Method and system for assessing metabolic rate and maintaining indoor air quality and efficient ventilation energy use with passive environmental sensors

Abstract

An integrated method for assessing metabolic rate and maintaining indoor air quality and efficient ventilation energy use. A physical sensor assesses room occupancy. An actuated ventilation system is set to a constant CO.sub.2 level in a predetermined healthy range, where the actuated ventilation system includes a CO.sub.2 sensor. The actuated ventilation system sets a first air ventilation rate and the sensor measures a first CO.sub.2 level. The system determines whether CO.sub.2 level is in a healthy range, if not then the CO.sub.2 level is adjusted by setting a subsequent air ventilation rate. A subsequent CO.sub.2 level is measured. If the CO.sub.2 level is determined to meet a predetermined healthy range, then an assessment of change of air ventilation rate (Δ ACH) is determined. The determination of air change rate can be further augmented by a physical pressure based measurement. The overall metabolic rate is generated.

Claims

1. An integrated method for assessing metabolic rate and maintaining indoor air quality comprising: a) assessing room occupancy with at least one physical sensor; b) setting an actuated ventilation system to a constant CO.sub.2 level in a predetermined healthy range, where the actuated ventilation system includes at least one CO.sub.2 sensor; c) operating the actuated ventilation system to set a first air ventilation rate; d) operating the at least one CO.sub.2 sensor to measure a first measured CO.sub.2 level; e) determining if the first measured CO.sub.2 level is in a predetermined healthy range, if not then further actuating the actuated ventilation system to set a second air ventilation rate to drive an adjusted CO.sub.2 level into the predetermined healthy range; f) operating the at least one CO.sub.2 sensor to measure the adjusted CO.sub.2 level; and g) operating the actuated ventilation system to determine if the adjusted CO.sub.2 level is at the predetermined healthy range and if it is then determining the setting of change of air ventilation rate (αACH), otherwise repeating acts f)-g); h) assessing air change rate; and i) operating a processor to provide, based on a difference between the adjusted CO.sub.2 level and the first measured CO.sub.2 level, an assessment of overall metabolic rate for multiple users or an individual assessment of an individual's metabolic rate for a single user.

2. The method of claim 1 further including the act of recognizing a particular subject by operating a physical sensor.

3. The method of claim 2 wherein the physical sensor is selected from the group consisting of a weight sensor, a biometric sensor and combinations thereof.

4. The method of claim 2 further including the act of learning from recognizing the subject to improve accuracy.

5. The method of claim 1 wherein at least one CO.sub.2 sensor that mitigates drift by operating a reference channel, using a CO.sub.2 non-absorbing wavelength, and a sensing channel using at least one CO.sub.2 absorbing wavelength, and signal resulting from the combination of both channels.

6. The method of claim 5 wherein the at least one CO.sub.2 sensor further comprises an in-situ temperature measurement sensor for corrections of CO.sub.2 level readings, and an auto-calibration algorithm for accurate CO.sub.2 readings over time.

7. The method of claim 1 wherein the predetermined healthy range of CO.sub.2 level is a set of values within 600-1000 ppm to enhance the air quality and the ventilation system energy use.

8. The method of claim 1 wherein the assessing of air change rate comprises assessing air change rate by a CO.sub.2 decay model or by an augmenting physical pressure based model.

9. An integrated system for assessing metabolic rate and maintaining indoor air quality in an indoor space, the system comprising: at least one physical sensor; an actuated ventilation system adapted to be set to a predetermined healthy range of CO.sub.2 level, where the actuated ventilation system includes at least one CO.sub.2 sensor; where the actuated ventilation system includes a set of conditions with pre-assessed and adaptable air ventilation rates (ACH), the actuated ventilation system is set to a first air ventilation rate (ACH1 providing a first CO.sub.2 value sensed by the at least one CO.sub.2 sensor and being in a predetermined healthy range, and the actuated ventilation system is configured to change to a second air ventilation rate (ACH2) providing a sensed CO.sub.2 value sensed by the at least one CO.sub.2 sensor that is equal to the first CO.sub.2 value to keep a constant CO.sub.2 level; and a processor programmed for determining an assessment of change of air ventilation rate (αACH) as a difference between ACH1 and ACH2, and outputting a metabolic rate for one or more individuals present in the indoor space.

10. The system of claim 9, wherein the processor is further programmed for recognizing a particular subject by operating the at least one physical sensor.

11. The system of claim 10 wherein the physical sensor is selected from the group consisting of a weight sensor, a biometric sensor and combinations thereof.

12. The system of claim 9 wherein the at least one CO.sub.2 sensor further comprises a reference channel, using a CO.sub.2 non-absorbing wavelength, and a sensing channel using a CO.sub.2 absorbing wavelength, and a combined signal resulting from both channels.

13. The system of claim 12 wherein the at least one CO.sub.2 sensor further comprises an in-situ temperature measurement sensor for corrections of CO.sub.2 level readings, and an auto-calibration algorithm for accurate CO.sub.2 readings over time.

14. The system of claim 9 wherein the predetermined healthy range of CO.sub.2 level is a set of values within 600-1000 ppm to enhance the ventilation system energy use.

15. An integrated method for assessing metabolic rate and maintaining indoor air quality comprising: a) assessing room occupancy with at least one physical sensor and recognizing a particular subject by operating a physical sensor; b) setting an actuated ventilation system to a constant CO.sub.2 level in a predetermined healthy range, where the actuated ventilation system includes at least one CO.sub.2 sensor; c) operating the actuated ventilation system to set a first air ventilation rate (ACH1); d) operating the at least one CO.sub.2 sensor to measure a first CO.sub.2 level, wherein the at least one CO.sub.2 sensor mitigates drift by operating a reference channel, using a CO.sub.2 non-absorbing wavelength, and a sensing channel using an IR wavelength, and signal resulting from the combination of both channels; e) operating the actuated ventilation system to determine if the CO.sub.2 level is a set value within a predetermined healthy range of 600-1000 ppm, if not then further actuating the actuated ventilation system set a second air ventilation rate (ACH2) to drive the CO.sub.2 level into the predetermined healthy range; f) operating the at least one CO.sub.2 sensor to measure a second CO.sub.2 level; and g) operating the actuated ventilation system to determine if the CO.sub.2 level is at the predetermined healthy range and if it is then determining an assessment of change of air ventilation rate (αACH), otherwise repeating acts f)-g); h) assessing air change rate; and i) outputting an assessment of overall metabolic rate for multiple users or outputting an individual assessment of metabolic rate for a single user, based on a difference between the first CO.sub.2 level and the second CO.sub.2 level.

16. The method of claim 15 wherein the physical sensor is selected from the group consisting of a weight sensor, a biometric sensor and combinations thereof.

17. The method of claim 16 further including the act of learning from recognizing the particular subject to improve accuracy of the assessment of overall metabolic rate for the multiple users or of the assessment of metabolic rate for the single user.

18. The method of claim 16 wherein the at least one CO.sub.2 sensor further comprises in-situ temperature measurement sensor for correcting CO.sub.2 level readings, and automatically calibrating for accurate CO.sub.2 readings over time.

19. The method of claim 15 wherein the assessing of air change rate comprises assessing air change rate by a CO.sub.2 decay model or by an augmenting physical pressure based model.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

(1) While the novel features of certain embodiments of the invention are set forth with particularity in the appended claims, the invention, both as to organization and content, will be better understood and appreciated, along with other objects and features thereof, from the following detailed description taken in conjunction with the drawings, in which:

(2) FIG. 1 schematically illustrates a system for assessing indoor air quality and energy efficiency and biometrics assessment under free-living conditions.

(3) FIG. 2 schematically shows an example of a sensor array for an enclosure, such as a small room or car cabin.

(4) FIG. 2A schematically shows a more detailed illustration of the sensor array console module.

(5) FIG. 3 shows data including temperature, relative humidity, and CO.sub.2 concentrations vs. time within a room for a classroom test of an assessment system.

(6) FIG. 4A-FIG. 4C, graphically show experimental data of CO.sub.2 levels corresponding to decay #1, #2, and #3 from FIG. 3.

(7) FIG. 5A-FIG. 5B graphically show experimental data representing CO.sub.2 concentration (ppm) vs. time.

(8) FIG. 6A-FIG. 6C schematically show an example of an occupancy sensor. As an example, the pressure-based sensor has two fractions that allow to detect object presence and movement from a person.

(9) FIG. 7A-FIG. 7C graphically show experimental data including temperature, relative humidity (RH), and CO.sub.2 concentrations vs. time within a 2007 Toyota Corolla while driving in testing conditions.

(10) FIG. 8A-FIG. 8C graphically show experimental data including growth of CO.sub.2 concentration vs. time.

(11) FIG. 9A-FIG. 9D graphically show experimental data including decay of CO.sub.2 concentration vs. time.

(12) FIG. 10 graphically shows temperature, relative humidity (RH), and CO.sub.2 concentrations vs. time within a car while driving with the ventilation system closed, and the driver breathing in and out of the car cabin via a tubing system that precludes exhaled carbon dioxide build up.

(13) FIG. 11A-FIG. 11C graphically show experimental data including CO.sub.2 concentration, VCO.sub.2, and metabolic rate vs. time.

(14) FIG. 12 schematically shows a block diagram illustrating an example of an algorithm employed in a method and system for assessing metabolic rate and maintaining indoor air quality using passive environmental sensors.

(15) FIG. 13 schematically shows a method for learning to recognize a particular subject in block diagram form.

(16) FIG. 14 shows a more detailed schematic of the sensor array.

(17) FIG. 15 shows the relationship between the effective air exchange rate (A) inside a car cabin assessed with a single occupant (driver) and driving speed in miles per hour (MPH).

(18) FIG. 16 shows the metabolic rate as EE (energy expenditure, kcal/day) on the right, and the model fitted CO.sub.2 generation rate (K.sub.gen) on the left.

(19) FIG. 17A-FIG. 17D show examples of real-time CO.sub.2 concentration profile and the model fitted curves under different driving speeds.

(20) FIG. 18A shows an example of a vehicle used in assessment of data shown in FIG. 15 thru 17.

(21) FIG. 18B shows an example of a vehicle AC control panel.

(22) FIG. 18C shows examples of a vehicle's optional ventilation conditions.

(23) FIG. 19A-FIG. 19D show examples of CO.sub.2 concentration at different vehicle speeds and for different numbers of vehicle occupants.

(24) FIG. 20A shows how a CO.sub.2 profile can be affected by changing the effective air exchange rate from 1 to a higher value, up to 22 h.sup.−1, e.g. by alternating the recirculation (RC) mode on and off, and/or opening windows.

(25) FIG. 20B demonstrates the effect of occupant metabolic rate on CO.sub.2 accumulation within a vehicle.

(26) In the drawings, identical reference numbers identify similar elements or components. The sizes and relative positions of elements in the drawings are not necessarily drawn to scale. For example, the shapes of various elements and angles are not drawn to scale, and some of these elements are arbitrarily enlarged and positioned to improve drawing legibility. Further, the particular shapes of the elements as drawn, are not intended to convey any information regarding the actual shape of the particular elements, and have been solely selected for ease of recognition in the drawings.

DETAILED DESCRIPTION

(27) The following disclosure describes a method and apparatus for assessing metabolic rate and indoor air quality. Several features of methods and systems in accordance with example embodiments are set forth and described in the figures. It will be appreciated that methods and systems in accordance with other example embodiments can include additional procedures or features different than those shown in the figures. Example embodiments are described herein with respect to a method and apparatus using passive sensors for monitoring carbon dioxide (CO.sub.2) and setting an air change rate (ACH) in a car cabin or small room. However, it will be understood that these examples are for the purpose of illustrating the principles, and that the invention is not so limited.

(28) Unless the context requires otherwise, throughout the specification and claims which follow, the word “comprise” and variations thereof, such as, “comprises” and “comprising” are to be construed in an open, inclusive sense that is as “including, but not limited to.”

(29) The term air change rate and air ventilation rate as well as the symbol ACH are used with the same meaning, and means the number of times the volume of a room or car cabin is exchanged per unit of time.

(30) Reference throughout this specification to “one example,” “an example embodiment,” “one embodiment,” “an embodiment” or combinations and/or variations of these terms means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present disclosure. Thus, the appearances of the phrases “in one example” or “in an example” in various places throughout this specification are not necessarily all referring to the same example embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

Definitions

(31) Generally, as used herein, the following terms have the following meanings when used within the context of air quality assessment:

(32) The articles “a” or “an” and the phrase “at least one” as used herein refers to one or more.

(33) “The Auto41 system” as used herein refers to a system developed by the inventors that passively detects and tracks metabolic rate via indirect calorimetry under free-living conditions in confined environments that includes an array of sensors and an intelligent algorithm embedded in a mobile device as a software application (app).

(34) Bluetooth® technology, as used herein means a commercially available low-power wireless connectivity technology used to stream audio, transfer data and broadcast information between devices.

(35) As used herein, “plurality” is understood to mean more than one. For example, a plurality refers to at least two, three, four, five, ten, 25, 50, 75, 100, 1,000, 10,000 or more.

(36) As used in this specification, the terms “computer”, “processor” and “computer processor” encompass a personal computer, a tablet computer, a smart phone, a microcontroller, a microprocessor, a field programmable object array (FPOA), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a programmable logic array (PLA), or any other digital processing engine, device or equivalent capable of executing software code including related memory devices, transmission devices, pointing devices, input/output devices, displays and equivalents.

(37) “Obtaining” is understood herein as manufacturing, purchasing, or otherwise coming into possession of.

Example Embodiments

(38) In contrast to known methods described in the background section above, here presented for the first time is a method and system for accurate assessment of metabolic rate under free living conditions, using an inexpensive sensor system that passively assesses the metabolic rate in a minimum period of 20 min.-90 min. in conjunction with the simultaneous assessment of indoor air quality (IAQ) so that the indoor air quality can be maintained. In one example, the system includes sensors of carbon dioxide, temperature, humidity, pressure, noise and occupancy, as well as a mathematical model that determines individual's metabolic rate from produced carbon dioxide (VCO.sub.2) rate.

(39) The system's sensors could be deployed in numerous places using ventilation systems, to monitor and adjust indoor air quality (IAQ) to maintain good IAQ as needed. In this regard, CO.sub.2 concentrations and other parameters known to the field, including but not limited to equivalent carbon dioxide concentration, can be used as surrogate of IAQ. The primary source of CO.sub.2 concentrations within non-industrialized settings—and in the absence of combustion sources—is from the produced carbon dioxide (VCO.sub.2) rate of a room's occupants. Today, a majority of people spend approximately 90% of their day indoors, and, as a result, are at risk of developing the signs and symptoms associated with Sick Building Syndrome (SBS) [11a]. Furthermore, recent publications have reported direct effects of low-to-moderate CO.sub.2 concentrations on human decision-making performance [11 b-d]. It is also probable that human physical performance may be affected due to exposure to unhealthy CO.sub.2 concentrations. The optimization of existing ventilation systems is necessary to minimize SBS symptoms and degradation on human performance. Current monitoring methods for ventilation systems offer intermittent sampling of parameters for IAQ, however, the ability to confirm the physical presence of people in a setting and to determine the number of occupants in confined environments allows for unique assessment of the individual's biometrics, more specifically a metabolic rate.

(40) One motivation inspiring the presently disclosed system is the need for detection and quantification of CO.sub.2 in indoor conditions generated by human metabolic rates. It is known that CO.sub.2 is already in the atmosphere in concentrations ranging from approximately 350 to 450 ppm [11a, 12]. Current atmospheric levels of CO.sub.2 pose little to no health risks. The US Occupational Safety and Health Administration has set a 5000-ppm Permissible Exposure Limit as an 8-hour Total Weight Average (TWA), and a 30,000-ppm Short Term Exposure Limit as a 10-minute TWA for CO.sub.2 [13]. In non-industrialized indoor settings, such as an office, classroom, and vehicle; CO.sub.2 concentrations can range between 350 and 4500 ppm and higher [11, 12]. Studies have shown that indoor occupants have reported symptoms of discomfort at indoor CO.sub.2 concentrations that exceed 600 ppm, when spending a substantial amount of time within an indoor setting [11], which is below the established exposure limits [14]. The discomfort affecting these occupants can be serious; those in CO.sub.2 rich environments exhibit upper and lower respiratory tract complications such as dysphonia, dry cough, and asthma. Nervous system complications including headaches and difficulty concentrating are also signs of CO.sub.2 overexposure in addition to skin and eye irritation [15].

(41) Referring now to FIG. 1, a system that passively detects and tracks metabolic rate via indirect calorimetry under free-living conditions in confined environments is schematically shown. A system for providing metabolic rate readings under free-living conditions 10, while maintaining indoor air quality and energy efficiency includes a sensor array 15 installed in a small room or car cabin 20, and a processor including a user interface 22 viewed, for example, from an application 25 (app) in a processor 24 as installed in a mobile device or display, such as a “smart phone,” computer, tablet computer, LCD display or the like. The fixed sensor array 15 includes a console module and a seat module as described in more detail below with respect to FIG. 2. The user interface 22 contains a software application that acquires the data, and uses an intelligent algorithm to actuate a ventilation system, and determine the changes in Air change rate (ΔACH) so that a good indoor air quality is achieved by maintaining constant and healthy levels of CO.sub.2 with an energy efficient cost. The system determines variability of ACH and provides metabolic rate information of the subject. 30, which can be related to lifestyle, environmental factors, and can provide information related to weight management based on other biometrics and environmental readings obtained from the individual. Communication between the sensor array 15 and the user interface 22 may be wired or implemented using known wireless technologies 17, for example Bluetooth® technology.

(42) In response to information received from the sensor array 15, the application 25 provides output readings of metabolic rate for a single individual or multiple individuals. The output readings are extracted from the air change rate changes and levels of CO.sub.2 and are graphically illustrated in plot 40 showing levels of metabolic rate versus time. Low levels of metabolic rate 42, 44 may correspond, for example to sleep deprivation in a subject or subjects. High levels of metabolic rate 48, may correspond to for example, stress levels in the subject(s).

(43) In one example, user interface 22 may include a display including an upper display 50 showing a graphical representation of ACH/hour and a lower display 52 indicating indoor air quality index based on CO.sub.2 levels in parts per million. One of the displays may include color-coded stratified regions in order to give a viewer an indication of whether readings of CO.sub.2 levels are in a good range, an unhealthy high range or an unhealthy low range. Another display may include color-coded stratified regions in order to give a viewer an indication of energy expenditures of the ventilation system to keep the pre-set CO.sub.2 level at constant and healthy levels.

(44) One example of system 10 is being implemented by the inventors and named the “Auto41” system which has an array of sensors and an intelligent algorithm embedded in a mobile device's specific application (app). The algorithm is explained in more detail below. The system can be used without any training. In operation, the system detects the presence of an individual or individuals in a confined environment, such as a small office or a car, and passively assess his/her/their metabolic rate. The system also provides an indoor air quality index based on carbon dioxide level and the air change rate changes to maintain good IAQ. The Auto41 system is an always-on system, so it can be used anytime, as soon as it detects occupancy.

(45) Referring now to FIG. 2, an example of a sensor array for a small room or car cabin is shown schematically. The sensor array 15 includes a console module 60, a seat module 62, and ventilation module with pre-calibration for air change rate (ACH) 70. The console module 60 includes a number of sensors for detecting, for example, carbon dioxide levels, environmental comfort parameters (e.g. temperature, humidity, pressure) and noise. The seat module 62 may include sensors for detecting heart rate and occupancy (using a sensor based on pressure detection for weight). The ventilation 70 is pre-calibrated for air change rate.

(46) The sensor array ventilation module can include flow sensor(s) and pre-calibrates the system for air change rate. In another embodiment, the seat module can be replaced with an optical or virtual modules that are designed to determine number of occupants and heart rates [9, 10].

(47) The sensor array 15 may be incorporated into the Auto41 system and integrated into the console module 60 and seat module 62 for monitoring occupancy, environmental comfort conditions, CO.sub.2, weight, and/or heart rate, and setting air change rates. The system enables the exploration of health-related outcomes under never-before implemented, “freely-behaving conditions with good indoor air quality assurance” so that researchers can further the understanding of the ongoing obesity epidemic in the United States. It is hypothesized that assessing metabolic rate passively and for prolonged periods of time will provide an unprecedented opportunity of data mining to find patterns of environment-person-metabolic rate interactions related to the obesity epidemic.

(48) Referring now to FIG. 2A, a more detailed illustration of the sensor array console module is schematically shown. Console module 60, in one example, may include a carbon dioxide sensor 72, a relative humidity sensor 74, a temperature sensor 76, a barometric pressure sensor 78, noise sensor 80, a microcontroller 82 for onboard signal processing and a Bluetooth® chip 84. Those skilled in the art are familiar with such commercially available components. Readings from each or any of the sensors may be transmitted to a user interface 24 for further processing, or/and may be processed by the onboard software application implementing the system algorithm.

Experimental Examples

(49) Having described the salient components of the herein disclosed system for assessment of metabolic rate and maintenance of indoor air quality, experimental results will now be discussed to promote further understanding of the components of the invention. Two different settings were used to probe the assessment air change rate, and metabolic rate for a classroom and a car cabin under variable CO.sub.2 levels. One additional setting was used to probe the assessment of metabolic rate in a car cabin, while maintaining constant CO.sub.2 levels by an actuated ventilation previous calibrated for air change rate. Classroom: This allowed implementation of the passive metabolic assessment system in a large room along with the assessment of averaged metabolic rate in the class, as well as the assessment of air change rate (ACH). Car cabin: This allowed the assessment of an individual's metabolic rate value, and characterization of the ACH under conditions of no-ventilation and recirculation mode, a mode typically used in cars to save cooling or heating energy. On a different setting, the car cabin allowed the assessment of metabolic rate under constant CO.sub.2 conditions.
For sake of convenience, both scenarios are sometimes referred to herein as a “room”.

(50) Classroom tests: The classroom environment tests were conducted on Arizona State University's main campus in Tempe, Ariz. within the G-wing of the Engineering Center, room number 237 (ECG 237). The subjects for the classroom environment tests were from a set of classes that were conducted at ECG 237: an afternoon class of 18 and a morning class of 30. The ages of the classroom test subjects were from 18 to 30 years old. Results from the classroom tests were averaged out among the 18 and 30 respective occupants within each class. Individualized health results for each classroom test subject were not taken. Only an average age, weight, and height with a percentage of gender (which was ˜60% for male, and ˜40% female) was assessed with no identifiers from the occupants in the room, and used to estimate the expected average metabolic rate using Mifflin-St Jeor epidemiological Equation [3].

(51) Car tests: The vehicle tests were conducted on residential streets within the city of Tempe in a Toyota Corolla with a car test subject, healthy 27-year-old adult male. The car environment tests portion of the study was approved by the Institutional Review Board of Arizona State University (IRB protocol #1304009100 for collection of environmental data, and #1012005855 for collection of metabolic rate data with portable indirect calorimetry technology). The test subject participated voluntarily, providing written consents to participate in the study. All tests for this study were conducted from February to April 2017. In another setting, the vehicle tests were conducted on Tempe Campus of Arizona State University by a second driver in a Hyundai, Electra (2012) car.

(52) Sensors and Devices

(53) A sensor system was built to conduct the tests. The system consisted of a carbon dioxide sensor, a temperature and humidity sensor (HOBO® unit), and a noise and pressure-based sensor to detect occupancy (details below). The sensors were connected to an external data logger. The carbon dioxide sensor was based on a double-beam non-dispersed infrared (NDIR) detector with a maximum CO.sub.2 sensing wavelength of 4.26 μm [16, 17], [18]. Specifications of the sensor array includes an accuracy of ±50 ppm or 5% of reading and a repeatability of ±20 ppm as well as an operating range from 32° F. to 122° F. and 0 to 95% Relative Humidity, non-condensing. Calibrations using dilutions of a 5% carbon dioxide gas and pure air were completed before any study tests were conducted to assure the accuracy of the system CO.sub.2 detection levels.

(54) Sensors' Experimental Setup

(55) The experimental setup for the classroom tests involved placing the sensor array in a location within ECG 237 that would provide readings that closely mirrored the average CO.sub.2 concentration of the entire room. In this case, the noise sensor and pressure based sensor for occupancy were not used, and occupancy was assessed via physical person count. Two mid-size, 1-ft diameter fans were placed in the front and back of the room to ensure a well-mixed environment. The sensor was started at 4:00 PM on Feb. 20, 2017 and collected data the entire night and early morning. This was done to obtain a more representative CO.sub.2 concentration profile. At about 10:17 AM on Feb. 21, 2017 the sensor's data logger was stopped and all data was collected.

(56) The experimental setup for the vehicle tests optimized the placement of the CO.sub.2, temperature, and humidity sensors in locations that would give the most accurate representation of the average CO.sub.2 concentration of the entire car volume. The sensor was placed in the front dashboard approximately a meter from the driver. Conditions such as the opening and closing of windows were tracked and varied over a series of tests. The vehicle tests were conducted with the same occupant for all tests. In addition, testing of single occupancy was corroborated with the noise and pressure-based sensors, which are described in the result and discussion section.

(57) Modeling

(58) The base development of the model considers prior state of art by the World Health Organization to monitor indoor pollutant concentrations [6], and includes the following assumptions: The only source of CO.sub.2 generated is from a room's occupants. Once all occupants leave, CO.sub.2 is assumed to decay until equilibrium is reached with the outside baseline value at approximately 350-450 ppm. The air is displaced via the room's natural and/or mechanical ventilation systems.
The model considers the following total differential equation for the change in CO.sub.2 concentrations:

(59) $\begin{array}{cc}\frac{d\left[{\mathrm{CO}}_2\right]}{\mathrm{dt}}=\frac{{d\left[{\mathrm{CO}}_2\right]}_{gen}}{\mathrm{dt}}+\frac{{d\left[{\mathrm{CO}}_2\right]}_{\mathrm{ven}}}{\mathrm{dt}}& \mathrm{Eq}.\left(1\right)\end{array}$
where [CO.sub.2].sub.gen is the carbon dioxide generated by the room's occupant, and [CO.sub.2].sub.ven is the carbon dioxide ventilated from the room. Three situations were modeled and measured: a—Ventilation and no occupants in the room, b—Ventilation and occupants in the room, c—No ventilation and occupants in the room. a—Ventilation and no occupants in the room: In this condition, the generation is assumed to be 0, and the differential equation simplifies to the following:

(60) $$\begin{gathered} \begin{array}{cc}\frac{{d\left[{\mathrm{CO}}_2\right]}_{gen}}{\mathrm{dt}}=0.fwdarw.\frac{d\left[{\mathrm{CO}}_2\right]}{dt}=\frac{{d\left[{\mathrm{CO}}_2\right]}_{ven}}{\mathrm{dt}} \\ \frac{d\left[{\mathrm{CO}}_2\right]}{\mathrm{dt}}=-{k_{ven}\left[{\mathrm{CO}}_2\right]}^x& \mathrm{Eq}.\left(2\right)\end{array} \end{gathered}$$
where k.sub.ven is the Air Change Rate, and the unknown (x) ventilation order can be determined by plotting the following relationships:

(61) Zeroth Order Ventilation: [CO.sub.2]═[CO.sub.2].sub.i−k.sub.vent

(62) First Order Ventilation: ln[CO.sub.2]=−k.sub.vent+ln[CO.sub.2].sub.i

(63) Second Order Ventilation:

(64) $\frac{1}{\left[{\mathrm{CO}}_2\right]}=\frac{1}{{\left[{\mathrm{CO}}_2\right]}_i}+k_{ven}t$

(65) As will be discussed in the results section, the first order ventilation rate law yielded the highest R-squared values for all decays. Consequently, the ventilation reaction rate constant (k.sub.ven) is equivalent to the Air Change Rate (ACH), defined here as A. The first order type of decay validated previous studies conducted by Escombre and Bouhamra et. al. [19, 20]. Therefore, Eq. (1) is simplified to:

(66) $\begin{array}{cc}\frac{d\left[{\mathrm{CO}}_2\right]}{\mathrm{dt}}=-{k_{ven}\left[{\mathrm{CO}}_2\right]}^1=-{\lambda \left[{\mathrm{CO}}_2\right]}^1& \mathrm{Eq}.\left(3\right)\end{array}$
Solving for the indoor CO.sub.2 concentration in an interval where the CO.sub.2 concentration ranges between an initial value [CO.sub.2], and a final value [CO.sub.2] for all times intervals generates the following analytical expression for [CO.sub.2]:

(67) ${\int }_{{\left[{\mathrm{CO}}_2\right]}_i}^{\left[{\mathrm{CO}}_2\right]}\frac{1}{\left[{\mathrm{CO}}_2\right]}d\left[{\mathrm{CO}}_2\right]=-{\int }_{t_i}^t\lambda \mathrm{dt}.fwdarw.\ln \left[{\mathrm{CO}}_2\right]-{\ln \left[{\mathrm{CO}}_2\right]}_i=-\lambda t$$\begin{array}{cc}\ln \left[{\mathrm{CO}}_2\right]={\ln \left[{\mathrm{CO}}_2\right]}_i-\lambda t& \mathrm{Eq}.\left(4\right)\\ \left[{\mathrm{CO}}_2\right]={\left[{\mathrm{CO}}_2\right]}_i{e}^{-\lambda t}& \mathrm{Eq}.\left(5\right)\end{array}$ b—Ventilation and occupants in the room: the second scenario considered the generation of CO.sub.2 from occupants with an ACH different from 0. In this situation, Eq. 1 can be generically expressed:

(68) $\begin{array}{cc}\frac{d\left[{\mathrm{CO}}_2\right]}{\mathrm{dt}}=\frac{{d\left[{\mathrm{CO}}_2\right]}_{gen}}{\mathrm{dt}}-\lambda \left[{\mathrm{CO}}_2\right]& \mathrm{Eq}.\left(6\right)\end{array}$
and solved, considering the CO.sub.2 concentration at steady state (S.S.). Making this assumption the differential equation simplifies to the following:

(69) $\mathrm{At}S.S.:0=\frac{{d\left[{\mathrm{CO}}_2\right]}_{gen}}{dt}-\lambda \left[{\mathrm{CO}}_2\right].fwdarw.\lambda \left[{\mathrm{CO}}_2\right]=\frac{{d\left[{\mathrm{CO}}_2\right]}_{gen}}{dt}\mathrm{At}S.S.:\frac{{d\left[{\mathrm{CO}}_2\right]}_{gen}}{dt}=\lambda \left[{\mathrm{CO}}_2\right]={\lambda \left[{\mathrm{CO}}_2\right]}_{gen}$
Assuming the following boundary condition
At t=0.fwdarw.[CO.sub.2]═[CO.sub.2].sub.i
where [CO.sub.2].sub.i refers to any CO.sub.2 above the baseline atmospheric concentration initially present, and a baseline atmospheric concentration of CO.sub.2, [CO.sub.2].sub.0, which is equivalent to 350 to 450 ppm, the following analytical expression for [CO.sub.2] is obtained:

(70) $\begin{array}{cc}\left[{\mathrm{CO}}_2\right]={\left[{\mathrm{CO}}_2\right]}_0+{\left[{\mathrm{CO}}_2\right]}_{gen}\left(1-e^{-\lambda t}\right)+{\left[{\mathrm{CO}}_2\right]}_i{e}^{-\lambda t}& \mathrm{Eq}.\left(7a\right)\end{array}$$\begin{array}{cc}{\left[{\mathrm{CO}}_2\right]}_{gen}=\frac{{\left[{\mathrm{CO}}_2\right]}_0+{\left[{\mathrm{CO}}_2\right]}_t{e}^{-\lambda t}-\left[{\mathrm{CO}}_2\right]}{e^{-\lambda t}-1}& \mathrm{Eq}.\left(7b\right)\end{array}$
Eq. (7a) and (7b) was used to fit the experimental data with units defined as follows: Air Change Rate (ACH), λ=hour.sup.−1; Initial Concentration of carbon dioxide, [CO.sub.2].sub.i=ppm, Outdoor CO.sub.2 Concentration, [CO.sub.2].sub.0=ppm; Generated CO.sub.2, [CO.sub.2].sub.gen=ppm; and time, t=hour.
Alternatively or as an added measure, pressure measurements can be used to determine ACH based on physical principles that is independent of chemical species. Volumetric flow follows the equation:
Q=k(p).sup.0.5  Eq. (8)
where Q is the volumetric flow rate, k is a calibration constant, and Δp is pressure change of the enclosure.
As a result, by measuring the pressure change, the flow rate can be determined. The air change rate (ACH) can subsequently be determined since it is proportional to Q. When the ventilation is turned on, the pressure inside the room will temporarily increase. The pressure will decrease due to leakage and eventually reach a plateau. This plateau value is expected to be a function of the rate at which fresh air entering the enclosure equals air leaked. When the ventilation is turned off, the pressure inside the enclosure will decrease, according to the equation:
Δp=Δp.sub.plateaue.sup.−ACH.Math.t  Eq. (9)
where Δp.sub.plateau is the plateau pressure difference, and ACH is the air change rate. As can be seen from the equation:
ACH=ln(2)/t.sub.1/2  Eq. (10)
where t.sub.1/2 is the time it take to reach half of the plateau value, ln denotes natural logarithm. Therefore, by measuring t.sub.1/2, the air change rate (ACH) can be determined. It is important to point out that this approach allows frequent correction of changes in ACH, thus allowing more accurate and robust measurements, even when there are changes in the environments.

(71) Assessment of indoor air quality and metabolic rate: In environments where occupants are coming in and out and leave an empty room, the air change rate was determined with experimental decay values by taking the natural logarithm of the CO.sub.2 concentration and plotting it against the time at which the decay began. The slope of the line corresponded to an ACH value. The ACH value, denoted as λ (hour.sup.−1), along with the room's volume (V) is related to the ventilation rate (Q) via the following equation [19]:
Q=λ*V  Eq. (11)
The ventilation rate is needed to solve the generated CO.sub.2 ([CO.sub.2].sub.gen) [21]

(72) $\begin{array}{cc}{\left[{\mathrm{CO}}_2\right]}_{gen}=\frac{{\mathrm{CO}}_2\mathrm{source}\mathrm{generation}\mathrm{rate}}{\mathrm{Room}\mathrm{ventilation}\mathrm{rate}}*10^6\mathrm{ppm}=\frac{\stackrel{.}{P}}{Q}\left(\mathrm{ppm}\right)& \mathrm{Eq}.\left(12\right)\end{array}$
Plotting experiment values from the classroom and vehicle tests, and extracting known values of λ, V, [CO.sub.2].sub.o, and [CO.sub.2].sub.i, the value of {dot over (P)} was fitted and empirically determined after completing a series of iterations. For example, the Microsoft Excel® What-if-analysis Goal Seek function was used for part of this work to determine values of P that produced the smallest percent error between the experimentally obtained CO.sub.2 concentrations and values obtained via the developed model (Eq. 7).

(73) Understanding the phenomena of cellular respiration has allowed for the development of a model that directly relates metabolic rate to consumed O.sub.2 (VO.sub.2) rate and produced CO.sub.2 (VCO.sub.2) rate by a living being. This relationship was developed by physiologist J. B. de V. Weir [22, 23]:

(74) 0$\begin{array}{cc}\mathrm{Metabolic}\mathrm{Rate}\left(\frac{\mathrm{kcal}}{\mathrm{day}}\right)=\left[3.941\left({\mathrm{VO}}_2\left(\frac{L}{\min }\right)\right)+1.11\left({\mathrm{VCO}}_2\left(\frac{L}{\min }\right)\right)\right]*1.440& \mathrm{Eq}.\left(13\right)\end{array}$
In the absence of VO.sub.2 rate readings, such as in the Double Labeled Water Method [8], the concept of Respiratory Quotient (RQ) can be used to relate VCO.sub.2 to VO.sub.2 [24]. RQ is the ratio of the produced carbon dioxide rate (VCO.sub.2) and consumed oxygen rate (VO.sub.2) of a person:

(75) $\begin{array}{cc}RQ=\frac{{\mathrm{VCO}}_2}{{\mathrm{VO}}_2}& \mathrm{Eq}.\left(14\right)\end{array}$
The RQ can be implemented into the Weir equation giving the following relationship between metabolic rate, and VCO.sub.2 [24]:

(76) $\begin{array}{cc}\mathrm{Metabolic}\mathrm{Rate}\left(\frac{\mathrm{kcal}}{\mathrm{day}}\right)=\left[3.941\left({\mathrm{VCO}}_2\left(\frac{L}{\min }\right)\right)+\mathrm{RQ}+1.11\left({\mathrm{VCO}}_2\left(\frac{L}{\min }\right)\right)\right]*1.440& \mathrm{Eq}.\left(15\right)\end{array}$
The RQ is typically in the range of 0.67 to 1.2 and depends on the specific substrate undergoing cellular respiration [24]. However, studies have found the weighted average of RQ to be 0.86 [25]. Inputting this value into the Weir equation provides a simplified metabolic rate calculation:

(77) $\begin{array}{cc}\mathrm{Metabolic}\mathrm{Rate}\left(\frac{\mathrm{kcal}}{\mathrm{day}}\right)=8.19\left({\mathrm{VCO}}_2\left(\frac{\mathrm{mL}}{\min }\right)\right)& \mathrm{Eq}.\left(16\right)\end{array}$
Using the developed Weir equation and the described-above mathematical model, the VCO.sub.2 will be able to be quantified via passive environmental CO.sub.2 sensors and used to determine a person's metabolic rate or the average metabolic rate in a group of a room's occupants. It is worthy to mention that, this is another important contribution by the present model that has not been reported before. c—No ventilation and occupants in the room: the third testing scenario considered the generation of CO.sub.2 from occupants, and no ventilation (ACH=λ=0). In this condition, the generation from occupant(s) can be considered zero order since the constant production of CO.sub.2 is intrinsic from the person as far as the person is living, and produced from consumed food and energy storage (person's glycogen, fat, proteins) [26, 27]. Note that this is another important contribution by the present model that has not been associated before. Therefore, Eq. (1) can be generically expressed as

(78) $\begin{array}{cc}\frac{d\left[{\mathrm{CO}}_2\right]}{\mathrm{dt}}={k_{gen}\left[{\mathrm{CO}}_2\right]}^0& \mathrm{Eq}.\left(17\right)\end{array}$
and solved as follows:
[CO.sub.2]=k.sub.gent+S  Eq. (18)
where S can be considered as follows from the boundary condition:
At t=0.fwdarw.[CO.sub.2]═S═[CO.sub.2].sub.0+[CO.sub.2].sub.i
with [CO.sub.2].sub.0, the baseline concentration based on CO.sub.2 atmospheric concentration, and [CO.sub.2].sub.i additional CO.sub.2 concentration from a remaining source. Therefore, the following analytical expression for [CO.sub.2] is applicable:
[CO.sub.2]=k.sub.gent+[CO.sub.2].sub.0+[CO.sub.2].sub.i  Eq. (19)
with k.sub.gen (ppm/min), ppm could be defined as mL of exhaled CO.sub.2/L of exhaled air volume=mL of exhaled CO2/dm.sup.3 exhaled air volume, k.sub.gen (ppm/min)×V (room volume) (dm.sup.3)=VCO.sub.2 (mL CO.sub.2/min), and VCO.sub.2 can be directly applied to Eq. (16) to assess metabolic rate.
Results
Classroom Tests Under Variable CO.sub.2 Levels

(79) In order to determine the capability of the Equations involved in the algorithm of the system presented in the present invention, the CO.sub.2 generation rate from a group of individuals in a classroom as well as the classroom Air Change Rate (ACH) were evaluated first. The generated carbon dioxide from a room's occupants was used to calibrate ACH of the room ventilation, and it was an advantage since: 1—it avoids the assessment of leakage functions and mechanical ventilation airflow rates, 2—CO.sub.2 is a chemically inert, and non-toxic gas, and 3—the release of CO.sub.2 concentrations from a room's occupants cause indoor concentrations to be greater than outdoor concentrations resulting in an outdoor-indoor concentration gradient [28]. Once all occupants of a room leave, the indoor CO.sub.2 concentrations decline until outdoor and indoor concentrations reach equilibrium and this decay can be used to determine the room's ACH [25].

(80) Referring now to FIG. 3, raw CO.sub.2 concentrations, relative humidity (RH), and temperature vs. time from the classroom test are graphically shown. The classroom test was conducted on Feb. 20, 2017 from 4:06 PM to 10:17 AM in the next day. The data show variable fluctuations of growths and decays with a long period of relatively constant concentrations. Temperature, relative humidity, and CO.sub.2 concentrations vs. time within room ECG 237 for Classroom test taken on February 20.sup.th to 21.sup.st from 4:06 PM to 10:17 AM. The left y-axis corresponds to the CO.sub.2 concentration in ppm, while the right y-axis corresponds to the temperature in ° F. and relative humidity in %. Measurements were updated every 30 seconds. “Decay #1” starts at 4:06 PM and ends at 4:22 PM. “Decay 2” occurs from 5:38 PM to 6:22 PM, and “Decay 3” occurs from 9:39 AM to 10:16 AM. “Growth 1” starts at 4:22 PM and ends at 5:38 PM, and “Growth 2” starts at 8:35 AM to 9:39 AM.

(81) The sensor's data logging started in the middle of a decay, labeled as “decay #1”. The decline in CO.sub.2 levels continued until approximately 4:22 PM where there was a reversed CO.sub.2 level growing trend labeled “growth #1”. Growth #1 was a clear indicator that the room gained occupants as the scheduled class time was approached. Concentrations rose until a plateau value was obtained at about 4:52 PM. The plateau showed that steady-state CO.sub.2 concentration was reached, and that the difference between the CO.sub.2 being produced and the ventilation rate was approximately zero. The plateau continued until 5:38 PM which corresponded to the course's dismissal time. A decay, labeled “decay #2”, took place until the indoor CO.sub.2 concentration matched outside levels. The baseline value was attained at about 6:22 PM and was determined to be at about 436 ppm. A growth in concentration, labeled “growth #2”, occurred at 8:35 am where the CO.sub.2 levels rose rapidly, and reached quasi-steady state. At 9:39 AM, the last observed decay, “decay #3”, occurred. Growth #2 and decay #3 corresponded to the arrival and dismissal of the morning class.

(82) Referring now to FIG. 4A-FIG. 4C, data of CO.sub.2 levels corresponding to decays #1, #2, and #3 from FIG. 3, as ln CO.sub.2 (ppm) vs. time are shown. FIG. 4A-FIG. 4C show an example graphical representation of the natural logarithm of CO.sub.2 concentration in ppm vs. time in hours for: A—“Decay #1” that occurred from 4:06 to 4:22 PM, where no room occupants were present for the duration of the decay. The slope of the line is the ACH value, which was determined to be 1.68 hour.sup.−1. B—“Decay #2” that occurred from 5:38 to 6:22 PM, where no room occupants were present for the duration of the decay. The ACH value was determined to be 1.10 hour.sup.−1. C— “Decay #3” that occurred from 9:39 to 10:16 AM, where three room occupants were present for the duration of the decay. The slope was determined to be 0.90 hour.sup.−1. This represents a typical mechanism for calibration of air change rate in environment with known environmental conditions. In an alternative approach, ACH can be determined or corrected real-time with a ambient air pressure sensor where the air change rate changes can be interpreted with Bernoulli's theory.

(83) The linear behavior between ln[CO.sub.2] and time, indicated that the ventilation rate has a first order behavior, and followed Eq. (4). Therefore, a linear fitting was performed to determine the ACH values, which rendered R.sup.2 coefficients between 0.95-0.96. Table 1 summarizes the findings.

(84) TABLE-US-00001 TABLE 1 ACH values from classroom tests providing pre-calibrated ventilation conditions to the system. Classroom's Air Change Rate λ (hour.sup.−1) Decay #1 1.68 Decay #2 1.10 Decay #3 0.90

(85) Note that the decay #1 was a period where the classroom had no occupants and the door remained open with ventilation supported by fans as described in the experimental section. On the contrary, decay #2 had no occupants also, but the classroom door was closed with ventilation supported by fans. On the other hand, decay #3 had the classroom door closed, fans were off but there were 3 occupants. The higher ACH value resulting from decay #1 with respect to decay #2 may due to the fact of having the door opened vs. closed. In addition, the fact of having slightly lower value in decay #3 vs. decay #2 could be attributed to the fans off and 3 occupants concurrently producing CO.sub.2 in the room. It is worth noticing that Eq. (4) was used for decay #3, and it was assumed the contributions of 3 occupants left in the room after the presence of 30 occupants was not significant.

(86) Referring now to FIG. 5A-FIG. 5B graphical representation of the CO.sub.2 concentration (ppm) vs. time is shown. The fitted parameter is [CO.sub.2].sub.gen (CO.sub.2 source generation rate) value for: A—“Growth #1” that occurred from 4:25 to 4:45 PM, where 18 room occupants were present. B—“Growth #2” that occurred from 8:51 to 9:39 AM, where 30 room occupants were present.

(87) FIG. 5A-FIG. 5B shows the curves of periods for growth #1 and growth #2 from FIG. 3. In this case, fittings of the CO.sub.2 levels to Eq. (7a) from the model were performed. The fitted parameter was the CO.sub.2 source generation rate [CO.sub.2].sub.gen, and the parameter {dot over (P)} (in units of ppm/hour) was obtained by applying Eq. (12), using λ, [CO.sub.2].sub.i, [CO.sub.2].sub.o, and Q (room ventilation rate)=V (room volume)×λ) as known parameters. Since the number of occupants in the room for growth #1 and #2 were known, an averaged carbon dioxide production rate, VCO.sub.2 per occupant was calculated, using the following expression:

(88) $\begin{array}{cc}\frac{\stackrel{.}{P}}{Q}=\frac{{\mathrm{VCO}}_2}{\sigma \times Q}& \mathrm{Eq}.\left(17\right)\end{array}$
followed by the estimation of an average metabolic rate per person in the room. Table 2 summarizes the findings. The average metabolic rate values were 1511 and 1422 kcal/day, which were in coincidence with the level of metabolic rate estimated from the Mifflin-St Jeor Equation considering average age, average height, average weight, and gender percentage distribution. Although the results did not assess individuals' metabolic rate values, the use of the model to assess metabolic rate from environmental CO.sub.2 detection (particularly Eq. (7a), (9), and (14)) has been demonstrated. As a consequence, similar tests were carried out in cars, hypothesizing that measured CO.sub.2 level changes and the model can also assess metabolic rate from the driver.

(89) TABLE-US-00002 TABLE 2 CO.sub.2 source generation rate and metabolic rate calculation parameters from classroom tests Variables.sup.a Growth #1 Growth #2 Air Change Rate - λ 1.10 hour.sup.−1 0.90 hour.sup.−1 Room Volume 9720 ft.sup.3 = 262,440 dm.sup.3 9720 ft.sup.3 = 262,440 dm.sup.3 Room Ventilation Rate - Q (λ × V) ~9720 ft.sup.3/hour ~9720 ft.sup.3/hour Initial + Outside CO.sub.2 Concentration - 616 ppm 509 ppm [CO.sub.2].sub.i + [CO.sub.2].sub.o CO.sub.2 source generation rate - 410,000 ppm/hour 450,000 ppm/hour [CO.sub.2].sub.gen occupant occupant Fitting Percent Error 6% 6% Metabolic rate Calculation Parameters.sup.b [CO.sub.2].sub.max ~771 ppm ~1216 ppm σ - # of room occupants 18     30     VCO.sub.2 184 mL/min 174 mL/min Metabolic rate 1511 kcal/day 1422 kcal/day .sup.aknown and fitted parameters using Eq. (7a), .sup.bMetabolic rate/calculation parameters, using $\frac{\stackrel{.}{P}}{Q}=\frac{\mathrm{VCO}2}{\sigma \times Q}\mathrm{and}\mathrm{Eq}.\left(16\right).$
Vehicle Tests Under Variable CO.sub.2 Levels

(90) A total of four vehicle tests were conducted. Three of them were performed between the months of March and April 2017, and the final test took place in September 2017. Table 3 shows a summary of the dates of the 3 first tests as well as the testing conditions. These tests were assessed on three consecutive dates where outside temperature and humidity were in a narrow range of 75-83° F., and 25-30% RH, respectively. In all tests, the integrated sensor data in the car seats were used to assure two main points, number of persons in the car, and whether the person(s) was/were talking. The integrated pressure sensor in the seats indicated the presence of one person in all tests, while the noise sensor indicated that the driver of the car was talking while driving.

(91) TABLE-US-00003 TABLE 3 Three of four vehicle tests' testing Date Mar. 11, 2017 Mar. 12, 2017 Mar. 13, 2017 Time of Test 9:33 PM-10:22 PM 9:34 PM-10:09 PM 9:35 PM-10:12 PM Location of Sensor Front middle dashboard Front middle dashboard Front middle dashboard Year, Make, & 2007 Toyota Corolla 2007 Toyota Corolla 2007 Toyota Corolla Model of Vehicle Number of Occupants 1 Occupant 1 Occupant 1 Occupant Occupant Conditions Talking through the Talking through the Talking through the duration of the experiment duration of the experiment duration of the experiment Testing Environment Windows Closed & Windows Closed & Windows Opened No Circulation No Circulation (recirculation) (recirculation) Car Environment Residential Streets Residential Streets Residential Streets

(92) Referring now to FIG. 6A-FIG. 6C, in FIG. 6A an example of a schematic representation of the pressure-based sensor indicating the number of occupants in the car. An occupancy sensor 102 was located on each seat of the car, and had two sections, front and back, that allowed detecting object presence and movement. The movement is important to discriminate between unanimated objects (e.g. bags) on the seat, and a person. FIG. 6B-FIG. 6C each shows a typical trace of sensor signal for both sensor components, which enables discrimination of an object from a person. The sensors were tested in the lab and later integrated in a car. Back (plot B) and front (plot C) sensor signal for detection of an object and a person.

(93) Referring now to FIG. 7A-7C, the CO.sub.2 levels, temperature and relative humidity vs. time recorded during car tests #1, #2, and #3, respectively, while a single occupant was present in the car are shown. Temperature, relative humidity (RH), and CO.sub.2 concentrations vs. time within a 2007 Toyota Corolla while driving in testing conditions shown in Table 3. The left y-axis corresponds to the CO.sub.2 concentration in ppm, while the right y-axis corresponds to the temperature in ° F. and relative humidity in %. Measurements were updated every 30 seconds. A—is a plot of Car test #1 taken on Mar. 11, 2017 from 9:33 PM to 10:22 PM. B—is a plot of Car test #2 taken on Mar. 12, 2017 from 9:34 PM to 10:09 PM. C— is a plot of Car test #3 taken on Mar. 12, 2017 from 9:35 PM to 10:15 PM.

(94) The raw results for the vehicle tests #1 and #2, where windows were closed and ventilation was shut-off, show alternating growths and decays of CO.sub.2 levels. The growths are due to generation of CO.sub.2, and periods of CO.sub.2 decay are observed due to the automatic activation of the ventilation system of the car. It is interesting to notice that in the case of test #1, the ventilation powered on automatically for about 10 minutes after 15 minutes of driving, even though air conditioning was manually set to a level of zero. In the same run, the ventilation system automatically turned on again ˜12 min. later. In the case of test #2, the automatic ventilation system turned on ˜24 min. after the car was started. The decay periods observed in the car test #1 are labeled “decay #1” and “decay #2”. The decay period observed in the car test #2 has been labeled “decay #3”. These CO.sub.2 decay periods occurred while the driver was still inside the car, and therefore, there was a source of significant generation of CO.sub.2. As a consequence, Eq. (4) can no longer be applied to assess ACH. The ACH must be assessed using Eq. (7a), once the CO.sub.2 source generation rate ([CO.sub.2].sub.gen) is determined. Along this line, [CO.sub.2].sub.gen can be determined from the growth CO.sub.2 level periods, assuming the ACH is negligible, and therefore Eq. (19) is applied. It is worth to notice that, in the case of Eq. (19), [CO.sub.2].sub.gen is determined as k.sub.gen (with convenient conversion units, ppm/hour).

(95) Referring now to FIG. 8A-FIG. 8C, graphical representations of the CO.sub.2 concentration vs. time are shown. Eq. (19) is applied to fit the growths from car tests #1 and #2, which has been named “growth #1” and “growth #2” for the car test #1, and “growth 3” for the car test #2. The fitted parameter is k.sub.gen to assess CO.sub.2 source generation rate from Eq. (19) for: A—“Growth #1” that occurred from 9:33 to 9:49 PM in car test #1, B—“Growth #2” that occurred from 9:59 to 10:10 PM in car test #1, C-“Growth #3” that occurred from 9:35 to 9:57 PM in car test #2.

(96) Table 4 shows a summary of the calculation of metabolic rate, resulting from the analysis.

(97) TABLE-US-00004 TABLE 4 CO.sub.2 source generation rate and metabolic rate from car tests Metabolic rate Calculation Growth #1 Growth #2 Growth #3 Average (SD) Outside + Initial CO.sub.2 735 ppm 1557 ppm 1744 ppm n/a Concentration − [CO.sub.2].sub.o + [CO.sub.2].sub.i k.sub.gen = [CO.sub.2]/hour 5350 ppm/hour 3454 ppm/hour 4348 ppm/hour 4384 (948) ppm/hour R.sup.2 - regression coefficient 0.895 0.995 0.940 n/a Fitting Percent Error (average 6.1% 0.9% 3.3% n/a Absolute error for each data point) Car Volume 90.3 ft.sup.3/2438 dm.sup.3 90.3 ft.sup.3/2438 dm.sup.3 90.3 ft.sup.3/2438 dm.sup.3 90.3 ft.sup.3/2438 dm.sup.3 σ - # of room occupants 1 1 1 1 VCO.sub.2 (with t = 60 min) 176(35) mL/min Estimated metabolic rate 1433(285) kcal/day Measured metabolic rate (Breezing ™) 1510 kcal/day Difference Error 5%

(98) With the empirically determined k.sub.gen (ppm/min) from car test #1 and #2, which rendered an average of 4383 ppm/hour or 73.1 ppm/min. VCO.sub.2 (mL CO.sub.2/min) was determined with the following procedure: VCO.sub.2 (mL CO.sub.2/min)=k.sub.gen (ppm/min)×V (room volume) (2438 dm.sup.3)=176.3 mL CO.sub.2/min, and VCO.sub.2 can be directly applied to Eq. (13) to assess metabolic rate, which rendered a value of 1433 kcal/day.

(99) Referring now to FIG. 9A-FIG. 9D, experimental data including CO.sub.2 concentration vs. time is graphically shown. The fitted parameter is ACH to assess the automatic ventilation in the car when the ventilation system was voluntarily set at shut-off conditions. Eq. (7a) is used for the assessment, utilizing [CO.sub.2].sub.gen assessed under no ventilation conditions (FIG. 6): A—“Decay #1” that occurred from 9:52 to 9:58 PM in car test #1, B—“Decay #2” that occurred from 10:11 to 10:20 PM in car test #1, C—“Decay #3” that occurred from 9:59 to 10:05 PM in car test #2, D—Decay #4 that occurred from 9:35 to 9:38 PM in car test #3.

(100) Using the k.sub.gen determined before, the ACH for the automatic ventilation in the car was assessed. FIG. 9A-FIG. 9C show the fitting of decay #1, decay #2, and decay #3 for car test #1 and #2, respectively, using the Eq. (7), and Table 5 summarizes the results, indicating that the average ventilation rate is (9.6±0.9) hour.sup.−1 as well as other parameters resulting from the fitting results from experimental values to the model. For first time, the automatic ventilation rate of a car at recirculation conditions (AC max mode—ventilation system shut-off conditions) has been assessed. This mode of ventilation is set in the cars to avoid excessive build-up of CO.sub.2 [29].

(101) The third vehicle test was conducted on Mar. 11, 2017 from 9:35 PM to 10:12 PM. The testing conditions are noted on Table 3. The CO.sub.2 concentrations vs. time are graphically displayed in FIG. 9D, as well as the corresponding fitting to Eq. (7), utilizing an averaged k.sub.gen of 4384 ppm/hour assessed from the driver under no ventilation conditions. As it can be observed in Table 5, the air change rate was ˜69.5 hour.sup.−1, which is ˜7 times higher than at closed window and recirculation condition due to the fact the windows were opened.

(102) TABLE-US-00005 TABLE 5 CO.sub.2 source generation rate and calculation parameters from car tests Decay #1, Decay Decay Decay #2, and #3 Decay Variables #1 #2 #3 Average(SD) #4 Conditions Windows closed and recirculation on Windows open Air Change Rate - λ 9.4 hour.sup.−1 10.5 hour.sup.−1 8.8 hour.sup.−1 9.6 ± 0.8 hour.sup.−1 69.5 hour.sup.−1 Car Volume 90.3 ft.sup.3/2438 dm.sup.3 90.3 ft.sup.3/2438 dm.sup.3 90.3 ft.sup.3/2438 dm.sup.3 90.3 ft.sup.3/2438 dm.sup.3 Room Ventilation 22,132 dm.sup.3/hour 24,690 dm.sup.3/hour 20,734 dm.sup.3/hour 163,234 dm.sup.3/hour Rate - Q Initial [CO.sub.2].sub.i 1220 1473 1540 425 [CO.sub.2].sub.max 1956 2208 3283 894 Baseline concentration - 735 ppm 735 ppm 1744 ppm 469 ppm Average absolute error 1.2%  1.3%  2.2%  6.3%  for each data point R.sup.2 0.9985 0.9857 0.8685 0.8856 Average percentage 20% 22% 33% ~0% significance of [1 − exp(−λt)] term Average percentage 80% 78% 67% ~100%  significance of [exp(−λt)] term σ - # of room occupants 1 1 1 1

(103) In order to assure that the ACH is null in a closed ventilation setup and that there is no CO.sub.2 generation being introduced in the car cabin from the car's combustion engine, a fourth test was performed as a control experiment with the car engine on, and the ventilation system closed.

(104) Referring now to FIG. 10, parameters of temperature, relative humidity (RH) and carbon dioxide levels are shown for the fourth test. Temperature and relative humidity were similar to the previous car tests, and the carbon dioxide levels did not change significantly from the baseline level recorded right before the car test.

(105) A new method of tracking the metabolic activity of individuals in confined environments using a sensor array to measure exhalation rate of CO.sub.2 and subsequent modeling the individual's metabolic rate using the Weir equation has been disclosed herein. The model also allowed for the assessment of the indoor air quality of a room or vehicle in addition to the quantification of the metabolic rate of an individual or group. Experimental results from the classroom test show that the average metabolic rate from the two classes taken was 1511 kcal/day and 1422 kcal/day which are within ˜5% of the 1500 kcal/day average expected for the group of students [6]. The classroom scenario validates the accuracy of model and techniques employed to determine the CO.sub.2 source generation rate. The vehicle tests yielded metabolic rate average of 1433 kcal/day with a 5% error with respect to a reading from a validated mobile indirect calorimeter (Breezing®), which assessed a metabolic rate of 1510 kcal/day.

(106) It is important to note that 5% discrepancy is an acceptable level for clinically relevant evaluation. It is also important to notice that the variability between assessments was relatively large (˜20%), given that the subject may have faced different metabolic rates set up by free-living conditions factors. The variability of a person's metabolic rate can be affected by numerous environmental factors such as daily stress, sleep, diet, medications, physical activity, and even exposure to chemicals, pollutant and weather factors. In this regard, the assessment of metabolic rate measurements under free-living conditions is valuable for observing daily variability, as well as averaging higher resolution measures to assess more representative metabolic rate values. In this regard, the present method uses proven validity in a previous study performed under controlled conditions. The previous study probed that the assessment of metabolic rate from prolonged testing of carbon dioxide production rate in intubated patients is a good surrogate and replacement of more expensive assessment performed with Gold Standard Indirect calorimetry Instrument Parvo Medics [25].

(107) In addition, the present study has revealed that indoor CO.sub.2 concentrations are consistently above 600 ppm in both classroom and vehicle settings and therefore, there is an imminent need of detection of metabolic rate under free-living conditions with good maintenance of indoor air quality. The threshold value at which indoor occupants begin to experience SBS related symptoms and illnesses is at or above 600 ppm. Concentrations in vehicle settings reached levels as high as 3000 ppm and rarely dipped below 600 ppm. Drivers subjected to these conditions for extended periods of time may experience fatigue, drowsiness, and loss of focus [30]. This needs is the motivation for the present invention.

(108) Vehicle Tests Under Constant CO.sub.2 Levels

(109) One additional setting was used to probe the assessment of metabolic rate in a car cabin, while maintaining constant CO.sub.2 levels by an actuated ventilation previous calibrated for air change rate. The car (Hyundai. Electra) cabin's ventilation system was pre-calibrate for conditions with AC on, and recirculation of air off. Table 6 summarizes the corresponding ACH values.

(110) TABLE-US-00006 TABLE 6 ACH values from vehicle tests providing pre-calibrated ventilation conditions to the system. Ventilation setting Air Change Rate, λ (hour.sup.−1) 1 0.92 2 0.43 3 0.70 4 0.61

(111) Referring now to FIG. 11A-FIG. 11C, the data shown there illustrates an example of an algorithm employed in a method and system for assessing metabolic rate and maintaining indoor air quality using passive environmental sensors. The automatic ventilation condition was pre-set to have CO.sub.2 levels between 650-900 ppm, and a pre-calibration of ACH was used for assessment of VCO.sub.2, and metabolic rate. The CO.sub.2 shows the period of time the ventilation was turned “on” to maintain a healthy IAQ level.

(112) The system changes the ventilation setting with pre-calibration for air change rate to maintain a constant level of CO.sub.2, and determined ΔACH. Eq. (7b)-(9), Eq. (13) and related expressions are used to assess [CO.sub.2].sub.gen, VCO.sub.2, and metabolic rate. This disclosure along with its related disclosed features demonstrate the capacity of the system to assess metabolic rate, which is distinctive from previous reported inventions and general knowledge of someone skilled in the art [4b].

(113) With the ability to determine the CO.sub.2 level, and the previous assessed metabolic rate for the individual, a ventilation system could realistically forecast CO.sub.2 concentrations in a closed environment and accordingly adjust the rate of ventilation to prevent CO.sub.2 build-up. Based on the levels predicted, ventilation systems could be activated to raise ACH values by increasing supply of air entering the specific room.

(114) Referring now to FIG. 12, a block diagram illustrating an example of an algorithm employed in a method and system for assessing metabolic rate and maintaining indoor air quality using passive environmental sensors is shown. The algorithm may be embodied in a software program 800 including the acts of receiving sensor data 802, identifying the presence of a subject or subjects 804, setting a constant CO.sub.2 level as a goal for the ventilation system to maintain 806, setting a first ACH 808, measuring a first CO.sub.2 level 810, re-adjusting ACH (ACH2) to meet the CO.sub.2 level goal 808, measuring a second CO.sub.2 level 810, calculating the metabolic rate 805.

(115) The Auto41 system automatically detects metabolic rate as soon as the occupancy sensor in the confined environment detects the sole person that daily uses the location. Similarly to the Doubly Labeled Water Method [1], Auto41 relates the metabolic rate to a respiratory quotient of 0.85, given by the following ratio:

(116) $\begin{array}{cc}RQ=\frac{{\mathrm{VCO}}_2}{{\mathrm{VO}}_2},& \left(\mathrm{Eq}.14\right)\end{array}$
and calculates metabolic rate using:
VO.sub.2═VCO.sub.2/0.85  (Eq. 20)
and the simplified Weir equation:

(117) $\begin{array}{cc}\mathrm{Metabolic}\mathrm{Rate}\left(\frac{\mathrm{kcal}}{\mathrm{day}}\right)=8.19\left({\mathrm{VCO}}_2\left(\frac{\mathrm{mL}}{\min }\right)\right).& \left(\mathrm{Eq}.16\right)\end{array}$
Auto41 passively assesses VCO.sub.2 under free-living conditions using at least one environmental sensor of CO.sub.2 and a model integrated into an intelligent algorithm in a confined environment. To the best of our knowledge, this is the first report of an alternative option to Doubly Labeled Water Method for assessment of metabolic rate in real-world living with (at the least) a daily measure with capability of maintaining CO.sub.2 in a healthy range. Furthermore, the system can be free of user periodic calibration.

(118) In one useful example, an integrated method for identification of occupants in a room based on energy-efficient IAQ, and biometrics 800 includes assessing room occupancy with at least one physical sensor 802, outputting an assessment of overall metabolic rate for multiple users or outputting an individual assessment of an individual's metabolic rate for a single user, setting an actuated ventilation system to a constant CO.sub.2 level in a predetermined healthy range 806, where the actuated ventilation system includes at least one CO.sub.2 sensor. After the level is set for a predetermined healthy range, then the actuated ventilation system is operated to measure a first air ventilation rate (ACH1) 808. Then the at least one CO.sub.2 sensor to measures a first CO.sub.2 level 810. Next, the actuated ventilation system determines if the CO.sub.2 level is in a predetermined value within healthy range of 600-1000 ppm, if not then it supplies a signal to the actuated ventilation system to drive the CO.sub.2 level into the predetermined healthy range. The actuated ventilation system calculates or measures a subsequent air ventilation rate (ACH2) 808 and operates the at least one CO.sub.2 sensor to measure a subsequent CO.sub.2 level 810. The actuated ventilation system determines if the CO.sub.2 level is at the predetermined healthy range and if it is then determining an assessment of change of air ventilation rate (ΔACH), otherwise repeating acts 808-814 as detailed above.

(119) In one example, the method further includes recognizing a particular subject 804 by operating a physical sensor where the physical sensor is selected from the group consisting of a weight sensor, a biometric sensor and combinations thereof.

(120) Referring now to FIG. 13, a method for learning to recognize a particular subject is shown in block diagram form. The act of learning from recognizing the subject to improve accuracy 804 includes determining whether the subject was identified correctly 910, if not, then entering the correct subject 914. Once the correct subject is identified than recalculating and storing physical parameters related to the physical sensors to better identify the subject on the next pass, 916. Then the algorithm continues by returning to step 805. For example, the identification of the individual recognized may be displayed to a person in the room or vehicle. If it is incorrect, the person may input the correct identification into the application as by activating a keyboard, button, icon or the like.

(121) Referring now to FIG. 14, a more detailed schematic of the sensor array is shown. In still another example, the at least one CO.sub.2 sensor 15 mitigates drift by operating a reference channel 132, using a CO.sub.2 non-absorbing wavelength, and a sensing channel using a CO.sub.2 maximum absorbing wavelength, and signal 133 resulting from combining data signals of both channels.

(122) In another example, the CO.sub.2 sensor further comprises in-situ temperature measurement for corrections of CO.sub.2 level readings, and an auto-calibration algorithm for accurate CO.sub.2 readings over time.

(123) Referring now to FIG. 15, the relationship between the effective air exchange rate (A) inside a car cabin assessed with a single occupant (driver) and driving speed in miles per hour (MPH) is shown. Eq. (7) and related ones were used to assess A. The line indicates linear regression curve above 17 MPH. The assessment of A was performed analyzing the carbon dioxide profiles obtained with the driver driving at different speeds, including parked conditions.

(124) Referring now to FIG. 16, the metabolic rate as EE (energy expenditure, kcal/day) on the right, and the model fitted CO.sub.2 generation rate (K.sub.gen) on the left is shown. The data shown in FIG. 16 were extracted from the carbon dioxide patterns shown in FIG. 17A-FIG. 17D. S

(125) Still referring to FIG. 15 and FIG. 16, certain exemplary embodiments of the invention can be used inside a car or any other confined space. FIGS. 15 and 16 show examples of air exchange rate, and metabolic rate values assessed inside the car cabin with a single occupant (driver) at different speeds. Both car's air exchange rate and driver's metabolic rate can be assessed with this invention's system, model, and algorithms. As shown in FIG. 15, the system allowed to determine that the air exchange rate increases with the car speed. In addition, the metabolic rate values shown in FIG. 16 are accurate values representing the metabolic rate values assessed by independent methods (metabolic instrument) on the same individual (driver).

(126) Referring now concurrently to FIG. 17A-FIG. 17D, real-time CO.sub.2 concentration profile and the model fitted curves under different driving speed of a) 0 MPH; b) 16 MPH; c) 37 MPH; d) 64 MPH are shown respectively. The CO.sub.2 concentration was kept under 1100 ppm by alternating recirculation mode on and off, and keeping the ventilation level of 1 (minimum ventilation energy consumption). These patterns were obtained under controlled alternating air recirculation and ventilated conditions, which enabled controlling air quality inside the car cabin. Since the ventilation conditions were maintained at 1.sup.st (minimum) level, the ventilation energy was minimized.

(127) Referring now jointly to FIG. 18A-FIG. 18B a vehicle used in assessment of data shown in FIG. 15 thru 17, an AC control panel of the car and different ventilation condition options of a vehicle are respectively shown. Referring specifically to FIG. 18C an example of car used for the tests shown in FIGS. 15-17, and summarizes the different car's ventilation modes are shown. The ventilation modes include: mode #1 air recirculation (ON) with no ventilation, mode #2 alternating air recirculation with ventilation, and mode #3 alternating ventilation, with no recirculation. For the sake of safe carbon dioxide exposure, ventilation energy minimization, and biometrics extraction, mode #2 is the mode of choice for the presently disclosed method.

(128) Referring now to FIG. 19A-FIG. 19D, different ventilation conditions at speeds from zero MPH, 33 MPH, 49 MPH and 71 MPH are shown respectively. For example, FIG. 19A shows carbon dioxide concentration in parts per million (PPM) for a time period of 0 to 15 minutes for occupants from 1 through 5 AT ZERO MPH. Similarly, FIG. 19B, shows similar data for 33 MPH. Similarly, FIG. 19C, shows similar data for 49 MPH. Similarly, FIG. 19C, shows similar data for 49 MPH. Similarly, FIG. 19D, shows similar data for 71 MPH.

(129) Referring now to FIG. 20A and FIG. 20B, simulated CO.sub.2 concentration growth profiles inside a car cabin (recirculation mode on) with different number of occupants under various speeds, and realistic air exchange rates (FIG. 15) are shown. In FIG. 20B a horizontal line has been drawn at both 1000 and 2500 ppm with a label to indicate the corresponding time at which a vehicle with a single occupant reaches the aforementioned CO.sub.2 concentration. As mentioned previously, CO.sub.2 accumulation at these levels can have effects on driver safety since at 1000 ppm a person's cognitive function begins to decline and the effect is more severe at 2500 ppm. The simulation predicts that CO.sub.2 levels within the car cabin reaches 1000 ppm for a single occupant in 15 minutes with RC mode on, regardless of vehicle speed. The risk of CO.sub.2 accumulation to dangerous levels is significantly higher for car cabins where there is more than 1 occupant; this is clearly evidenced in the simulation's output where CO.sub.2 levels exceeding 2500 ppm are reached in under 15 minutes when the vehicle is occupied with at least 3 occupants, regardless of vehicle speed. This result highlights the importance of consistent CO.sub.2 concentration control to ensure that indoor air quality is always maintained with respect to CO.sub.2.

(130) Referring now specifically to FIG. 20A, an example of how a CO.sub.2 profile can be affected by changing the effective air exchange rate from 1 to a higher value, up to 22 h.sup.−1, e.g. by alternating the recirculation (RC) mode on and off, and/or opening windows is shown. The effect of different fan levels (with corresponding λ values extrapolated from experimental data) were simulated using the aforementioned model. To keep the CO.sub.2 concentration within a vehicle at a level that does not affect the driver's cognitive abilities, it is necessary to increase λ. At higher fan levels the CO.sub.2 concentration decreases faster to safer levels.

(131) Referring now specifically to FIG. 20B an example of the effect of occupant metabolic rate on CO.sub.2 accumulation within a vehicle is demonstrated. Clearly, occupant metabolic rate has a substantial influence on the CO.sub.2 concentration growth profile. A driver with a relatively high EE of 2500 kcal/day will reach a CO.sub.2 concentration of 1000 ppm in just 6.8 minutes, as compared with virtually twice as long (13.7 min) for a driver spending only 1300 kcal/day. The high-EE driver can reach a CO.sub.2 concentration of 2500 ppm in less than half an hour.

(132) It is important to mention that the application of the present invention is very relevant to confined environments, small rooms and cabins in general such as those in offices, airplanes, cars, trucks, enclosed small vehicles, space shuttles. The accumulation of carbon dioxide over time can be significant in short periods of time compromising the cognitive performance of the individuals located inside the confined environments with limited air exchange. FIG. 19 shows a set of simulations performed with the model presented here, when there is no active actuation of air circulation, which promote carbon dioxide build up. It can be observed that even, when a vehicle such as the car, increases speed, increasing the air exchange rate; it takes only few minutes to reach dangerous carbon dioxide levels (higher than 1000 ppm) for an average person's metabolic rate (1300 kca/day). The use of the invention as proposed in this application would avoid such a problem, since it can actively control the ventilation to set back the carbon dioxide levels at a healthy stage (FIG. 20a). This is critically important since people with higher metabolic rate can reach critical exposure to unhealthy carbon dioxide concentrations in shorter periods of time (FIG. 20b). The capability of assessing metabolic rate, managing air exchange, and intelligently controlling air ventilation while minimizing energy are unique features of this disclosed invention.

(133) Certain exemplary embodiments of the invention have been described herein in considerable detail in order to comply with the patent Statutes and to provide those skilled in the art with it the information needed to apply the novel principles of the present invention, and to construct and use such exemplary and specialized components as are required. However, it is to be understood that the invention may be carried out by different equipment, and devices, and that various modifications, both as to the equipment details and operating procedures, may be accomplished without departing from the true spirit and scope of the present invention.

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