Metabolic rate detection method and system and wearable sensor

Abstract

The invention discloses a metabolic rate detection method and system and a wearable sensor. The method comprises the following steps: acquiring a metabolic rate parameter of a target object; the metabolic rate parameters comprise heart rate, skin resistance, muscle rate or local heat loss parameters; the local heat loss parameter is used for representing a measurement result of a target detection part of the target object based on the local heat loss measurement module; determining local surface heat loss according to the local heat loss parameter; determining whole-body total heat loss and whole-body unit heat loss according to the local surface heat loss and the convective heat loss coefficient of the target detection part; the convective heat loss coefficient is the proportion of the convective heat loss of the target detection part in the convective heat loss of all parts; and determining the metabolic rate of the target object according to the whole body unit heat loss, the heart rate, the skin resistance and the muscle rate. The accuracy of metabolic rate measurement is improved, and the size and the cost of a corresponding detection system are reduced. The method can be widely applied to the technical field of metabolic rate evaluation.

Description

Technical Field

[0001] The present invention relates to the technical field of metabolic rate assessment, and in particular to a metabolic rate detection method, system and wearable sensor. Background Art

[0002] Thermal comfort significantly impacts people's satisfaction and productivity in the workplace. Assessing thermal comfort involves both environmental and personal parameters, with personal parameters such as metabolic rate and clothing insulation playing a key role in accurately determining thermal comfort.

[0003] Related technologies have proposed various methods and models to express and evaluate human metabolic rate. One embodiment proposes metabolic rate measurement instruments (such as indirect and direct calorimetry) that are bulky, have limited locations, are expensive, and lack wearability. Another embodiment employs a machine learning algorithm to estimate energy expenditure using parameters such as heart rate, heat flow, and activity information, but this method suffers from low accuracy. Summary of the Invention

[0004] The purpose of the present invention is to solve one of the technical problems existing in the prior art to at least a certain extent.

[0005] To this end, the object of the present invention is to provide a highly accurate metabolic rate detection method, system and wearable sensor.

[0006] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present invention include the following aspects:

[0007] On the one hand, an embodiment of the present invention provides a metabolic rate detection method, which is detected by a metabolic rate detection system, wherein the metabolic rate detection system includes a local heat loss measurement module; the method includes: collecting metabolic rate parameters of a target object; the metabolic rate parameters include heart rate, skin resistance, muscle rate or local heat loss parameters; the local heat loss parameters are used to characterize the measurement results of the target detection part of the target object based on the local heat loss measurement module; determining the local surface heat loss according to the local heat loss parameters; determining the overall total heat loss and the overall unit heat loss according to the local surface heat loss and the convective heat loss coefficient of the target detection part; the convective heat loss coefficient is the proportion of the convective heat loss of the target detection part to the convective heat loss of all parts; determining the metabolic rate of the target object according to the overall unit heat loss, the heart rate, the skin resistance and the muscle rate. The present application determines the overall unit heat loss through the local surface heat loss, and then determines the metabolic rate based on the overall unit heat loss, which is conducive to improving the accuracy of metabolic rate measurement. At the same time, the detection method improved by the present application is simple and easy to implement, which is conducive to reducing the volume and cost of the corresponding detection system.

[0008] Furthermore, in one embodiment of the present invention, the local heat loss measurement module includes a microchannel and temperature and humidity detection units located at both ends of the microchannel, the microchannel includes: a serpentine channel, a circular channel or a straight channel, and the local surface heat loss is determined according to the local heat loss parameter, including:

[0009] Determining a first temperature and humidity at a channel entrance and a second temperature and humidity at a channel exit by means of the temperature and humidity detection unit;

[0010] A local surface heat loss is determined according to the first temperature and humidity and the second temperature and humidity.

[0011] Furthermore, in one embodiment of the present invention, determining the overall total heat loss and the overall unit heat loss based on the local surface heat loss and the convective heat loss coefficient of the target detection location includes:

[0012] Collect the surface temperature and ambient temperature of each detection part;

[0013] determining the convective heat loss of each of the detection locations according to the surface temperature and the ambient temperature;

[0014] determining the convective heat loss coefficient according to the convective heat loss of each of the detection locations;

[0015] determining an overall total heat loss based on the local surface heat loss and the convection heat loss coefficient;

[0016] An overall unit heat loss is determined based on the overall total heat loss and the body surface area of ​​the target object.

[0017] Furthermore, in one embodiment of the present invention, determining the metabolic rate of the target subject based on the overall unit heat loss, the heart rate, the skin resistance, and the muscle rate includes:

[0018] determining a first value based on the product of the overall unit heat loss, the heart rate, and the muscle rate;

[0019] The metabolic rate is determined according to a quotient of the first value and the skin resistance.

[0020] Furthermore, in one embodiment of the present invention, the metabolic rate detection system includes a square wave-based skin resistance measurement module, which includes: a square wave generator, a resistance measurement unit, and at least two electrodes, wherein the electrodes are used to provide an electrical signal related to the skin resistance of the target subject. Acquiring skin resistance includes:

[0021] generating a square wave signal to periodically change the polarity of the electrode;

[0022] The resistance measuring unit collects the electrical signal based on the electrodes to determine the skin resistance.

[0023] On the other hand, an embodiment of the present invention provides a metabolic rate detection system, which measures metabolic rate using the above-mentioned metabolic rate detection method, the system comprising: at least one of a parameter acquisition module or a skin resistance measurement module, a local heat loss measurement module, and a control module;

[0024] The local heat loss measurement module is used to collect local heat loss parameters of the target object, and the parameter acquisition module is used to collect the heart rate or muscle rate of the target object; the skin resistance measurement module is used to collect the skin resistance of the target object;

[0025] The control module is used for:

[0026] determining a local surface heat loss according to the local heat loss parameter;

[0027] Determine the overall total heat loss and the overall unit heat loss based on the local surface heat loss and the convective heat loss coefficient of the target detection part; the convective heat loss coefficient is the ratio of the convective heat loss of the target detection part to the convective heat loss of all parts;

[0028] The metabolic rate of the target subject is determined based on the overall unit heat loss, the heart rate, the skin resistance, or the muscle rate.

[0029] Furthermore, in the metabolic rate detection system of an embodiment of the present invention, the local heat loss measurement module includes a microchannel, temperature and humidity detection units located at both ends of the microchannel, and a wind generator located at the entrance of the microchannel; the microchannel includes: a serpentine channel, a circular channel, or a straight channel;

[0030] The temperature and humidity detection unit is used to collect a first temperature and humidity at the channel entrance and a second temperature and humidity at the channel exit;

[0031] The control module is used to: determine the local surface heat loss according to the first temperature and humidity and the second temperature and humidity;

[0032] The metabolic rate detection system also includes a temperature detection module for collecting the surface temperature and ambient temperature of the local detection site; the control module is also used to determine the overall unit heat loss based on the surface temperature, the ambient temperature and the local surface heat loss.

[0033] Furthermore, in the metabolic rate detection system of the embodiment of the present invention, the skin resistance measurement module includes: a square wave generator, a resistance measurement unit and at least two electrodes;

[0034] The resistance measuring unit is connected to the electrode via the square wave generator, and the electrode is used to provide an electrical signal related to the skin resistance of the target object;

[0035] The control module is used for:

[0036] generating a square wave signal to periodically change the polarity of the electrode;

[0037] The resistance measuring unit collects the electrical signal based on the electrodes to determine the skin resistance.

[0038] Furthermore, in the metabolic rate detection system of an embodiment of the present invention, the microchannel is an acrylic channel, and the height of the acrylic channel is 1 to 5 mm and the length is 10 to 30 mm.

[0039] In another aspect, an embodiment of the present invention provides a metabolic rate detection device, comprising:

[0040] at least one processor;

[0041] at least one memory for storing at least one program;

[0042] When the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the above-mentioned metabolic rate detection method.

[0043] On the other hand, an embodiment of the present invention provides a storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned metabolic rate detection method.

[0044] On the other hand, an embodiment of the present invention provides a wearable sensor, including the above-mentioned metabolic rate detection system, which measures the metabolic rate using the above-mentioned metabolic rate detection method.

[0045] The method provided in an embodiment of the present invention is detected through a metabolic rate detection system, which includes a local heat loss measurement module; the method includes: collecting metabolic rate parameters of the target object; the metabolic rate parameters include heart rate, skin resistance, muscle rate or local heat loss parameters; the local heat loss parameters are used to characterize the measurement results of the target detection part of the target object based on the local heat loss measurement module; according to the local heat loss parameters, the local surface heat loss is determined; according to the local surface heat loss and the convective heat loss coefficient of the target detection part, the overall total heat loss and the overall unit heat loss are determined; the convective heat loss coefficient is the proportion of the convective heat loss of the target detection part to the convective heat loss of all parts; according to the overall unit heat loss, the heart rate, the skin resistance and the muscle rate, the metabolic rate of the target object is determined. The present application determines the overall unit heat loss through local surface heat loss, and then determines the metabolic rate based on the overall unit heat loss, which is conducive to improving the accuracy of metabolic rate measurement. At the same time, the detection method improved by the present application is simple and easy to implement, which is conducive to reducing the volume and cost of the corresponding detection system. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present invention or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0047] Figure 1 A schematic flow chart of an embodiment of the metabolic rate detection method provided by the present invention;

[0048] Figure 2 A schematic diagram of the optimization and calibration process of the local heat loss measurement module provided by the present invention;

[0049] Figure 3 A schematic structural diagram of an embodiment of a metabolic rate detection system provided by the present invention;

[0050] Figure 4 An exploded diagram of the structure of an embodiment of the metabolic rate detection system provided by the present invention;

[0051] Figure 5 A schematic diagram showing the principle of an embodiment of a skin resistance measurement module provided by the present invention;

[0052] Figure 6 Schematic diagram of the experimental setup based on the metabolic rate detection system provided by the present invention;

[0053] Figure 7 A schematic diagram of the experimental process based on the metabolic rate detection system provided by the present invention;

[0054] Figure 8 A comparison of heat loss over time for the three channel types provided for this application;

[0055] Figure 9 A comparison chart of heat loss and change rate under four sweat loss levels;

[0056] Figure 10 A comparison of the metabolic rates of the male group obtained by related techniques and this application at three ambient temperatures;

[0057] Figure 11 A comparison of metabolic rates of the female group obtained by related techniques and this application at three ambient temperatures;

[0058] Figure 12 This is an illustration of the universality results of the male group in the experiment provided in this application;

[0059] Figure 13 Schematic diagram of the universality results of the female group in the experiment provided in this application;

[0060] Figure 14 This is a comparison chart of the temperature measurement results of nine body parts of male subject 2 in the experiment provided in this application. DETAILED DESCRIPTION

[0061] The embodiments of the present invention are described in detail below, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention and are not to be construed as limiting the present invention. The step numbers in the following embodiments are provided for ease of explanation only and do not limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0062] Thermal comfort is crucial in all environments. Typically, six factors are involved in assessing thermal comfort, including four environmental parameters (air temperature, radiant temperature, wind speed, and humidity) and two personal parameters (metabolic rate and clothing insulation). Numerous studies have shown that thermal comfort significantly impacts workplace satisfaction and productivity. Furthermore, several studies have explored the relationship between thermal comfort and building energy consumption. For example, one embodiment can save approximately 17% of building energy consumption by raising people's thermal comfort level from "slightly cool" to "neutral," particularly during hot seasons when cooling is required. An interactive system can save at least 20% of energy without causing people to be dissatisfied with the thermal environment. Another embodiment proposes a new approach to balancing thermal comfort and building energy consumption. Other studies have indicated that personal parameters (such as metabolic rate and clothing insulation) play a key role in accurately determining thermal comfort, a finding supported by sensitivity analysis. Furthermore, metabolic rate is a crucial factor in determining the predicted mean vote (PMV), an evaluation metric used to characterize the human body's thermal response. In addition to PMV calculations, several attempts have demonstrated the importance of metabolic rate in developing thermal comfort-related models through heat balance.

[0063] There are many ways to measure the human metabolic rate. On the one hand, in general thermal comfort calculations, the metabolic rate is usually fixed using the ASHRAE chart and set as a default value based on the type of activity, without considering gender or individual differences. The main limitation of this method is its low accuracy because the most critical parameters are always set to default values. On the other hand, metabolic rate can also be obtained through actual measurement, and common methods include direct and indirect calorimetry. Indirect calorimetry measures metabolic rate by analyzing oxygen inhalation, carbon dioxide exhalation, and nitrogen balance during oxidation. This method requires professional instruments and masks to collect and analyze gas components during breathing. Commercial instruments such as BodyGem, Cosmed K4b2, Cosmed K5, Medgem TM The Vmax Encore metabolic cart typically uses indirect calorimetry. Direct calorimetry requires subjects to remain in a closed, insulated space for an extended period of time to measure heat loss. Unfortunately, while both methods are more accurate, they both have significant drawbacks. These include bulky, expensive equipment or the need for dedicated rooms, and the testing process being intrusive, time-consuming, non-portable, inconvenient, and location-specific. These are all well-known drawbacks.

[0064] Related technologies have proposed a variety of methods and models to express and evaluate human metabolic rate. Many studies have attempted to establish a connection between metabolic rate and indoor environmental factors such as air temperature, airflow, and indoor thermal conditions. In addition, studies have shown that some physiological parameters (including basal metabolic rate, heart rate, and maximum work capacity) can be directly incorporated into metabolic models. One embodiment optimizes the model from the perspective of heart rate and basal metabolic rate, and takes into account gender, age, and weight, although there is inevitably a standard deviation of 10% to 15%. In another embodiment, a data-driven method achieved an accuracy of more than 65% in predicting metabolic rate. These findings have enhanced the understanding of human metabolism from the perspective of model derivation.

[0065] With the development of wearable technology, some embodiments have identified the manufacture of wearable sensors for various purposes and applications that combine innovative concepts such as sweat assessment, energy storage, and human energy consumption. Some of these wearable sensors are very useful in assessing thermal comfort. For example, one embodiment develops a wearable sweat rate sensor that uses a miniature thermal pneumatic actuator to measure human thermal comfort. The armband of one embodiment uses a machine learning algorithm to estimate energy consumption using parameters such as heart rate, heat flow, and activity information, with an error rate of approximately 10%. The previous study of this application proposed a basic metabolic theory framework based on local unit heat loss, heart rate, skin resistance, and body muscle rate, which shows potential for improvement.

[0066] Metabolic rate is a core parameter for accurately assessing thermal comfort and building energy efficiency, but several challenges remain. Existing instruments for measuring metabolic rate, such as indirect and direct calorimetry, have significant drawbacks, including bulk, limited locations for use, high cost, lack of wearability, and interference with daily activities. Furthermore, despite the rapid development of wearable technology, there is a lack of basic research models and reliable methods for wearable measurement of metabolic rate.

[0067] The metabolic rate detection method, system, and wearable sensor proposed in embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0068] On the one hand, referring to Figure 1, a metabolic rate detection method is provided in an embodiment of the present invention. The metabolic rate detection method in the embodiment of the present invention can be applied to a terminal, a server, or software running in a terminal or a server. The terminal can be a tablet computer, a laptop computer, a desktop computer, etc., but is not limited thereto. The server can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms. The method provided in the present application is detected by a metabolic rate detection system, and the metabolic rate detection system includes a local heat loss measurement module; the method includes:

[0069] Step S100: collecting metabolic rate parameters of the target object; the metabolic rate parameters include heart rate, skin resistance, muscle rate or local heat loss parameters; the local heat loss parameters are used to characterize the measurement results of the target detection part of the target object based on the local heat loss measurement module;

[0070] Step S200: determining local surface heat loss according to local heat loss parameters;

[0071] Step S300: Determine the overall total heat loss and the overall unit heat loss based on the local surface heat loss and the convective heat loss coefficient of the target detection part; the convective heat loss coefficient is the ratio of the convective heat loss of the target detection part to the convective heat loss of all parts;

[0072] Step S400: Determine the metabolic rate of the target subject based on the overall unit heat loss, heart rate, skin resistance or muscle rate.

[0073] The proposed solution can detect metabolic rate based on at least one of heart rate, skin resistance, muscle rate, or a local heat loss parameter. For example, the metabolic rate can be determined based on the local heat loss parameter; the metabolic rate can also be determined based on the local heat loss parameter and heart rate; the metabolic rate can also be determined based on the local heat loss parameter, skin resistance, and heart rate; or the metabolic rate parameters of the target subject can be collected; the metabolic rate parameters can include heart rate, skin resistance, muscle rate, and a local heat loss parameter; and the metabolic rate of the target subject can be determined based on overall unit heat loss, heart rate, skin resistance, and muscle rate. Of course, the above are merely illustrative examples, and this application does not limit the number or specific selection of parameters. The muscle rate in this application can be the target subject's body muscle rate. The heart rate in this application can be obtained using any heart rate measurement device, and the muscle rate and skin resistance can also be obtained using corresponding detection devices. The local heat loss measurement module measures the target detection area of ​​the target subject, and the obtained local heat loss parameters include temperature and humidity data. In another embodiment, the heart rate, skin resistance, muscle rate, and local heat loss parameters in this application can also be obtained from a relevant database to determine the metabolic rate. It is understood that during the detection process, the local heat loss measurement module corresponds to the target detection area of ​​the target object. The parameters in this application can be measured in a contactless manner, resulting in local heat loss parameters. The local surface heat loss of the target detection area can then be obtained using the local heat loss parameters. It should be noted that the target detection area can be an easily detectable body part of the target object, such as the wrist, arm, or upper arm. This application does not limit the specific location of the target detection area. The convective heat loss coefficient in this application is used to convert local surface heat loss into overall total heat loss / total body heat loss, thereby improving the accuracy of the metabolic rate. The overall total heat loss in this application represents the target object's total body heat loss, and the overall unit heat loss represents the target object's unit body heat loss. The convective heat loss coefficient can be a predefined preset value, a preset value related to the target object, or a preset value related to the measurement scenario. In some embodiments, the convective heat loss coefficient can be a fixed preset value, or a preset value that varies based on relevant parameters of the target object and the measurement scenario. This application does not specifically limit the convective heat loss coefficient. In summary, the present application determines the metabolic rate through simple logical processing, which is beneficial to reducing the volume of the corresponding detection system and improving wearability.

[0074] Furthermore, in one embodiment of the present invention, the local heat loss measurement module includes a microchannel and temperature and humidity detection units located at both ends of the microchannel, wherein the microchannel includes: a serpentine channel, a circular channel, or a straight channel. The local surface heat loss is determined according to the local heat loss parameter, including:

[0075] Determine a first temperature and humidity at the channel entrance and a second temperature and humidity at the channel exit through a temperature and humidity detection unit;

[0076] The local surface heat loss is determined according to the first temperature and humidity and the second temperature and humidity.

[0077] The local heat loss measurement module in the present application includes a microchannel. It can be understood that the microchannel can be a serpentine channel, a straight channel, or a circular channel. The present application does not limit the specific type of microchannel. In one embodiment, taking the serpentine channel as an example, the local heat loss measurement module of the present application also includes a temperature and humidity detection unit located at the entrance of the serpentine channel and a temperature and humidity detection unit located at the exit of the serpentine channel, which collects the first temperature and humidity at the entrance of the channel and the second temperature and humidity at the exit of the channel. Then, the humidity ratio at the entrance and exit of the serpentine channel is determined, and the local surface heat loss that characterizes the skin surface in the channel is determined in combination with the water vapor partial pressure and volume flow at the entrance of the serpentine channel. The local surface heat loss in the present application is used to characterize the skin surface heat loss at the target detection site.

[0078] Furthermore, in one embodiment of the present invention, the overall total heat loss and the overall unit heat loss are determined based on the local surface heat loss and the convective heat loss coefficient of the target detection part, including:

[0079] Collect the surface temperature and ambient temperature of each detection part;

[0080] Determine the convective heat loss of each test location based on the surface temperature and ambient temperature;

[0081] Determine the convective heat loss coefficient based on the convective heat loss of each test location;

[0082] Determine the overall total heat loss based on the local surface heat loss and convection heat loss coefficient;

[0083] Determine the overall unit heat loss based on the overall total heat loss and the target object's body surface area.

[0084] In some embodiments, the convective heat loss coefficient in the present application can be obtained by measurement. Specifically, each time the metabolic rate is measured, the current corresponding convective heat loss coefficient is measured. First, the surface temperature and ambient temperature of each detection part are collected. Each detection part can be set according to actual needs. For example, it can be the head, chest, back, upper arm, lower arm, wrist, thigh, calf, and foot of the target object. Those skilled in the art can determine the detection part based on the average skin temperature. Secondly, based on the collected surface temperature and ambient temperature, the convective heat loss of each detection part is determined; and then the proportion of the target detection part in all detection parts is used as the convective heat loss coefficient. Then, the overall total heat loss is determined based on the quotient of the local surface heat loss and the convective heat loss coefficient. Finally, the overall unit heat loss is determined based on the quotient of the overall total heat loss and the body surface area of ​​the target object. It should be noted that the body surface area of ​​the target object can be determined by the height and weight of the target object.

[0085] In other embodiments, the convective heat loss coefficient can be determined through a large number of experiments for different target objects under different experimental environments, and then integrated into a first preset value. In this case, the convective heat loss coefficient is determined by obtaining the first preset value.

[0086] In other embodiments, the convective heat loss coefficient may be the convective heat loss coefficient corresponding to the current target subject. For each target subject, a second preset value corresponding to the convective heat loss coefficient is determined through a single measurement. In subsequent metabolic rate measurements of the target subject, the convective heat loss coefficient is determined by obtaining the second preset value.

[0087] Furthermore, in one embodiment of the present invention, determining the metabolic rate of the target subject based on the overall unit heat loss, the heart rate, the skin resistance, and the muscle rate includes:

[0088] determining a first value based on the product of the overall unit heat loss, the heart rate, and the muscle rate;

[0089] The metabolic rate is determined according to a quotient of the first value and the skin resistance.

[0090] In some embodiments, the metabolic rate of the target subject is determined by combining the overall unit heat loss, heart rate, skin resistance, and muscle rate using preset coefficients.

[0091] Furthermore, in one embodiment of the present invention, the metabolic rate detection system includes a square wave-based skin resistance measurement module, which includes: a square wave generator, a resistance measurement unit, and at least two electrodes, wherein the electrodes are used to provide an electrical signal related to the skin resistance of the target subject. Acquiring skin resistance includes:

[0092] generating a square wave signal to periodically change the polarity of the electrode;

[0093] The resistance measuring unit collects the electrical signal based on the electrodes to determine the skin resistance.

[0094] It should be noted that the square wave generator in this application can be implemented using a digital logic circuit, an analog circuit, a microcontroller, or a signal generator. Those skilled in the art can select an appropriate square wave generator based on actual needs, and this application does not limit the type of square wave generator. The electrodes in this application can be metal electrodes, gel electrodes, or electrodes made of other materials, and this application does not specifically limit the electrode material. The number of electrodes in this application is greater than or equal to two. In one embodiment, the square wave circuit is implemented using two metal electrodes. Specifically, the square wave signal is provided by the square wave circuit. It is understood that DC circuits in related art assume that the positive and negative poles of the metal electrodes remain unchanged throughout the test, e.g., the positive pole remains positive and the negative pole remains negative. During the test, this can easily lead to ion deposition at the detection site (e.g., the palm), resulting in biased measurement results. The square wave circuit introduced in this application uses a preset duration. The positive and negative poles of the metal electrodes are switched using a square wave signal. The positive and negative poles change according to a preset period throughout the test, thus eliminating ion deposition. Of course, this application does not limit the specific parameters of the square wave signal generated by the square wave circuit.

[0095] This application proposes a method for converting local unit heat loss to overall unit heat loss. This application proposes to design a heat dissipation system (i.e., a local heat loss measurement module) based on the local unit heat loss model, and proposes the most suitable serpentine acrylic channel. This application proposes a portable measurement method for human metabolism (characterization model, i.e., metabolic rate detection method), and for the first time uses the following parameters: heart rate, skin resistance, skin heat dissipation (i.e., overall unit heat loss) and muscle rate. This application designs and manufactures a portable metabolic sensor system (i.e., metabolic rate detection system) based on the model.

[0096] On the other hand, an embodiment of the present invention provides a metabolic rate detection system, which measures metabolic rate using the above-mentioned metabolic rate detection method, and the system includes: at least one of a parameter acquisition module or a skin resistance measurement module, a local heat loss measurement module, and a control module;

[0097] The local heat loss measurement module is used to collect the local heat loss parameters of the target object, and the parameter acquisition module is used to collect the heart rate or muscle rate of the target object; the skin resistance measurement module is used to collect the skin resistance of the target object;

[0098] The control module is used to:

[0099] Determine the local surface heat loss based on the local heat loss parameters;

[0100] Determine the overall total heat loss and overall unit heat loss based on the local surface heat loss and the convective heat loss coefficient of the target detection part; the convective heat loss coefficient is the ratio of the convective heat loss of the target detection part to the convective heat loss of all parts;

[0101] Determine the subject's metabolic rate based on overall unit heat loss, heart rate, skin resistance, or muscle rate.

[0102] Furthermore, in the metabolic rate detection system of an embodiment of the present invention, the local heat loss measurement module includes a microchannel, temperature and humidity detection units located at both ends of the microchannel, and a wind generator located at the entrance of the microchannel; the microchannel includes: a serpentine channel, a circular channel, or a straight channel;

[0103] The temperature and humidity detection unit is used to collect a first temperature and humidity at the channel entrance and a second temperature and humidity at the channel exit;

[0104] The control module is used to: determine the local surface heat loss according to the first temperature and humidity and the second temperature and humidity;

[0105] The metabolic rate detection system also includes a temperature detection module for collecting the surface temperature and ambient temperature of the local detection site; the control module is also used to determine the overall unit heat loss based on the surface temperature, the ambient temperature and the local surface heat loss.

[0106] It is understandable that the wind generator in the present application may be a micro fan, a micro air pump, a piezoelectric fan, an ion wind generator, etc. The present application does not limit the specific type of the wind generator.

[0107] Furthermore, in the metabolic rate detection system of the embodiment of the present invention, the skin resistance measurement module includes: a square wave generator, a resistance measurement unit and at least two electrodes;

[0108] The resistance measuring unit is connected to the electrode via the square wave generator, and the electrode is used to provide an electrical signal related to the skin resistance of the target object;

[0109] The control module is used for:

[0110] generating a square wave signal to periodically change the polarity of the electrode;

[0111] The resistance measuring unit collects the electrical signal based on the electrodes to determine the skin resistance.

[0112] Furthermore, in the metabolic rate detection system of the embodiment of the present invention, the microchannel is an acrylic channel, and the height of the acrylic channel is 1 to 5 mm and the length is 10 to 30 mm.

[0113] It can be seen that the contents of the above method embodiments are all applicable to the present system embodiments. The functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0114] The following is a detailed description of the metabolic rate detection method and detection system provided by the present application using a specific embodiment:

[0115] Accurately assessing metabolic rate is crucial for predicting human thermal comfort. However, existing instruments have limitations such as being bulky, invasive, and inconvenient to use. In addition, wearable metabolic rate measurement has long been challenging due to the lack of a comprehensive theoretical model based on easily measurable parameters. This application optimizes the theoretical method for wearable metabolic rate measurement proposed in previous studies, which combines heart rate, whole-body unit heat loss, skin resistance, and body muscle percentage. This application improves the method for evaluating whole-body unit heat loss by introducing a newly proposed convective heat loss coefficient for each body part. In terms of wearable sensor design, after calibration experimental verification, the heat loss measurement system with a serpentine channel was proven to be the most suitable design. This application also integrates an improved square wave-based skin resistance design, thereby realizing an upgraded version of the integrated metabolic sensor.

[0116] In order to verify the performance of the sensor and model, the present application conducted experiments on ten male and female subjects under three temperature conditions (22, 25 and 28°C) and four activity levels (resting, tiptoeing, light walking and moderate walking). First, the results of the present application showed that the metabolic rate measured by the optimized sensor was strongly positively correlated with the results measured by the control instrument, such as the Quark CPET instrument, and the determination coefficient was high (the highest was 0.97 in the male group and the highest was 0.98 in the female group). The method of the present application achieved an accuracy of at least 96% and an uncertainty of less than 2.5% in both groups. Secondly, the present application found that when the subject was fixed, only one metabolic rate model was required to apply to the combined temperature conditions. Third, this positive correlation was further improved by combining the whole body unit heat loss. Finally, the present application also obtained valuable information about skin temperature.

[0117] In summary, this application provides a powerful method for predicting and evaluating human metabolic rate through an innovative wearable approach. The method and sensor of this application are expected to contribute to energy conservation in smart buildings.

[0118] This application first aims to establish and optimize a previously developed metabolic rate model with higher accuracy. This innovative model combines easily measurable parameters such as heart rate, whole-body unit heat loss, skin resistance, and muscle percentage. The second goal of this application is to achieve portable and accurate measurement of metabolic rate while verifying the theory. In order to verify the model and metabolic rate sensor of this application, this application uses the gold standard of indirect calorimetry for sensor calibration. This is the first attempt of this application to achieve portable measurement of human metabolic rate and propose a simple model based on physiological parameters. This application further improves the previously proposed local human heat loss model by introducing the convective heat transfer coefficient of whole-body heat dissipation, thereby improving performance. In addition, the results of this application show that the universal model can be applied to individuals through personalized coefficients, demonstrating the great potential of wearable metabolic rate measurement.

[0119] Accurate and portable metabolic rate measurement can greatly promote the precise assessment and improvement of human thermal comfort, as well as improve work efficiency. The metabolic rate detection method and system provided in this application are introduced in detail in five parts. The first part briefly introduces the research background and related wearable sensors. The second part includes the design of the physical model and metabolic wearable sensor. The third part introduces the experimental setup, procedures and volunteer information. This application also reports heat loss experiments and evaluates the performance of the proposed model based on the results. The last part summarizes the conclusions.

[0120] 1 Research background, see the above introduction to related technologies.

[0121] 2 The second part of this application is the physical model development and sensor system design.

[0122] 2.1 Physical model

[0123] 2.1.1 Heart rate (HR).

[0124] According to ASHRAE standards, metabolic rate, measured in "Met," is the process of consuming oxygen for energy. Because the human body cannot store large amounts of oxygen, it continuously absorbs oxygen from the atmosphere during aerobic respiration. The body's oxygen consumption is determined by metabolic activity, and metabolic rate is accurately determined by the amount of oxygen dissipated, as shown in Equation (1).

[0125]

[0126] Where M is the human metabolic rate (unit is Met or W / m 2 ) ; TE is the thermal equivalent of oxygen (unit is J / L); A Du is the surface area of ​​human skin (unit: m 2 ).

[0127] Further understanding of oxygen consumption can be obtained by applying the Fick equation to equation (2).

[0128]

[0129] Where HR is heart rate (beats / min); SV is stroke volume (mL / beat); is the arteriovenous oxygen difference, which represents the difference in oxygen levels between arterial and mixed venous blood (mL / dL). Heart rate has a greater impact on oxygen consumption than the other two parameters. Substituting formula (2) into formula (1), it is clear that there is a strong positive correlation between metabolic rate and heart rate, as shown in formula (3):

[0130]

[0131] When the respiratory quotient is fixed, TE is a constant and A Du It is also a fixed value for a particular subject. Heart rate has a significant effect on metabolic outcomes.

[0132] 2.1.2 Skin resistance (R).

[0133] During metabolism, oxygen and nutrients are supplied to body tissues, leading to a redistribution of electrolytes and, consequently, changes in the dielectric properties of the skin. This also results in changes in skin electrical resistance. Furthermore, changes in sweat gland activity during different activities can be attributed to vasoconstriction and dilation. These processes are determined by the sympathetic nervous system and are not controlled by the individual. Based on the above analysis, skin electrical resistance, as an objective indicator of the skin surface, should be incorporated into the derivation of metabolic models, as it suggests certain relationships.

[0134] The measurement method based on the DC circuit, because one electrode is always connected to the positive pole of the power supply and the other electrode is kept at the negative pole, it experiences data changes caused by ion polarization. To solve this problem, in this application, the two electrodes switch polarity once at a preset frequency through a square wave circuit. This application uses the skin resistance measurement results of the DC circuit method in the previous study and the updated square wave circuit of this application to calculate the resistance change rate (R / R max The results showed that the minimum value of the skin resistance change rate increased from approximately 0.6 to 0.95 after using the square wave circuit. This method greatly improved the stability of the resistance test.

[0135] 2.1.3 Whole body unit heat loss (HL total )

[0136] The body continuously produces heat as a result of metabolic processes. Once generated, heat is transferred to the skin through tissue and blood perfusion. To maintain thermal balance in the body, the generated heat must be dissipated. Multiple processes occur simultaneously in the body to promote heat loss, including sweating, respiration, vasoconstriction, and vasodilation. Of these processes, the combination of radiation, convection, and evaporation contributes most to heat loss, while respiration accounts for only a small proportion. One study reported that heat loss increases from approximately 80W to 1400W during physical activity. Therefore, in this application, determining heat loss from the skin surface throughout the body is an important component of determining metabolic rate.

[0137] Previously, this application considered heat loss and measured the value of the skin surface. Based on the two conservation equations of dry air and water vapor (mass conservation and energy conservation), this application established the following skin heat loss model:

[0138]

[0139] in, is the heat loss from the skin surface in the channel (unit: kJ); P v,in is the partial pressure of water vapor at the channel inlet (unit: Pa); is the volume flow rate at the inlet (unit: m 3 / s); T in and T out is the temperature at the inlet and outlet (unit: °C); W in and W out is the channel humidity ratio at the inlet and outlet (dimensionless); A cover is the projected area of ​​the channel on the skin (unit: m 2 );HL wrist Indicates the unit heat loss of the skin surface (unit: W / m 2 ).

[0140] Subsequently, this application applies the above heat loss model to the metabolic rate model. It should be noted that the previous study assumed that the heat loss value of the whole body is the same. Therefore, in the previous study, the local heat loss HL at the wrist was used. wrist to represent the overall unit heat loss.

[0141] However, different body parts perceive and dissipate heat differently. Considering only local unit heat loss as overall unit heat loss is inaccurate. Therefore, this application proposes an innovative method to optimize the whole-body unit heat loss model based on the previous local heat loss model (Equation 6).

[0142]

[0143] in, is the total body heat loss (unit: kJ), which is calculated by dividing the heat loss in the skin surface channel ( That is, the local surface heat loss in this application) divided by the wrist convection heat loss coefficient α wrist (i.e., the convective heat loss coefficient of the target detection part in this application) is calculated; (dimensionless). Of course, it is understood that formula (6) gives the target detection part as the wrist, and those skilled in the art can define the target detection part as other easily measurable body parts as needed, and can also calculate the metabolic rate. This application does not limit the specific location information of the target detection part.

[0144] Based on the above ideas, this application introduces the heat loss coefficients of multiple body parts and uses Equation (7) to calculate the convective heat loss of each part. Nine body parts are considered: head (x1), right wrist (x2), left chest (x3), back (x4), forearm (x5), thigh (x6), calf (x7), foot (x8), and lower upper arm (x9).

[0145] By substituting the skin temperatures of the nine parts into formula (7), the convective heat loss of each part can be obtained ( to ).

[0146]

[0147] in, is the convective heat loss of the nth body part (unit: kJ), where n is the ranking number of the above nine parts; is the temperature corresponding to the nth point / part (unit: °C); T chamber is the experimental environment temperature (unit: °C).

[0148] Then, by taking the convective heat loss value ( to ) into formula (8) to calculate the heat loss ratio of the nine body parts.

[0149]

[0150] Finally, by calculating the wrist convective heat loss coefficient Substitute into equation (6) to determine the total body heat loss Then, the whole body unit heat loss is calculated by formula (9).

[0151]

[0152] Among them, HL total is the unit heat loss of the whole body (unit: kJ); A body surface is the body surface area, which can be calculated using the DuBois method as follows:

[0153] A body surface =0.007184×Weight(kg) 0.425 ×Height(cm) 0.725 (10)

[0155] 2.1.4 Body muscle ratio (BMR).

[0156] More and more studies have found a relationship between metabolic rate and body muscle ratio. One embodiment points out that skeletal muscle plays a key role in metabolic rate and is an important indicator of metabolic rate. One embodiment reports the correlation between metabolic rate and skeletal muscle mass, especially when at rest. As people exercise, metabolism becomes more intense, requiring increased ATP (adenosine triphosphate) levels and synthesis. In addition, body muscle ratio is often used to describe brown adipose tissue, which plays an important role in heat production, indicating that muscle oxidative capacity can make a difference in heat generation.

[0157] 2.1.5 Skin temperature.

[0158] Studies have shown that there is no strong correlation between skin temperature and metabolic rate. Resting metabolic rate cannot be reliably predicted by either core or skin temperature, although skin temperature provides some clues. In this application, skin temperature is used to calculate convective heat loss, as described in Section 2.1.3. Although skin temperature is not directly incorporated into the model proposed in this application, its impact is accounted for in the heat loss calculations. Throughout the experiment, skin temperature was acquired at nine body sites using a thermocouple and recording system at a sampling rate of three times per minute.

[0159] 2.1.6 Optimized metabolic rate mathematical model.

[0160] Based on the above scientific derivation and analysis, this application optimizes the metabolic rate model of the previous study by considering variables such as heart rate, whole-body unit heat loss, skin resistance, and body muscle ratio, as shown in Equations (11) and (12).

[0161]

[0162] Among them, M model is the metabolic rate (unit: Met) measured by the sensor provided in this application; HL total is the total body heat loss (unit: kJ); HR is the heart rate (unit: beats / min); R is the skin resistance; BMR is the body muscle ratio (i.e. muscle rate, unit: %).

[0163] Local heat dissipation at the wrist Temperature and humidity sensors (at the channel entrance and exit, 2 sets in total, which need to be calculated according to formula (4) after acquiring data); skin whole body unit heat loss (HL total ): calculated according to formulas (5)-(9) after the temperature of different points on the body is measured by a thermocouple; human muscle rate (BMRa): measured using the bioelectric impedance analysis (BIA) method (exemplarily, using an Inbody scale); the constant (α) with units is the preset coefficient in this application: the units on both sides of the balance formula, exemplarily, can be 0.286 ohm / beats; personalized constant terms (α, β): different values ​​for different volunteers, and the specific values ​​need to be obtained after conducting experiments simultaneously with the metabolic meter.

[0164] The optimized metabolic rate model proposed in this application (as shown in Equation 11 or 12) takes into account the positive linear relationship between heart rate and metabolic rate. In addition, as exercise intensity increases, skin resistance decreases due to active sweat glands. This application also improves the heat loss assessment method by introducing an innovative whole-body unit heat loss model. This newly optimized metabolic rate model uses total heat loss instead of local heat loss, providing a more accurate, scientific, and comprehensive assessment method.

[0165] 2.2 Design of heat loss measurement system.

[0166] Reference Figure 2 For the optimization and calibration of the heat loss measurement system, a is the design of the heat loss measurement system, b is three different channel designs (straight, serpentine and circular), and c is the heat loss simulation experiment setup of the three design channels. The heat loss measurement system (i.e. the local heat loss measurement module in this application) is as follows Figure 2 As shown in a, it contains a small fan, an acrylic channel and two temperature and humidity sensors (i.e., the temperature and humidity detection unit in this application). The two sensors are installed downward at the inlet and outlet of the channel to measure the air temperature and humidity. In order to find the type of channel that is most suitable for skin heat transfer, a calibration experiment was designed. During the test, the entire system was placed on the surface of artificial skin. Three shapes of channels were designed: circular, serpentine, and straight, as shown in Figure 1. Figure 2 As shown in b, each shape of channel is designed with a length of 20mm and 30mm. The channel height is 3mm, which is 25% less than the channel height used in the previous study. The height of the channel is the total thickness of the acrylic of the heat dissipation system (top cover 1mm + channel 2mm). The height of the temperature and humidity sensor is reduced by about ten times compared with the previous version. The heat loss measurement research method of this application does not exclude skin evaporation, convection and radiation. The performance of the system should be evaluated using these three shapes of channels. Therefore, the calibration experimental setup (such as Figure 2(as shown in Figure c) three heat loss measurement systems are placed on an artificial skin with a porous structure simulating sweat glands. In order to simulate the increased intensity of activity, the artificial sweat is heated to higher temperatures of 25, 40, 50 and 70°C by a hot plate. Each temperature is maintained for at least 15 minutes to ensure that a stable sweating state is achieved. The unit weight loss can be calculated by the balance display before and after heating. At the same time, the unit wrist heat loss is obtained by collecting data from the heat loss measurement system. The results are shown in Section 4.1 of this application. It should be noted that the serpentine channel in this application is a channel with a curved shape and is not limited to Figure 2 The channel shape shown. Compared with circular channels and straight channels, serpentine channels are more suitable for calculating unobstructed heat dissipation on the skin surface.

[0167] 2.3 Design of wearable sensor for human metabolic rate.

[0168] Figure 3 This is the design of a metabolic sensor (i.e., the metabolic rate detection system in this application), where a is a front view, b is a side view, c is a top view, and d is a bottom view. Figure 4 The exploded view of the metabolic sensor (from top to bottom). After selecting the most suitable heat loss measurement system, the entire metabolic rate sensor is integrated, such as Figure 3 and Figure 4 As shown, they are schematic diagrams and exploded diagrams from different perspectives. The size of the entire metabolic rate wearable sensor is 19mm×62mm×46mm, and it consists of four main parts and an acrylic bracket (thickness: 2mm): skin resistance measurement part (skin resistance measurement module); micro control unit (control module, MCU); local heat loss measurement part (local heat loss measurement module); heart rate measurement part. The skin resistance measurement part uses an optimized square wave circuit to eliminate the influence of ion polarization caused by continuous sweating, thereby achieving more accurate measurement. For example, refer to Figure 5 As shown, the skin resistance measurement part includes two metal electrodes, a resistance DC chip (i.e., the resistance measurement unit in this application), a square wave circuit including two relays, and a signal control unit. The local heat loss measurement part includes a serpentine channel, a small fan, and two temperature and humidity detectors installed at the entrance and exit of the channel. The heart rate chip uses the PPG method to obtain real-time heart rate. Almost all chips are driven by MCU, using esp8266 12f chip, and the code is burned through Arduino IDE. The integrated metabolic sensor can obtain local heat loss, real-time heart rate every five seconds, and skin resistance every second. All data are transmitted to a laptop for further processing. The detailed layout of the entire sensor is shown in four directions. Figure 3 In the , while the exploded view, wiring connection and actual wearing pictures are in Figure 4Metabolic sensors are portable, convenient, miniaturized, non-invasive, and capable of real-time measurement. The integrated metabolic rate sensor of this application will be used in subsequent experiments to collect data together with the CosmedQuark CPET (control experiment).

[0169] Specifically, the square wave-based skin resistance measurement system consists of a DC resistance measurement module and a square wave switching module that works with two relays. The DC resistance measurement module is directly connected to the MCU and can communicate with the MCU via the ADC (analog to digital conversion) using the A0 pin. The two relays are controlled by an additional Arduino Uno for high and low levels. Figure 5 The working principle of square wave resistance measurement: By default, NC and COM are connected, so the left electrode is positively charged and the right electrode is negatively charged, corresponding to the high level moment of the relay. When the signal control unit sends a signal to disconnect NC and COM, NO contacts COM at this moment, corresponding to the low level moment of the relay. In this case, the left electrode is negatively charged and the right electrode is positively charged. In this way, the positive and negative charges of the two electrodes are switched, which avoids the fixed deposition of anions and cations under a certain electrode, affecting the test results. It should be noted that Figure 5 The square wave generator shown is realized by two relays, which is an illustrative example and does not serve as a specific limitation of this solution. In some embodiments, the positive and negative poles are switched once every 1 second. Figure 2 The heart rate module is located on the nylon strap and communicates with the MCU via I2C.

[0170] The local heat loss measurement section includes two temperature and humidity sensors, a microfan, and an acrylic serpentine channel. The microfan is powered by an MCU, and the temperature and humidity sensors use a single-bus protocol for data transmission. The energy conservation and mass conservation laws within the channel are used to establish relationships. Air energy conservation: Energy delivered at the inlet + heat dissipated through the skin = Energy at the outlet (calculated as enthalpy). Mass conservation: 1) The dry air mass flow rate at the inlet is equal to the dry air mass flow rate at the outlet; 2) The humid air at the inlet + the humid air inside the channel = the humid air at the outlet.

[0171] The MCU outputs temperature, humidity, and heart rate data to the computer through the serial port every 5 seconds, and outputs skin resistance data every 1 second. Of course, the above is an illustrative example, and this application does not limit the frequency of data collection and output.

[0172] 3. Experiments provided in the third part of this application:

[0173] 3.1 Experimental equipment and experimental conditions setting.

[0174] Table 1 lists the equipment used in the experiment. The design and setup of this experiment have completed safety and human research ethics assessments. The experiment was conducted in the Hong Kong Jockey Club Controlled Environment Laboratory, which maintained the required indoor temperature, humidity, and air flow rate throughout the study. A thermocouple panel was installed in the environmental chamber to measure human body temperature three times per minute. To calibrate and evaluate the performance of the metabolic rate sensor designed by this application, this application used a Cosmed Quark CPET device (i.e., a control group experiment using the gold standard method). The wearable metabolic device developed by this application was used to collect local heat loss from the wrist, heart rate data every 5 seconds, and skin resistance data every second. In addition, the InBody Dial H20N device was used to obtain skeletal muscle percentage data. Data acquisition, display, and rhythm control were completed by two computers and an iPad, respectively.

[0175]

[0176]

[0177] Table 1

[0178] 3.2 Experimental setup and data collection process.

[0179] Figure 6 The experimental setup of the metabolic rate sensor developed in this application is demonstrated. Before the test, all participants were asked to avoid drinking caffeinated beverages. The subjects underwent three tests under three different temperature conditions (for example, 22°C, 25°C and 28°C, with humidity and air flow rate remaining unchanged). First, nine thermocouples were attached to specific body surfaces, including the head, left chest, left back, left forearm, right wrist, left thigh, left calf, left foot and lower upper arm, to measure metabolic rate using the system provided by this application. Subsequently, a blue mask was worn to collect metabolic rate data through breathing to measure metabolic rate using the gold standard method (Quark CPET, control experiment). Next, the wearable sensor designed in this application was placed on the left wrist. After a 20-minute adaptation period, data collection began. The formal experiment consisted of four stages: resting, tiptoeing, light walking, and moderate walking, such as Figure 7 Each phase lasted 10 minutes. To ensure movement synchronization, a fixed-frequency metronome was used, allowing participants to move to the rhythm during the last three phases. The above process was repeated at 22°C, 25°C, and 28°C, with participants wearing identical clothing (approximately 0.38 clo). Each participant required at least three hours of total training. Ten volunteers were recruited for the experiment, half of whom were male. Detailed information about the volunteers is provided in Table 2.

[0180]

[0181] Table 2

[0182] 4. Experimental results and discussion provided in Section 4 of this application:

[0183] 4.1 Local skin heat loss measurement results.

[0184] To determine the most effective type of local heat loss measurement system, this application conducted a quantitative analysis based on heat loss calibration results. First, the change in heat loss over time at different temperatures was calculated. Furthermore, the application further established the relationship between unit sweat loss, heat loss, and rate of change. Finally, the type of optical channel was determined and incorporated into the design of a wearable metabolic rate sensor.

[0185] 4.1.1 Variation of heat loss over time for the three channel types.

[0186] Figure 8 The heat loss of the three channel types was compared over time using the calibration experiments of the three different heat loss measurement systems mentioned above. Figure 8 In the figure, a is a straight channel, b is a serpentine channel, and c is a circular channel (solid points represent the results of a 3 cm channel, and hollow points represent the results of a 2 cm channel). It can be seen that for the straight channel and serpentine channel systems, as the heating temperature increases, the heat loss increases, indicating that the sweating process of the two channel lengths is more intense. In comparison, the circular channel system has the worst performance. Figure 8 As can be seen in Figure b, the 3cm serpentine channel performs more stably at 75°C, especially in terms of phase differentiation and data stability. On the other hand, for the straight channel heat loss measurement system, the 2cm channel performs better.

[0187] 4.1.2 Channel selection results.

[0188] Figure 9 are the heat loss and change rate results under four sweat loss levels, where Figure 9 In the figure, a is a straight channel and b is a serpentine channel (the left axis is a bar graph and the right axis is a line graph). The above results show that the 3cm serpentine channel and the 2cm straight channel are suitable for measuring heat loss at different temperatures. This application further analyzes the relationship between the unit heat loss and unit sweat loss of these two channels. The results are as follows: Figure 9 As shown. At each heating temperature stage, the sweat loss (grams) was calculated by measuring the weight loss before and after the stage, thereby determining the unit sweat loss. At the same time, the heat loss per unit area was obtained using the heat loss measurement model of the present application (by measuring the temperature and humidity at the channel inlet and outlet (Formula (4))). Figure 9It shows that both the straight channel and the serpentine channel show an increasing trend during more intense sweating. To determine the optimal channel, this application calculated the rate of change in each stage, such as Figure 9 As shown in the line graph, the serpentine channel shows a significantly higher rate of change than the straight channel. This means that, for the same unit of sweat loss, the serpentine channel system is more sensitive than the straight channel system. Based on these findings, this application selected a 3cm serpentine channel for sensor design. This channel exhibited a more stable and significant rate of change across the four heating stages and had higher sensitivity than the other types.

[0189] 4.2 Optimize metabolic rate model results.

[0190] The primary objective of this experiment is to verify the agreement between the proposed optimized metabolic model and instrumentally measured metabolic rates. This section demonstrates the model's performance for both male and female subjects, as well as under mixed temperature conditions. Furthermore, the model's performance is compared with local and global heat losses. Finally, the accuracy and uncertainty of the proposed method are discussed.

[0191] 4.2.1 Results of the developed sensor and model at various experimental temperatures.

[0192] Figure 10 Comparison of the metabolic rate measured for the male group at three ambient temperatures (y-axis) with the results of the proposed metabolic rate model (x-axis); Figure 11 Comparison of the metabolic rate (y-axis) measured for the female group at three ambient temperatures with the results of the proposed metabolic rate model (x-axis). sensor : The measurement result of the metabolic rate sensor provided by this application, M measured : Metabolic rate results by QuarkCPET.

[0193] Specifically, according to the experimental process, this application collected metabolic rate data from both Quark CPET and the sensor provided by this application through a face mask. Quark CPET was used to calibrate the sensor designed by this application and served as a control experiment. Overall, metabolic rate gradually increased during the four aforementioned activity stages, and oxygen consumption also increased accordingly. Figure 10 and Figure 11 Correlations between metabolic rates obtained by the Quark CPET instrument and wearable sensors are shown for male and female groups, respectively. The optimized metabolic rate model showed a consistent upward trend, consistent with the gold standard measurement method. In addition, a significant positive linear relationship was observed under all experimental conditions, with a coefficient of determination (R 2) ranged from 0 to 3.5 Met. This demonstrates that the optimized model accurately estimates metabolic rate. Specifically, the lowest and highest coefficients of determination were 0.909 and 0.9693 for the male group, and 0.9009 and 0.9768 for the female group. The optimized model performed well in predicting metabolic rate at all three temperatures.

[0194] 4.2.2 Sensor and model results at mixed experimental temperatures.

[0195] Figure 12 These are generalizable results for the male group. Figure 13 These are generalizable results for the female group. Figure 13 a1 is the result of female subject 2 before thermal fusion, a2 is the result of female subject 2 after thermal fusion, b1 is the result of female subject 5 before thermal fusion, b2 is the result of female subject 5 after thermal fusion

[0196] In further analysis, this application used data from the same participants under different temperature conditions. Figure 12 a1, b1 and Figure 13 a1 and b1 show the results of each participant under three temperature conditions, and Figure 12 a2, b2 and Figure 13 a2 and b2 in the figure summarize the data points under all experimental conditions. It can be understood that after using the moving average method, there is still a positive linear relationship under the three mixed temperature conditions, and the coefficient of determination (R 2 ) is at least approximately 0.9. These results demonstrate that the universal metabolic rate model with personalized coefficients remains valid for the same individual regardless of temperature. In other words, the model proposed in this application is consistent for the same individual across the three temperature conditions. These findings are expected to simplify the complexity of the model.

[0197] 4.2.3 Comparison of local and global heat loss in the metabolic rate model.

[0198] Previous studies have proposed a metabolic rate model that includes local heat loss parameters. This application introduces the concept of convective heat loss coefficients for different body parts, thereby converting local heat loss into whole-body heat loss. By replacing local unit heat loss with whole-body unit heat loss, a more scientific calculation is ensured. In the test, this application used a heat loss measurement system with a serpentine channel. Table 3 shows the quantitative results of the metabolic model under local and whole-body unit heat loss (mainly R 2). Although both calculation methods showed high coefficients of determination in both male and female groups, the positive correlation was further improved when using whole-body unit heat loss, especially in the male group. It is worth noting that the results of all male subjects showed an improvement in the coefficient of determination when using whole-body unit heat loss. Specifically, the highest coefficient of determination in the male group increased from 0.91 to 0.95. In the female group, most results were also improved by using the whole-body unit heat loss method, but the improvement was relatively small because the change in heat loss across the four activity phases in women was not as significant as that in men. In summary, the whole-body unit heat loss calculation method is significantly superior to the local unit heat loss method.

[0199]

[0200] Table 34.2.4 Uncertainty and accuracy analysis of the optimization model.

[0201]

[0202] Table 4

[0203] To further illustrate the performance of the sensor and the proposed model in metabolic rate assessment, this application conducted an accuracy analysis and uncertainty assessment. Table 4 describes the uncertainty comparison of the sensor and the Quark CPET instrument results at a 95% confidence interval (CI). Obviously, the uncertainty was less than 2.5% for both the female and male groups in four stages and three ambient temperatures. In addition, the accuracy calculation results of the measurement using the sensor provided by this application showed that it achieved an accuracy of over 96% in metabolic assessment, with 96.5% for the male group and 96.81% for the female group. In comparison, the accuracy of the SenseWear armband of one embodiment at the resting state level was 86.95%. In addition, one embodiment mentioned that the standard deviation of metabolic rate estimation using heart rate and basal metabolic rate was 10% to 15%. It is reported that the error of indirect calorimetry in metabolic rate measurement is less than 1%. It is worth noting that the method proposed in this application, combined with the designed wearable sensor, achieved an accuracy of at least 96%, which is an excellent performance. In summary, the integrated sensor and the improved whole-body unit heat loss model demonstrated excellent repeatability and reliability.

[0204] 4.3 Changes in body temperature under different metabolic activities and temperatures.

[0205] By taking into account the skin temperature at the corresponding location, the convective heat loss of each body part was determined. Furthermore, the application analyzed the changes in skin temperature at each body part under different experimental conditions. Based on these findings, the application determined the areas of the body that are most sensitive to changes in the environment and activity in both men and women.

[0206] 4.3.1 Body temperature changes under experimental conditions.

[0207] While body surface temperature may not be significantly related to metabolic rate, they do show certain patterns across activity levels and ambient temperatures. Figure 14 The results show temperature measurements of nine body parts for male subject 2 during four activities at three different temperatures. The results show that, regardless of activity level, all body surface temperatures increased as the ambient temperature increased from 22°C to 28°C. In colder environments, vasoconstriction leads to a decrease in skin surface temperature, a response that helps reduce heat loss and maintain core body temperature. Conversely, in warmer environments, the body needs to dissipate excess heat through vasodilation, increasing blood flow and raising skin temperature. However, the results did not reveal significant temperature differences between the tested body parts. Furthermore, temperatures of the forehead, trunk, and extremities showed a decreasing trend. These findings were consistent across the other eight participants.

[0208] 4.3.2 Results of sensitive temperature indicators for different body parts.

[0209] In addition to analyzing individual body temperature measurements, the authors further analyzed the top three sensitive indicators for subjects of the same sex at different temperatures and activity levels. The results revealed that the feet, calves, and hands were the three areas of the body where skin temperature increased in response to both environmental and activity intensity changes. Overall, the extremities are more sensitive to environmental perception and internal heat generation. These findings provide scientific support for the common practice of measuring wrist temperature during exercise. Besides being convenient for measurement, changes in wrist temperature can also provide valuable information. Similar results were observed in the male group.

[0210] 5. Conclusions provided in Section 5 of this application:

[0211] Previous studies have demonstrated the importance of accurately assessing metabolic rate to determine thermal comfort. However, existing measurement instruments are invasive, non-portable, and bulky, interfering with daily activities.

[0212] This application proposes a wearable metabolic rate sensor that is miniaturized, integrated, and capable of real-time measurement while providing comfort for the wearer. Furthermore, the theoretical metabolic rate model is optimized by replacing local unit heat loss with whole-body unit heat loss. The proposed method improves the metabolic rate assessment model by incorporating parameters such as heart rate, electrical resistance, and body muscle percentage.

[0213] This application improves the calculation method of whole-body unit heat loss without blocking skin evaporation and convection. Based on the above-mentioned heat loss improvement method, this application designed three measurement channels. In the calibration experiment, the serpentine channel performed best in terms of test range and sensitivity. This application further optimized the metabolic rate model through improvements in heat loss calculation. In addition, the wearable sensor was improved using a newly designed heat loss measurement system and a square wave-based skin resistance chip. The evidence in this application shows that the metabolic rate model was verified for both male and female groups under three ambient temperature conditions (0 to 3.5Met). The metabolic rate values ​​measured by Quark CPET and the sensor designed in this application were positively correlated, and the determination coefficient was high (R 2 >0.9). In addition, this application successfully evaluated the human metabolic rate using a simplified model and corresponding wearable sensors. This application showed that the correlation was further improved after using whole-body unit heat loss, especially in the male group (male R 2 =0.97, female R 2 =0.98). The reason is that the difference in heat loss at different stages is more obvious for males than for females. This application proposes that as long as the subjects are fixed, the same model can be applied even to different environmental conditions (22, 25 and 28°C) (male group R 2 >0.92, female group R 2 >0.87). These results expand our understanding of the body's autoregulatory mechanisms. Our model offers significant advantages, employing a universal formula and varying coefficients for different subjects. The experimental uncertainty is less than 2.5%, with an accuracy of 96.5% for men and 96.81% for women. We propose that during changes in ambient temperature and activity, the three most sensitive areas are the feet, calves, and hands, as indicated by three skin temperature indicators.

[0214] In summary, this application improves the metabolic rate model by replacing local heat loss with a newly proposed whole-body unit heat loss. Combined with updated wearable sensors, this method enables more accurate and scientific measurements. This user-friendly, convenient, and non-intrusive measurement method opens the possibility of future wearable metabolic rate measurements, thereby enabling accurate and personalized thermal comfort measurements. Further research is needed to assess age differences in metabolic rate and explore the potential for detecting metabolic disorders.

[0215] In another aspect, an embodiment of the present invention provides a metabolic rate detection device, comprising:

[0216] at least one processor;

[0217] at least one memory for storing at least one program;

[0218] When the at least one program is executed by the at least one processor, the at least one processor is enabled to implement the above-mentioned metabolic rate detection method.

[0219] Similarly, the contents of the above method embodiments are applicable to the present device embodiments. The functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0220] On the other hand, an embodiment of the present invention provides a storage medium storing a program executable by a processor. When the program is executed by the processor, it is used to implement the above-mentioned metabolic rate detection method.

[0221] Similarly, the contents of the above method embodiments are applicable to the present storage medium embodiment. The functions specifically implemented by the present storage medium embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0222] On the other hand, an embodiment of the present invention provides a wearable sensor, including the above-mentioned metabolic rate detection system, which measures the metabolic rate using the above-mentioned metabolic rate detection method.

[0223] Similarly, the contents of the above method embodiments are applicable to the present sensor embodiment. The functions specifically implemented by the present sensor embodiment are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0224] In some optional embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the present invention is provided in an exemplary manner for the purpose of providing a more comprehensive understanding of the technology. The disclosed method is not limited to the operation and logic flow presented herein. Optional embodiments are contemplated in which the order of the various operations is changed and the sub-operations described as a part of a larger operation are performed independently.

[0225] In addition, although the present invention is described in the context of functional modules, it should be understood that, unless otherwise stated, one or more of the functions and / or features may be integrated into a single physical device and / or software module, or one or more functions and / or features may be implemented in separate physical devices or software modules. It is also understood that a detailed discussion of the actual implementation of each module is not necessary for understanding the present invention. More specifically, given the properties, functions, and internal relationships of the various functional modules in the devices disclosed herein, the actual implementation of the module will be understood within the ordinary skill of an engineer. Therefore, a person skilled in the art will be able to implement the present invention set forth in the claims using ordinary skill without undue experimentation. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present invention, which is determined by the full scope of the appended claims and their equivalents.

[0226] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several programs for enabling a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0227] The logic and / or steps represented in a flowchart or otherwise described herein, for example, may be considered as an ordered list of executable programs for implementing the logical functions, and may be embodied in any computer-readable medium for use by, or in conjunction with, a program execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can retrieve and execute a program from a program execution system, apparatus, or device). For purposes of this specification, a "computer-readable medium" may be any device that can contain, store, communicate, propagate, or transport a program for use by, or in conjunction with, a program execution system, apparatus, or device.

[0228] It should be understood that various parts of the present invention can be implemented using hardware, software, firmware, or a combination thereof. In the above-described embodiments, multiple steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable program execution system. For example, if implemented using hardware, as in another embodiment, any one of the following technologies known in the art or a combination thereof can be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0229] In the above description of this specification, reference to the terms "one embodiment / example," "another embodiment / example," or "certain embodiments / examples" means that the specific features, structures, materials, or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0230] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

[0231] The above is a specific description of the preferred implementation of the present invention, but the present invention is not limited to the embodiments. Those skilled in the art can make various equivalent modifications or substitutions without violating the spirit of the present invention. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present invention.

Claims

1. A metabolic rate detection method, characterized in that: The method is performed by a metabolic rate detection system, wherein the metabolic rate detection system includes a local heat loss measurement module; the method includes: Collecting metabolic rate parameters of the target object; the metabolic rate parameters include heart rate, skin resistance, muscle rate or local heat loss parameters; the local heat loss parameters are used to characterize the measurement results of the target detection part of the target object based on the local heat loss measurement module; determining a local surface heat loss according to the local heat loss parameter; Determine the overall total heat loss and the overall unit heat loss based on the local surface heat loss and the convective heat loss coefficient of the target detection part; the convective heat loss coefficient is the ratio of the convective heat loss of the target detection part to the convective heat loss of all parts; The metabolic rate of the target subject is determined based on the overall unit heat loss, the heart rate, the skin resistance, or the muscle rate.

2. The metabolic rate detection method according to claim 1, characterized in that: The local heat loss measurement module includes a microchannel and temperature and humidity detection units located at both ends of the microchannel. The microchannel includes: a serpentine channel, a circular channel, or a straight channel. The local surface heat loss is determined according to the local heat loss parameter, including: Determining a first temperature and humidity at a channel entrance and a second temperature and humidity at a channel exit by means of the temperature and humidity detection unit; A local surface heat loss is determined according to the first temperature and humidity and the second temperature and humidity.

3. The metabolic rate detection method according to claim 1, characterized in that: The determining of the overall total heat loss and the overall unit heat loss according to the local surface heat loss and the convective heat loss coefficient of the target detection part includes: Collect the surface temperature and ambient temperature of each detection part; determining the convective heat loss of each of the detection locations according to the surface temperature and the ambient temperature; determining the convective heat loss coefficient according to the convective heat loss of each of the detection locations; determining an overall total heat loss based on the local surface heat loss and the convection heat loss coefficient; An overall unit heat loss is determined based on the overall total heat loss and the body surface area of ​​the target object.

4. The metabolic rate detection method according to claim 1, characterized in that: The determining the metabolic rate of the target subject according to the overall unit heat loss, the heart rate, the skin resistance, and the muscle rate includes: determining a first value based on the product of the overall unit heat loss, the heart rate, and the muscle rate; The metabolic rate is determined according to a quotient of the first value and the skin resistance.

5. The metabolic rate detection method according to claim 1, characterized in that: The metabolic rate detection system includes a square wave-based skin resistance measurement module, which includes: a square wave generator, a resistance measurement unit, and at least two electrodes, wherein the electrodes are used to provide an electrical signal related to the skin resistance of the target object. The skin resistance is collected, including: generating a square wave signal to periodically change the polarity of the electrode; The resistance measuring unit collects the electrical signal based on the electrodes to determine the skin resistance.

6. A metabolic rate detection system, characterized in that: The metabolic rate is measured by the metabolic rate detection method according to any one of claims 1 to 5, the system comprising: at least one of a parameter acquisition module or a skin resistance measurement module, a local heat loss measurement module, and a control module; The local heat loss measurement module is used to collect local heat loss parameters of the target object, and the parameter acquisition module is used to collect the heart rate or muscle rate of the target object; the skin resistance measurement module is used to collect the skin resistance of the target object; The control module is used for: determining a local surface heat loss according to the local heat loss parameter; Determine the overall total heat loss and the overall unit heat loss based on the local surface heat loss and the convective heat loss coefficient of the target detection part; the convective heat loss coefficient is the ratio of the convective heat loss of the target detection part to the convective heat loss of all parts; The metabolic rate of the target subject is determined based on the overall unit heat loss, the heart rate, the skin resistance, or the muscle rate.

7. The metabolic rate detection system according to claim 6, characterized in that: The local heat loss measurement module includes a microchannel, a temperature and humidity detection unit located at both ends of the microchannel, and a wind generator located at the entrance of the microchannel; the microchannel includes: a serpentine channel, a circular channel or a straight channel; The temperature and humidity detection unit is used to collect a first temperature and humidity at the channel entrance and a second temperature and humidity at the channel exit; The control module is used to: determine the local surface heat loss according to the first temperature and humidity and the second temperature and humidity; The metabolic rate detection system also includes a temperature detection module for collecting the surface temperature and ambient temperature of the local detection site; the control module is also used to determine the overall unit heat loss based on the surface temperature, the ambient temperature and the local surface heat loss.

8. The metabolic rate detection system according to claim 6, characterized in that: The skin resistance measurement module includes: a square wave generator, a resistance measurement unit and at least two electrodes; The resistance measuring unit is connected to the electrode via the square wave generator, and the electrode is used to provide an electrical signal related to the skin resistance of the target object; The control module is used for: generating a square wave signal to periodically change the polarity of the electrode; The resistance measuring unit collects the electrical signal based on the electrodes to determine the skin resistance.

9. The metabolic rate detection system according to claim 7, characterized in that: The microchannel is an acrylic channel with a height of 1 to 5 mm and a length of 10 to 30 mm.

10. A wearable sensor, characterized in that: The method comprises the metabolic rate detection system according to any one of claims 6 to 9 , and measures the metabolic rate by the metabolic rate detection method according to any one of claims 1 to 5 .

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