Systems and methods for measuring mean radiant temperature

Abstract

System and methods for determining mean radiant temperature (MRT) of a room. The system includes contactless thermal sensor device and a processor programmed to determine MRT of the room based upon data from the sensor device. The sensor device can be or include an IR thermal array sensor configured to generate 2D images (e.g., low-resolution). 3D surface temperature data is extracted from the 2D thermal images, for example through homographic transformation, where coordinates are multiplied by transformation matrices. Designated or known points in the 2D thermal images of the room are correlated with corresponding points in a frontal view of the room to calculate the homography matrices. In some examples, the known points are determined as part of a location-based calibration process, for example by marking or tagging known locations of the room.

Description

[0001] SYSTEMS AND METHODS FOR MEASURING MEAN RADIANT TEMPERATURE Background

[0001] The present disclosure relates to measuring mean radiant temperature. More particularly, it relates to systems and methods for determining the mean radiant temperature of a room or other space via obtained thermal data.

[0002] Measuring and controlling human thermal perception-related parameters within an indoor or enclosed environment is crucial for ensuring occupant comfort, productivity, well-being, and reduced energy consumption. The human body is sensitive to both convective and radiative thermal effects. Convective effects are mainly attributed to air temperature, and radiative effects stem from surrounding surface temperatures (e.g., wall and window temperatures). Mean radiant temperature (MRT) is used to quantify the exchange of radiant heat between a human and their surrounding environment. MRT can be defined as the theoretical uniform surface temperature of an enclosure in which an occupant would exchange the same amount of radiant heat as in the actual non-uniform enclosure and represents the comprehensive radiant thermal impact perceived by individuals in their surroundings. The radiant exchange is an important component of the thermal comfort that will be experienced by a person, particularly in places where there may be significant differences in radiant and air temperatures, for example, near a large window.

[0003] Given the marked impact of radiant exchange on occupant comfort and energy consumption, the measured MRT of a location can greatly benefit home / building HVAC design and control. Unfortunately, no feasible, robust, cost-effective and ergonomic systems or methods exist for real-time MRT measurements in a built environment. One accepted practice measures MRT using a black globe sensor or thermometer (a black globe with a temperature sensor probe placed in the center). The black globe sensor does not actually measure surrounding temperatures; instead, the internal sensor simply outputs the mean temperature of the black globe surrounding it. Thus, a black globe thermometer does not readily provide information about the MRT of multiple parts of a location. As such, to capture information about a space at a given point in time, multiple black globe sensors are necessary. Black globe sensors are open to convective errors and require coupling with air velocity measurement and a correction process that is expensive, adding on the order of one thousand dollars to the cost of measurement. Further, these sensors have a slow response time and are recommended to conduct measurements after 15 or 30 minutes of thermal equilibrium. Moreover, the large globes are bulky and not aesthetically pleasing.

[0004] To eliminate the air velocity measurement requirement of black globe sensors, other techniques have been explored based on a similar principle with some modifications. One approach involves using two identical spheres, one black and the other polished. Both sensors are heated to the same temperature to expose them to the same convective heat loss. The difference in heat supply, which is the measure of radiation, is then used to estimate the mean radiant temperature. Another approach utilizes a single heated sensor (sphere or ellipsoid) heated or cooled to the same temperature as the surrounding air to eliminate convective heat gain or loss. The power consumption of the sensor serves as a measure of radiant heat exchange.

[0005] From the above, both black globe and heated sensor approaches have size, practicality, and response time limitations. A more accurate but expensive approach is the use of net radiometers taking readings across a spectrum of wavelengths at a given location. Originally developed for outdoor thermal radiation measurement, net radiometers have also been employed in research studies for indoor measurements. Net radiometers consist of four components: two upward-facing and two downward-facing radiometers. One set measures longwave thermal radiation (pyrgeometer), while the other set measures shortwave thermal radiation (pyranometer). However, net radiometers require directional adjustments to cover all six cardinal directions (either three pairs of net radiometers or six pairs of pyranometers and pyrgeometers). Moreover, the net radiometer is also a point sensor and needs to be placed at the point of interest. In addition, their costs and directional dependency make them less accessible, even for research purposes.

[0006] From the above, there is a lack of affordable, robust and reliable solutions for measuring MRT, which poses a problem for both research studies and daily use in the built environment. These measurements can be crucial for assessing and controlling the thermal comfort level of spaces, as well as maximizing the operating efficiency of heating and cooling systems. Summary

[0007] The inventors of the present disclosure recognized that a need exists for improved systems and methods for measuring the mean radiant temperature of a room or other space.

[0008] Some embodiments of the present disclosure are directed to a system for determining the mean radiant temperature of a room. The system includes at least one contactless thermal sensor device and at least one processor programmed to determine a mean radiant temperature of the room based upon data from the contactless thermal sensor device. In some examples, the contactless thermal sensor device is or includes an IR thermal array sensor configured to generate 2D images (e.g., low-resolution, 2D images). With these and related embodiments, 3D surface temperature data is extracted from the 2D thermal images, for example through homographic transformation, where coordinates are multiplied by transformation matrices. In this regard, designated or known points in the 2D thermal images of the room from the contactless thermal sensor device are correlated with corresponding points in a frontal view of the room to calculate the homography matrices. In some examples, the known or designated points are determined as part of a location-based calibration process, for example by marking or tagging corners or other known points of the room (that are otherwise in a field of view of the contactless thermal sensor device). With these and related embodiments, the complexity of the transformations is reduced and the accuracy of the mean radiant temperature determinations is increased. Brief Description of the Drawings

[0009] FIG. 1 is a block diagram of a mean radiant temperature measuring system in accordance with the principles of the present disclosure;

[0010] FIG.2A is a simplified top view of a room in which a contactless thermal sensor device of the system of FIG.1 has been installed;

[0011] FIG.2B is a simplified side view of the arrangement of FIG.2A;

[0012] FIG.3A is a simplified view of the room of FIGS.2A and 2B from a perspective of the contactless thermal sensor device;

[0013] FIG.3B is a simplified representation of the field of view of FIG.3A;

[0014] FIG. 4A is an unfolded representation of portions of the room of FIGS. 2A and 2B, illustrating a front view of the walls that are otherwise in a field of view of the contactless thermal sensor device;

[0015] FIG.4B designates the areas of the walls of FIG.4A that are in the field of view of the contactless thermal sensor device;

[0016] FIG.5A is a simplified view of the room of FIGS.2A and 2B from a perspective of the contactless thermal sensor device and indicating known points;

[0017] FIG.5B is an unfolded representation of the room of FIGS.2A and 2B and indicating the known points of FIG.5A;

[0018] FIG.6 schematically represents a homography transformation useful with systems and methods of the present disclosure;

[0019] FIG.7A is a sample thermal image generated by a low-resolution infrared (IR) thermal array sensor installed in a room;

[0020] FIG. 7B is another sample image generated by the same IR thermal array sensor installed in the same location of the same room as FIG. 7A, with tagging devices installed in corners of the room;

[0021] FIG. 7C is another sample image generated by the same IR thermal array sensor installed at the same location in the same room as FIG. 7A and identifying thermal data for walls of the room in the image;

[0022] FIG.8 illustrates an example method in accordance with the principles of the present disclosure;

[0023] FIG.9A-9C are simplified top-view representations of portions of other mean radiant temperature measuring systems in accordance with principles of the present disclosure installed to a room;

[0024] FIGS.10-13 depict the environment of various testing performed and described in the Examples section; and

[0025] FIG.14 is a plot of testing results described in the Examples section. Detailed Description

[0026] The present disclosure relates to systems and methods for determining or measuring mean radiant temperature (MRT), for example MRT at a point of interest in an enclosed area such as a room of a home or building. One example of a system 20 in accordance with principles of the present disclosure, and useful for performing methods of the present disclosure, is shown in block form in FIG. 1. The system 20 includes at least one contactless thermal sensor device 30, a processor 32, a memory 34, an input device 36, a display device 38, and a power supply 40. In some embodiments, the components 30-40 can be contained by a single housing (represented by dashed lines in FIG.1). In other embodiments, some components of the system 20 can be housed separately (e.g., the contactless thermal sensor device 30 can be a standalone component that communicates (wired or wireless connection) data to a separate device that includes the processor 32). In yet other embodiments, systems and methods of the present disclosure can include two or more contactless thermal sensor devices 30; with these and related embodiments, individual ones of the multiple contactless thermal sensor devices 30 can be installed in different or discrete locations of a room or can be installed at the same general location and oriented in different directions relative to the room. Details on the various components are provided below. In general terms, at least the contactless thermal sensor device 30 is installed to a fixed location of a room (or other enclosure) and operates to capture two-dimensional (2D) thermal images that are provided to the processor 32. The processor 32 is adapted (e.g., by executing program code stored as software in the memory 34) to extract three dimensional (3D) surface temperature data from the 2D thermal images (represented by a transformation module 42) and to determine the MRT (represented by a thermal model module 44) based upon the mapped thermal data and given or known room dimensions. MRT determinations can be generated for a point of interest of the room, as selected, for example, by a user via the input device 36. The determined or measured MRT can be conveyed to a user via the display 38. The system 20 can optionally include one or more additional sensor-type components 46 as described below. Regardless, with the systems and methods of the present disclosure, 2D low-resolution thermal images generated by a relatively low-cost, stationary optical sensor device (e.g., an infrared thermal array sensor) enable real-time or instantaneous MRT measurement. The systems and methods of the present disclosure can be utilized in various fashions, for example integrated into a built environment (e.g., integrated with room thermostats and smart building system). The systems and methods of the present disclosure can optionally be configured to determine or generate comfort-related metric(s) in addition to MRT.

[0027] The processor 32 can be a microprocessor, an embedded microprocessor, an embedded controller, a digital signal processor (DSP), etc. The processor 32 is configured to execute program code stored as software in memory 34. The program code, when executed by the processor 32, causes the processor 32 to implement the 3D surface temperature data extraction and MRT determination functions described herein (as represented by the transformation module 42 and the thermal model module 44). The processor 32 can reside in any suitable computing equipment, such as a personal computer, laptop, mobile electronic device, server or cloud-based computational platform. The processor 32 can further cooperate with the memory 34 to store data. The computer-implemented methods of the present disclosure operate one or more algorithms or equations described below.

[0028] The contactless thermal sensor device 30 is configured to detect and measure infrared radiation emitted by object(s), allowing for non-contact temperature measurements and thermal imaging. In some examples, the optical sensor device 30 is or includes at least one infrared (IR) thermal array sensor (e.g., an IR non-contacting thermal array sensor) and can be, or can be akin to, a thermal camera. These sensors consist of a two-dimensional array of individual infrared detectors, which are sensitive to specific wavelengths of infrared radiation. The radiation emitted by a target object is focused onto the detector array using specialized optics, such as lenses and mirrors. Each detector element in the array generates an electrical signal proportional to the incident infrared radiation, creating a thermal map of the target object’s surface. In some embodiments, the contactless thermal sensor device 30 that incorporates of an IR thermal array sensor is characterized by outputting a low-resolution image (e.g., is or includes a low-resolution wireless IR array sensor). As a point of reference, IR thermal array sensors come with different characteristics and different costs. IR thermal array sensors capture the heat emitted by any heat source and map it into a low-resolution matrix which can be seen as an image. Low-resolution can be considered a resolution of less than 64 x 64 pixels (e.g., 32 x 32 pixels, 32 x 24 pixels, 16 x 16 pixels, 8 x 6 pixels, etc.). Low-resolution IR thermal array sensors are significantly less expensive as compared to IR thermal array sensors with relatively high resolution. In other embodiments, the contactless thermal sensor device 30 can incorporate a higher resolution sensor.

[0029] In some embodiments, the contactless thermal sensor device 30 includes one IR non- contacting thermal array sensor maintained within a housing. In other embodiments, the contactless thermal sensor device 30 includes two or more IR non-contacting thermal array sensors maintained within a housing. Regardless of the exact format of the sensor(s), the contactless thermal sensor device 30 can include one or more additional components, such as a housing, optical components (e.g., lenses, mirrors, etc.), a controller, communication (wired or wireless communication), etc.

[0030] As mentioned above, the 2D thermal images captured by the contactless thermal sensor device 30 are utilized by the systems and methods of the present disclosure to determine or measure MRT. In this regard, once fixed within the room of interest, the contactless thermal sensor device 30 captures thermal images of surfaces of the room within the field of view of the sensor(s) of the contactless thermal sensor device 30. For example, FIGS. 2A and 2B depict, in simplified form, the contactless thermal sensor device 30 installed to a room 50. The room 50 includes or is defined by a floor or ground 60, a ceiling 62, a first wall 64 opposite a second wall 66, and a third wall 68 opposite a fourth wall 70. Windows 72 are formed in the second wall 66, and a door 74 is provided in the fourth wall 70. With the non-limiting example of FIGS.2A and 2B, the contactless thermal sensor device 30 has been installed to the first wall 64 approximately mid-way between the floor 60 and the ceiling 62 and utilizes a single sensor (e.g., a single IR thermal array sensor). With this arrangement, and from the perspective or field of view of the sensor of the contactless thermal sensor device 30, the second wall 66 can be considered or viewed as the front wall, the third wall 68 can be considered or viewed as the left side wall, and the fourth wall 70 can be considered or viewed as the right side wall.

[0031] With the above example arrangement, a field of view 80 of the sensor of the contactless thermal sensor device 30 includes an entirety of the second (or front) wall 66, along with portions of the floor 60, the ceiling 62, the third (or left side) wall 68, and the fourth (or right side) wall 70. For example, FIG. 3A schematically illustrates a two dimensional (2D) view of the room 50 from the perspective of the sensor of the contactless thermal sensor device 30 (as otherwise installed to the first wall 64 as in FIGS.2A and 2B). A perimeter of the field of view 80 is shown with a thickened line. The 2D thermal data from this field of view will contain temperature information of not only the front wall 66, but also a portion of each of the floor 60, the ceiling 62, the left side wall 68, and the right side wall 70. By way of further clarification, FIG.3B depicts the field of view 80 alone (i.e., those portions of the room 50 of FIG.3A that are not otherwise in the field of view 80 are not included in FIG. 3B). The representation of FIG.3B reflects the complete extent or perimeter of thermal images that will be collected for the room 50 by the single sensor of the contactless thermal sensor device 30 (as otherwise installed to the first wall 64 as in FIGS.2A and 2B).

[0032] While the data or thermal image generated by or from the contactless thermal sensor device 30 as in the view of FIGS. 3A and 3B are two-dimensional (2D), it also represents the thermal distribution of a three-dimensional (3D) space. A plane of the front wall 66 is perpendicular to the field of view 80; the planes of the floor 60, the ceiling 62, the left side wall 68, and the right side wall 70 are not perpendicular to the field of view 80, but portions thereof are still visible to the optical sensor device 30. Thus, for example, the portion of the floor 60 that is in the field of view 80 represents data in a plane differing from that of the front wall 66 (and the other walls). With this in mind, FIG. 4A is an “unfolded” representation of the room 50 relative to the first wall 64 (FIGS. 2A and 2B), and presents a front or frontal view of each of the floor 60, the ceiling 62, the front wall 66, the left side wall 68, and the right side wall 70. Continuing the above example, shading in FIG.4B identifies the portions of each of the walls 60, 62, 66, 68, and 70 that are in the field of view 80, further clarifying that the contactless thermal sensor device 30 (as otherwise installed to the first wall 64 as in FIGS. 2A and 2B) is capturing data of a 3D space. In some embodiments, temperature data of the room 50 useful for determining MRT can be obtained by understanding or extracting 3D surface temperature information from the 2D data or thermal images generated by the contactless thermal sensor device 30.

[0033] 3D surface temperature data can be extracted from the 2D thermal images of the contactless thermal sensor device 30 in various manners, and in some embodiments entails homographic (projective) transformation performed by the transformation module 42 (FIG. 1) to correlate the observed field of view 80 of the sensor of the contactless thermal sensor device 30 (i.e., FIG.3B) with a front or frontal view of the walls of the room (i.e., FIG.4B). With some examples, coordinates are multiplied by a transformation matrix, specifically referred to as the homography matrix in this context. Because homography transformation has eight degrees of freedom, a minimum of four corresponding points on two images can be employed to calculate the homography matrix. By way of further explanation, with the arrangement of FIGS. 3B and 4B in which the sensor of the contactless thermal sensor device 30 has captured views of five different surfaces or walls (i.e., the floor 60, the ceiling 62, the front wall 66, the left side wall 68, and the right side wall 70, where the floor 60 and the ceiling 62 can also be considered or termed as a “wall”), five distinct homographic transformations are implicated, each associated with a unique homography matrix.

[0034] With some systems and methods of the present disclosure, projective geometric transformation algorithms incorporate or utilize known or marked locations in the field of view of the sensor(s) of the contactless thermal sensor device 30. These designated location coordinates can be determined as part of calibration process that need only be performed once during installation of the contactless thermal sensor device 30 to the room 50 as described below. Continuing the non-limiting example described above with respect to FIGS. 2A and 2B in which the contactless thermal sensor device 30 has been installed to the first wall 64, with the field of view 80 of the sensor of the contactless thermal sensor device 30 resulting in the 2D imaging of FIG. 3B and the frontal wall visibility to the contactless thermal sensor device 30 of FIG. 4B, some projective transformation methods and algorithms of the present disclosure can be based upon known or tagged or marked coordinates of the corners of the room 50 in the field of view 80 (e.g., four corners at the front wall 66 that is otherwise opposite the contactless thermal sensor device 30). For example, FIG.5A is the schematic view of FIG. 3A (i.e., 2D view of the room 50 from the perspective of the sensor of the contactless thermal sensor device 30, including an identification of the field of view 80 that is otherwise captured by the contactless thermal sensor device 30). For ease of understanding, the portion of each of the floor 60, the ceiling 62, the front wall 66, the left side wall 68, and the right side wall 70 that are otherwise within, or captured by, the field of view 80 is shown or designated with a distinct color (i.e., the portion of the floor 60 in the field of view 80 is green, the portion of the ceiling 62 in the field of view 80 is purple, the portion (i.e., entirety) of the front wall 66 is maroon, the portion of the left side wall 68 in the field of view 80 is yellow, the portion of the right side wall 70 in the field of view 80 is pink); any portion of the room not in the field of view 80 of that particular contactless thermal sensor device 30 is designated by a light blue color. Within this field of view 80, the four corners of the front wall 60 have been identified and marked or tagged, and thus the coordinates thereof in the field of view 80 are known, for example as part of a calibration process described below. In the representation of FIG.5A, the four corners of the front wall 60 are identified as “A” (corner at the intersection of the front wall 60 with the ceiling 62 and the left side wall 68), “B” (corner at the intersection of the front wall 60 with the ceiling 62 and the right side wall 70), “C” (corner at the intersection of the front wall 60 with the floor 60 and the right side wall 70), and “D” (corner at the intersection of the front wall 60 with the floor 60 and the left side wall 68). The coordinates of each of the tagged corners A-D relative to the field of view 80 are thus known. Further, coordinates of the four corners of the field of view 80 (i.e., the thermal image captured by the sensor of the contactless thermal sensor device 30) are inherently known and are identified in the representation of FIG. 5A, including the corners identified as “E” (corner at the intersection of the field of view 80 with the ceiling 62 and the left side wall 68), “F” (corner at the intersection of the field of view 80 with the ceiling 62 and the right side wall 70), “G” (corner at the intersection of the field of view 80 with the floor 60 and the right side wall 70), and “H” (corner at the intersection of the field of view 80 with the floor 60 and the left side wall 68). By way of further explanation, FIG. 5B is an unfolded, scaled frontal wall representation of the room 50 of FIG.4B, and corresponds with the field of view 80 and identified points A-H of FIG.5A (i.e., the same color schemes or designations of FIG.5A are utilized in FIG.5B to illustrate wall surfaces in the field of view and outside of the field of view).

[0035] Projective (homographic) transformations are employed to correlate the observed field of the sensor of the contactless thermal sensor device 30 (i.e., FIG.5A) with the scaled front view (i.e., FIG. 5B). In other words, the colored sections of FIG. 5A can be matched with their corresponding colored sections in FIG. 5B through this transformation. To execute this transformation, some systems and methods of the present disclosure calculate homography matrices premised upon the known coordinates of corresponding corners in both images. Once the coordinates of the color-coded regions are identified, homography matrices for each wall are determined. Various programming can be employed to derive the homography matrices (e.g., programming language such as Python script). The programming algorithm is provided with the determined or known coordinates of the corresponding corners for each wall section within the field of view and the scaled front view (e.g., dimensions of the room(s) in the scaled wall image). In certain configurations, the systems of the present disclosure can optionally include an RGB camera (e.g., integrated with the contactless thermal sensor device 30). This feature enables automatic identification of known points. One example of a homography transformation relative to the right side wall 70 is illustrated in FIG. 6. The transformation algorithm is fed with the coordinates of the corresponding corners B, F, G, C from the field of view image and B’, C’, G’, C’ from the scaled front view.

[0036] As mentioned above, tagging or marking of the corners for use in the homography matrix calculations can be performed as part of a calibration process. In some embodiments, the calibration process can be positioned-based, and generally entail use of temporary markers (that are otherwise “visible” to the optical sensor device) in the fixed field of view. For example, and returning to FIGS. 2A and 2B, with the contactless thermal sensor device 30 installed to a fixed and final location and position within the room 50 (i.e., the final location at which the contactless thermal sensor device 30 will be positioned for subsequent MRT measurements), four known points along the front wall 62 (and thus in the field of view 80) are each “marked” so as to be visible to the sensor of the contactless thermal sensor device 30. The four known points can be selected from various locations and in some non-limiting embodiments, one or more of the four corners of the room 50 in the field of view 80 are utilized (i.e., the four corners of the front wall 62). In some examples, each of the four corners can be marked; in other examples, two of the corners are marked at a known distance from the two corners (e.g., intersection of the edges and the centerline of the corresponding wall). Marking of the four known points can be achieved in various manners. For example, a small heating element can be mounted at each of the four known points. Other heat generating objects can alternatively be employed, for example a human subject standing in front of the wall 62. Data (e.g., a thermal image) subsequently obtained by the contactless thermal sensor device 30 will include the marked known points (e.g., corners); coordinates of the so-marked points relative to the thermal image are then determined and employed for the homography transformation calculations as described above. So long as the contactless thermal sensor device 30 is not moved from the fixed location of the calibration process, the determined noted coordinates of the marked points relative to the field of view will not change. Thus, the temporary marker devices can be removed and for all subsequent data (e.g., thermal images) obtained by or from the contactless thermal sensor device 30, the now-known coordinates can be used with the corresponding the homography transformations. With these non-limiting techniques of the present disclosure, the process of corner or known point marking and homography matrix calculation can be termed “location / position- based calibration of the optical sensor” as they hinge on the sensor’s specific location and position. Regardless, after calibration, the systems of the present disclosure can continuously determine or measure MRT as described below.

[0037] By way of further explanation, FIG.7A is an example low-resolution thermal image 100 obtained from an IR thermal array sensor device installed to a fixed location of a room (it being understood that the field of view of the IR thermal array sensor dictates the perimeter of the image 100). The room for which the thermal image 100 was obtained includes multiple wall / surfaces that are in the field of view, but are not otherwise readily identifiable in the thermal image 100. FIG. 7B is an example low resolution thermal image 110 from the same IR thermal array sensor installed at the same location of the same room; prior to capturing the image 110, small heating elements were installed in four corners of the room (that were otherwise known to be in field of view of the IR thermal array sensor) and activated. The four heating elements are readily identified in the thermal image 110, and thus a location (and coordinates there of relative to a perimeter of the thermal image 110) of the four corners can be determined as part of the calibration process. With the coordinates of the four corners in the field of view now known, homography transformations described above can be readily performed on 2D thermal data subsequently obtained from the same IR thermal array sensor installed at the same location. So long as the IR thermal array sensor remains fixed / is not moved, the tagging and calibration process need only be performed once. As a point of reference, the example thermal images 100, 110 were obtained from an IR thermal array sensor installed to a room highly akin to the arrangement of FIGS. 5A and 5B; with this in mind, FIG. 7C is another example thermal image 120 from the same IR thermal array sensor installed at the same location and highlights the observed fields from (and thus measured thermal data of) individual walls using the same color schemes or designations of FIGS. 5A and FIG.5B (e.g., a perimeter of the floor 60 is highlighted with a green line; a perimeter of the ceiling 62 is highlighted with a purple line; etc.).

[0038] Returning to FIG.1, regardless of how 3D surface temperature data is extracted from the 2D thermal data obtained by the contactless thermal sensor device 30, the resulting mapped thermal data is provided to the thermal model module 44 for measuring or determining MRT. Because MRT is location-specific, the thermal model module 44 can also be provided with coordinates of a point in the room for which MRT is desired. The thermal model module 44 can determine MRT for a particular location or point of interest in various fashions. In some embodiments, the coordinates for the point of interest are provided to the thermal model module 44. In other embodiments, the thermal model module 44 can default to, and use the coordinates of, a center of the room as the point of interest. In further embodiments, the thermal module 44 is configured (e.g., programmed) to calculate or determine the MRT and the location and activity (e.g., sitting, standing, exercising, etc.) of a person within the room 50 where the determination of location is dynamically conducted using the outputs from the contactless thermal sensor device 30. Regardless, the thermal model module 44 determines or computes view factors and subsequently determines the MRT. For example, MRT for a point of interest can determined from the measured temperature of surrounding walls and surfaces and the angle factors between the point of interest and the surrounding surfaces. Because the sum of all angle factors is unity, the fourth power of MRT equals the mean value of the surrounding surface temperatures to the fourth power, weighted by the respective angle factors. One equation useful with the systems and methods of the present disclosure for determining MRT is provided as in Equation 1 as: ^^^^^^^^^^^^4 = ^^^^4^^^^ + ^^^^4^^^^ + ⋯+ ^^^ 41 ^^^^−1 2 ^^^^−2 ^^^^^^^^^^^^^−^^^^ Eq. (1)where: MRT is Mean Radiant Temperature; Tnis temperature of surface “n”; Fp-n is the angle factor between the point of interest and surface “n”.

[0039] The one or more additional sensor-type components 46 optionally provided with the systems of the present disclosure can take various forms. In some examples, the additional sensor 46 can include a device configured to automatically detect and measure dimensions of the room or space. For example, the additional sensor 46 can include or incorporate at least one RGB camera, at least one IR distance sensor, at least one depth camera, etc. Where provided, the distance-type sensor device can optionally be incorporated or integrated into the contactless thermal sensor device 30 (e.g., contactless thermal sensor device 30 can include the IR thermal array sensor(s) as described above for generating 2D thermal image data along with an RGB camera, an IR distance sensor arrangement, programming configured to operate the IR thermal array sensor(s) as an IR distance sensor, etc., all within a single housing). In other embodiments, the optional distance-type sensor device can be provided apart from the contactless thermal sensor device 30. In yet other embodiments, the additional sensor(s) 46 can include one or more devices adapted to detect one or more parameters relevant to thermal comfort. For example, a sensor or other device appropriate for detecting or measuring temperature, relative humidity (RH), air velocity, air quality, shortwave solar radiation, etc., can be provided with the system 20. In some embodiments, the optional thermal comfort-related sensor(s) can be integrated with one more additional components (e.g., temperature sensor, RH sensor, etc., can be integrated into a housing provided with other components such as the contactless thermal sensor device 30). In other embodiments, the optional thermal comfort-related sensor(s) can be located within the room apart from the contactless thermal sensor device 30 or can be worn by an occupant of the room (e.g., to detect clothing surface temperature, skin / face temperature, other physiological parameter, etc.).

[0040] FIG.8 illustrates one example of a method 200 for determining or measuring MRT in accordance with principles of the present disclosure. With additional reference to FIGS.2A and 2B, following fixed installation of at least the contactless thermal sensor device 30 to the room 50 of interest, position-based calibration is performed at 210. The calibration 210 can include obtaining a raw 2D thermal image at 212 from the contactless thermal sensor device 30 with tagged or marked known points (e.g., corners) in the field of view as described above, and obtaining dimensions of the room 50 at 214 (e.g., dimensions of the room 50 applicable to the scaled wall images of FIG. 4A). The dimensions of the room 50 can be obtained in various fashions. In some examples, the dimensions can be by manually measured and uploaded to the processor 32 (FIG. 1) or the memory 34 (FIG. 1). In other examples, the processor 32 can be programmed to determine dimensions and geometries of the room 50 from images of the room 50. With this in mind, images of the room 50 can be taken during the position / location-based calibration stage (step 210) using a smart phone or similar device and a connected mobile app (or uploaded to the processor 32 or the memory 34 manually). In other examples, an RGB camera or similar device can be provided (e.g., incorporated into the contactless thermal sensor device 30) and operated to capture real-time RGB images of the room 50 that are delivered to the processor 32 and / or the memory 34. In yet other examples, a distance sensor or similar device (e.g., an IR distance sensor provided with the contactless thermal sensor device 30) can be operated to automatically obtain dimensions and geometries of the room 50.

[0041] Corresponding point matches of the coordinates or data from steps 212 and 214 are determined at 216. The so-determined point matches are provided as inputs for homography matrices computation at 218. Following completion of the position-based calibration at 210, MRT determination or measurement can be performed by obtaining raw 2D thermal measurements or images from the contactless thermal sensor device 30 at 220. The 3D surface temperature from the 2D thermal image is extracted via homography transformations at 222. In this regard, the homography transformation operation makes reference to or utilizes the homography matrices generated at 218. The homography transformations generate mapped thermal data at 224 from which MRT can be determined at 226 (e.g., with respect to a point of interest in the room 50 as selected by a user). Following calibration at step 210, MRT can consistently be measured or determined on a real-time basis.

[0042] The systems and methods of the present disclosure are optionally configured to determine or estimate other comfort-related metrics in addition to MRT at 228. As a point of reference, MRT measurement is one of the most important challenges for thermal comfort measurement, and is also a parameter of several other comfort metrics. The systems and methods of the present disclosure are capable of determining or providing not only MRT, but also temperature information for other objects or items in the room, for example clothing surface temperature, subject skin / face temperature, etc., that can be important indicators / parameters for several other comfort-related measures. With this in mind, in some embodiments the systems and methods of the present disclosure are configured or formatted (e.g., programming provided to or with the processor 32) to determine or estimate an operative temperature of the room or space. The operative temperature is reference to the uniform temperature of an imaginary black enclosure, and the air within it, in which an occupant would exchange the same amount of heat by radiation plus convection as in the actual nonuniform environment (ASHRAE 55, 2021). Upon determination of MRT, the operative temperature, To, can be determined by Equation 2 below, where Tαis air temperature and A is weight coefficient depending on air velocity (A = 0.5 if air velocity < 0.2 meters / second (or 40 feet per minute).^^^^^^^^ = ^^^^^^^^^^^^ + (1 − ^^^^)^^^^^^^^^^^^ Eq. (2)

[0043] Additionally or alternatively, in some embodiments, the systems and methods of the present disclosure are configured or formatted (e.g., programming provided to or with the processor 32) to determine or estimate a predicted mean vote (PMV). PMV is an index that predicts the mean value of the votes of a large group of people on a seven point (ASHRAE) thermal sensation scale (ISO 7730, 2005) (ANSI / ASHRAE Standard 55, 2020) (ASHRAE Handbook Fundamentals – Ch. 9, 2021). The PMV index is a function of MRT and can be determined when the activity, M, and clothing temperature are estimated, and the following parameters are measured: air temperature, clothing surface temperature, MRT, and relative air velocity. Upon determining MRT, PMV can be determined by Equation 3 below as: ^^^^^^^^^^^^ = (0.303^^^^−0.036 ^^^^ + 0.028)�(M − W)− 3.05 × 10−3 × [5733 − 6.99 × (M − W) − ^^^^^^^^]− 0.42 x [(M − W) − 58.15] − 1.7 x 10−5 × M(5867 − pa)− 0.014 × M × (34 − Ta)− 3.96 × 10−8 × ^^^^^^^^^^^^ × [(Tcl + 273)4 − (MRT + 273)4]− ^^^^^^^^^^^^ × ℎ^^^^ × (^^^^^^^^^^^^ − ^^^^^^^^)� Eq. (3)where: M is the rate of metabolic heat production, W / m2; W is the rate of mechanical work accomplished, W / m2; fcl is the clothing surface area factor, determined as: ^^^ 1 ± 0.2 ^^^^^^^^^^^^, ^^^^^^^^^^^^ < 0.5 ^^^^^^^^^^^^^^^^^^^^^ =� 1.05 ± 0.1 ^^^^ Eq. (4)^^^^^^^^, ^^^^^^^^^^^^ > 0.5 ^^^^^^^^^^^^ Iclis the clothing insulation, m2K / W; Tclis the surface temperature of clothing,oC; Tais the air temperature,oC; hcis the conductive heat transfer coefficient, W / (m2K); and pais water vapor partial pressure in ambient air, kPa.

[0044] Conventionally, clothing temperature has been estimated in accord with Equation 5 below through iteration since there was no feasible way to measure that in real time. ^^^^^^^^^^^^ = 35.7 – 0.028(^^^^ – ^^^^) – ^^^^^^^^^^^^{39.6× 10–9^^^^^^^^^^^^ [(^^^^^^^^^^^^ + 273)4 – (^^^^^^^^^^^^ + 273)4]+ ^^^^^^^^^^^^ ℎ^^^^(^^^^^^^^^^^^ – ^^^^^^^^)} Eq. (5)However, the systems and methods of the present disclosure are capable of measuring the clothing temperature directly via, for example, the contactless thermal sensor device 30. Thus, the features increases the accuracy and reduces the computational cost when determining PMV by eliminating the iterative estimation process.

[0045] Additionally or alternatively, in some embodiments, the systems and methods of the present disclosure are configured or formatted (e.g., programming provided to or with the processor 32) to determine or estimate a predicted percentage dissatisfied (PPD). PPD is a function of PMV. PMV and predicts the mean value of the thermal votes of a large group of people exposed to the same environment. But individual votes are scattered around this mean value and it can be useful to be able to predict the number of people likely to feel uncomfortably warm or cool. The PPD is an index that establishes a quantitative prediction of the percentage of thermally dissatisfied people who feel too cool or too warm (ISO 7730, 2005) (ANSI / ASHRAE Standard 55, 2020). Upon determining PPV, PPD can be determined by Equation 6 below: PPD = 100 – 95 exp[– (0.03353PMV4 + 0.2179PMV2)] Eq. (6)

[0046] In addition to MRT and the above-given parameters (e.g., operative temperature, PMV, PPD), recent studies have demonstrated the correlations between thermal sensation and human face temperature and skin temperature (Jia M. et al, doi.org / 10.1016 / j.buildenv.2021.108479). The systems and methods of the present disclosure are capable of directly measuring face skin temperature using the contactless thermal sensor device 30 and are able to use this information for personal comfort sensation estimations in some embodiments.

[0047] Returning to FIG.1 and as mentioned above, in some embodiments, an entirety of the system 20 can be maintained by a single housing. With these and related examples, the single housing is mounted or fixed within the room. In other embodiments, the contactless thermal sensor device 30 alone can be installed to the room and operated to signal obtained data (e.g., via wired or wireless communication) to a separate device that in turn operates to perform the transformations and MRT determinations as described above. Further, while various examples of the present disclosure have described the use of a single contactless thermal sensor device 30 to perform MRT determinations for a room of interest, in other embodiments, two or more contactless thermal sensor devices can be installed and operated as a sensor network to increase accuracy. For example, FIG.9A is a simplified representation of portions of another embodiment system 250 in accordance with principles of the present disclosure installed to the room 50. The system 250 includes a plurality of the contactless thermal sensor devices 30 as described above (e.g., each including at least an IR thermal array sensor maintained within a housing). The contactless thermal sensor devices 30 are installed apart from one another at different locations inside the room 50 and thus collectively provide a network of different fields of view of the room 50.3D surface temperatures are extracted from the combination of the 2D thermal images generated by the contactless thermal sensor devices 30 as described above to generate mapped thermal data of the room 50 from which MRT can be determined (e.g., with a combination of 2D images taken from the four contactless thermal sensor devices 30 arranged as in FIG. 9A, the number of homographic transformations required is reduced). While FIG.9A illustrates four of the contactless thermal sensor devices 30, any other number, either greater or lesser, is equally acceptable. FIG.9B is a simplified representation of another embodiment system 260 in accordance with principles of the present disclosure installed to the room. The system 260 includes a plurality of the contactless thermal sensor devices 30 as described above (e.g., each including at least an IR array sensor maintained within a housing). The contactless thermal sensor devices 30 are installed at the same general location of the room 50, but arranged such that the respective field of views are directed in different directions. In some non- limiting embodiments, 2 – 5 different directions can be employed (e.g., depending upon the field of view and resolution of each of the contactless thermal sensor devices 30), although more than five different directions is equally acceptable. Regardless, 3D surface temperatures are extracted from a combination of the 2D thermal images generated by the contactless thermal sensor devices 30 as described above to generate mapped thermal data of the room 50 from which MRT can be determined.

[0048] In yet other, related embodiments, systems of the present disclosure can be configured to obtain 2D thermal images of different portions of the room 50 (that in turn are balanced with one another to generated mapped thermal data of the room 50) via a single IR thermal array sensor (or similar contactless thermal sensor). For example, FIG. 9C is a simplified representation of another embodiment system 270 in accordance with principles of the present disclosure installed to the room 50. The system 270 include a contactless thermal sensor device 30’. The contactless thermal sensor device 30’ can be highly akin to the contactless thermal sensor devices described above (e.g., including at least an IR thermal array sensor maintained within a housing), and further includes a manipulator mechanism (e.g., a pan-tilt mechanism) that is operable to direct the sensor’s field of view in different directions. In some embodiments, the manipulator mechanism is operable in a controlled fashion (e.g., a programmable logic controller prompts operation of the manipulator mechanism to repeatedly move, and then hold, the field of view between two (or more) established positions / directions. In some non-limiting embodiments, 2 – 5 different established positions / directions can be employed, although more than five established positions / directions is equally acceptable. Regardless, 3D surface temperatures are extracted from a combination of the 2D thermal images generated by the contactless thermal sensor device 30’ at each established position / direction as described above to generate mapped thermal data of the room 50 from which MRT can be determined.

[0049] Returning to FIG. 1, while some examples of the present disclosure have been described with reference to at least the contactless thermal sensor device 30 being installed to a wall of the room of interest, other installation locations are equally acceptable (e.g., installed to the ceiling, a corner of the room, away from any walls, etc.). In addition, the systems and methods of the present disclosure are equally useful for determining or measuring MRT for relatively simple room spaces (such as the room of FIGS. 2A and 2B) and complex room spaces (e.g., an office space, a room having multiple interior objects, etc.). EXAMPLES

[0050] Embodiments and advantages of features of the present disclosure are further illustrated by the following non-limiting examples. The particular materials and amounts thereof recited in these examples, as well as operating conditions and details, should not be construed to unduly limit the scope of the present disclosure.

[0051] MRT systems in accordance with principles of the present disclosure were prepared using a low-resolution IR thermal array sensor as the contactless thermal sensor device. In particular, 32 x 32 pixel IR thermal array sensors available from OMRON Corp. under the trade designation D6T-2L-01A were obtained. The IR thermal array sensor was installed to a sensor evaluation board available from OMRON Corp. under the trade designation 2JCIE-EV01-AR1 that in turn was installed to an IoT communication board available from Arduino S.r.l. under the trade designation MKR WiFi 1010 to provide a contactless thermal sensor device (e.g., an IR thermal array sensor device) with wireless communication capabilities. MRT determination experiments were performed using one or more of the IR thermal array sensor devices installed to various room configurations as described below, with data from the IR thermal array sensor device delivered to a computer-type device programmed to perform the transformation, thermal modeling, and MRT determination operations of the present disclosure. During each experiment, comparative MRT measurements were obtained by a net radiometer arrangement including a pyranometer (silicon-cell pyranometer) available from Apogee Instruments, Inc. and a pyrgeometer available from Apogee Instruments, Inc.

[0052] A first experiment was conducted in a bedroom-type space having four side wall. As generally shown in FIG. 10, four of the IR thermal array sensor devices (labeled in FIG.10 as “Opt.”) were installed to the walls, respectively, and were location / position calibrated. Data from each of the IR thermal array sensor devices was used to determine or measure MRT at a center of the room. Comparative MRT measurements were obtained by the net radiometer arrangement positioned in a center of the room.

[0053] A second experiment was conducted in a living room-type space having the lay-out shown in FIG.11. Four IR thermal array sensor devices (“Opt.”) were installed to the walls of the space, respectively, and were location / position calibrated. Data from each of the IR thermal array sensor devices was used to determine or measure MRT at a center of the room. Comparative MRT measurements were obtained by the net radiometer arrangement positioned in a center of the room.

[0054] A third experiment was conducted in a living room-type space having the lay-out shown in FIG.12. Four IR thermal array sensor devices (“Opt.”) were installed to the walls of the space, respectively, and were location / position calibrated. Data from each of the IR thermal array sensor devices was used to determine or measure MRT at a center of the room. Comparative MRT measurements were obtained by the net radiometer arrangement positioned in a center of the room.

[0055] A fourth experiment was conducted in an office-type space having the lay-out shown in FIG.13. Four IR thermal array sensor devices (“Opt.”) were installed to the walls of the space, respectively, and were each location / position calibrated. Data from each of the IR thermal array sensor devices was used to determine or measure MRT at a center of the room. Comparative MRT measurements were obtained by the net radiometer arrangement positioned in a center of the office-type space.

[0056] The four experiments described above were conducted for approximately 300 hours. Over the course of each experiment, the MRT determined via the thermal array sensor(s) was compared to the MRT measured by the net radiometer arrangement. FIG. 14 presents a summary of the overall results of the four experiments (i.e., the arrangements of FIG. 11 - 13) in which one, two and four IR thermal array sensor device combinations were compared (designated as “1Cam”, “2Cam” and “4Cam”, respectively, in FIG. 14). A maximum difference or error between IR thermal array sensor-derived MRT and net radiometer-derived MRT was ± 0.7oC, including outliers. From these results, it was determined that the systems and methods of the present disclosure can accurately determine or measure MRT under various scenarios and different space configurations.

[0057] The systems and methods of the present disclosure provide a marked improvement over previous designs. MRT of a room can be determined or measured using low- resolution, low-cost infrared sensors or similar contactless thermal sensor devices by capturing 2D low-resolution thermal images and extracting the surface temperature distribution of the 3D space through principles of computer vision, such as projective geometric (homography) transformations from which MRT is determined. In some examples, the systems and methods of the present disclosure can utilize low-resolution IR temperature sensor(s) in a stationary manner, eliminating the need for directional control mechanisms (e.g., pan-tilt) and simultaneous distance measurements. Moreover, the systems and methods of the present disclosure enable instantaneous capture of MRT, which can be critical to increasing the measurement’s reliability and accuracy while reducing power consumption. Unlike black globe and similar MRT point sensors, the systems and methods of the present disclosure are capable of measuring the distribution of MRT, providing valuable insight into the variations across different areas in a space without disturbing the occupants. When optionally integrated with built environment systems (e.g., room thermostats, smart building systems, etc.), the systems and methods beneficially provide improved accuracy in measuring the comfort level of space, leading to increased comfort and well-being. Operational efficiency of radiant heating and cooling systems can also greatly benefit from integration of the systems and methods of the present disclosure. In yet other end use applications, the systems and methods of the present disclosure can improve the data-driven design of buildings, for example reducing the cost of post-occupancy analysis and by integration into building modeling software.

[0058] Although the present disclosure has been described with reference to preferredembodiments, workers skilled in the art will recognize that changes can be made in form and detail without departing from the spirit and scope of the present disclosure.

Claims

Claims:

1. A system for determining mean radiant temperature of a room, the system comprising: at least one contactless thermal sensor device configured to be installed to the room; and at least one processor programmed to determine a mean radiant temperature of the room based upon data from the contactless thermal sensor device.

2. The system of claim 1, wherein the at least one contactless thermal sensor device includes at least one IR thermal array sensor.

3. The system of claim 2, wherein the contactless thermal sensor device is configured to generate 2D low-resolution images.

4. The system of claim 1, wherein the at least one processor is programmed to extract 3D surface temperature data from 2D thermal images captured by the contactless thermal sensor device.

5. The system of claim 4, wherein the at least one processor is further programmed to extract occupant location form the 2D thermal images.

6. The system of claim 4, wherein the at least one processor is programmed to determine the mean radiant temperature from the extracted 3D surface temperature data.

7. The system of claim 4, wherein the at least one processor is programmed to extract the 3D surface temperature data through homographic transformations.

8. The system of claim 7, wherein the homographic transformations are based upon homography matrices.

9. The system of claim 8, wherein the homography matrices are based upon corresponding known points in a 2D image of the room as generated by the contactless thermal sensor device and in a frontal view of the walls of the room.

10. The system of claim 9, wherein the known points include at least one corner of a wall of the room that is in a field of view of the contactless thermal sensor device.

11. The system of claim 8, wherein the known points are determined by a position-based calibration process.

12. The system of claim 1, wherein the at least one contactless thermal sensor device includes at least a first contactless thermal sensor device and a second contactless thermal sensor device, and further wherein upon final assembly to a room, a field of view of the first contactless thermal sensor device relative to the room differs from a field of view of the second contactless thermal sensor device relative to the room.

13. The system of claim 1, wherein the system further includes at least one of an RGB camera sensor and a distance sensor.

14. A method for determining mean radiant temperature of a room, the method comprising: installing at least one contactless thermal sensor device within the room; operating the contactless thermal sensor device to obtain thermal data of the room; determining a mean radiant temperature of the room from the obtained thermal data.

15. The method of claim 14, wherein in the contactless thermal sensor device includes at least one IR thermal sensor array.

16. The method of claim 15, wherein the obtained thermal data is 2D low-resolution images.

17. The method of claim 14, wherein the obtained thermal data includes 2D thermal images, and further wherein the step of determining includes extracting 3D surface temperature data from the 2D thermal images.

18. The method of claim 17, wherein the step of extracting includes applying homographic transformations to the obtained thermal data.

19. The method of claim 18, wherein the step of applying includes generating homography matrices utilizing known points in a 2D image of the room as generated by the contactless thermal sensor device and in a frontal view of walls of the room.

20. The method of claim 19, further comprising: performing a position-based calibration process to determine the known points.

21. The method of claim 20, wherein position-based calibration process includes: marking at least one corner of a wall of the room in the field of view of the contactless thermal sensor device; and identifying the marked corner in an image generated by the optical sensor device.

22. The method of claim 21, wherein the step of marking includes at least one of: locating a heating element at a predetermined location within the room.

23. The method of claim 14, further comprising installing at least one of an RGB camera and a distance sensor within the room.

24. The method of claim 14, further comprising: determining at least one of a location, position and activity of at least one occupant in the room based upon the obtained thermal data.

25. The method of claim 14, further comprising: measuring at least one of a face temperature and a skin temperature of at least one occupant in the room.

26. The method of claim 14, further comprising: determining at least one comfort-related metric selected from the group consisting of operative temperature, Predicted Mean Vote, and Predicted Percentage Dissatisfied based upon the obtained thermal data.

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