temperature, skin temperature, heart rate, blood pressure, average body temperature, thermal sensation and thermal comfort status through the thermal regulation-cardiovascular regulation-thermal comfort coupling mechanism model. The individual's thermal health status is judged based on 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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