WO2023052840A2

WO2023052840A2 - Smart mat systems and methods of using the same

Info

Publication number: WO2023052840A2
Application number: PCT/IB2022/000550
Authority: WO
Inventors: Thomas Serval; Gauthier de Rouzé
Original assignee: Mateo
Priority date: 2021-09-30
Filing date: 2022-09-30
Publication date: 2023-04-06
Also published as: WO2023052840A3

Abstract

A pressure sensing system includes a PCB layer, a plurality of pressure sensing units disposed on a first side of the PCB layer, a plurality of column traces disposed on the first side of the PCB layer, and a plurality of row traces disposed on a second side of the PCB layer that is opposite to the first side of the PCB layer. Each pressure sensing unit of the plurality of pressure sensing units includes a first electrically conductive trace and a second electrically conductive trace. Each column trace of the plurality of column traces connects two corresponding first electrically conductive traces of two generally vertically adjacent pressure sensing units. Each row trace of the plurality of row traces connects two corresponding second electrically conductive traces of two generally horizontally adjacent pressure sensing units.

Description

SMART MAT SYSTEMS AND METHODS OF USING THE SAME

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of and priority to U.S. Provisional Application No. 63/261,841, filed September 30, 2021, which is hereby incorporated by reference herein in its entirety.

TECHNICAL FIELD

[0002] The present disclosure relates to health products, and more specifically, to smart scale and/or smart mat systems.

BACKGROUND

[0003] Consumers are increasingly focused on health and health-related products. We focus on our weight, what we eat, how we stand, and so on. Thus, a need exists for a multi-use apparatus that generates health-related information of a user. The present disclosure is directed to addressing these needs and solving other problems.

SUMMARY

[0004] According to some implementations of the present disclosure, a pressure sensing system includes a PCB layer, a plurality of pressure sensing units disposed on a first side of the PCB layer, a plurality of column traces disposed on the first side of the PCB layer, and a plurality of row traces disposed on a second side of the PCB layer that is opposite to the first side of the PCB layer. Each pressure sensing unit of the plurality of pressure sensing units includes a first electrically conductive trace and a second electrically conductive trace. Each column trace of the plurality of column traces connects two corresponding first electrically conductive traces of two generally vertically adjacent pressure sensing units. Each row trace of the plurality of row traces connects two corresponding second electrically conductive traces of two generally horizontally adjacent pressure sensing units.

[0005] According to some implementations of the present disclosure, a smart mat system includes a tech device and a mat cover configured to be placed directly above and covering the tech device. The tech device includes a PCB layer and an array of pressure sensing units disposed on a first side of the PCB layer. Each pressure sensing unit includes a first electrically conductive trace disposed on the first side of the PCB layer, and a second electrically conductive trace disposed on the first side of the PCB layer. The first electrically conductive trace is connected to a generally vertically adjacent pressure sensing unit via a column trace that is disposed on the first side of the PCB layer. The second electrically conductive trace is connected to a generally horizontally adjacent pressure sensing unit via a row trace that is disposed on a second side of the PCB layer.

[0006] According to some implementations of the present disclosure, a smart mat system includes a PCB layer, an array of pressure sensing units disposed on a first side of the PCB layer, a memory storing machine-readable instructions, and a control system coupled to the memory and arranged to provide control signals to one or more processors. Each pressure sensing unit includes a first electrically conductive trace disposed on the first side of the PCB layer, and a second electrically conductive trace disposed on the first side of the PCB layer. The first electrically conductive trace is connected to a generally vertically adjacent pressure sensing unit via a column trace that is disposed on the first side of the PCB layer. The second electrically conductive trace is connected to a generally horizontally adjacent pressure sensing unit via a row trace that is disposed on a second side of the PCB layer. The control system is configured to execute the machine-readable instructions to receive, from the array of pressure sensors, pressure data. The control system is further configured to, based at least in part on the pressure data, determine that a user is engaging the smart mat system. The control system is further configured to, in response to the determining that the user is engaging the smart mat system, determine a physiological parameter of the user.

[0007] The foregoing and additional aspects and implementations of the present disclosure will be apparent to those of ordinary skill in the art in view of the detailed description of various embodiments and/or implementations, which is made with reference to the drawings, a brief description of which is provided next.

BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The foregoing and other advantages of the present disclosure will become apparent upon reading the following detailed description and upon reference to the drawings.

[0009] FIG. 1 is an illustrative block diagram of a smart mat system, according to some implementations of the present disclosure; [0010] FIG. 2 is an illustrative block diagram of the smart mat system of FIG. 1, according to some other implementations of the present disclosure;

[0011] FIG. 3A is an assembled view of a smart mat system, according to some other implementations of the present disclosure;

[0012] FIG. 3B is a disassembled view of the smart mat system of FIG. 3 A, according to some other implementations of the present disclosure;

[0013] FIG. 4A is an illustrative block diagram of a smart mat system, according to some implementations of the present disclosure;

[0014] FIG. 4B is an illustrative block diagram of a pressure sensing system of the smart mat system of FIG. 4A;

[0015] FIG. 5A is a first partial perspective view of a smart mat system, according to some implementations of the present disclosure;

[0016] FIG. 5B is a second partial perspective view of the smart mat system of FIG. 5A;

[0017] FIG. 6 is a partial perspective view of a smart mat system, according to some implementations of the present disclosure;

[0018] FIG. 7 illustrates a pressure sensor of a smart scale system, according to some implementations of the present disclosure;

[0019] FIG. 8A illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0020] FIG. 8B illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0021] FIG. 8C illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0022] FIG. 8D illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0023] FIG. 8E illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0024] FIG. 8F illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0025] FIG. 9A illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure; [0026] FIG. 9B illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0027] FIG. 9C illustrates a pressure sensor unit of a smart mat system, according to some implementations of the present disclosure;

[0028] FIG. 10A illustrates a portion of a pressure sensing matrix of a smart mat system, according to some implementations of the present disclosure;

[0029] FIG. 10B is an exploded view of the pressure sensing matrix of FIG. 10A, according to some implementations of the present disclosure;

[0030] FIG. 11 A shows a generated pressure map of a foot using a pressure sensing matrix, according to some implementations of the present disclosure;

[0031] FIG. 11B shows a generated pressure map of a foot using a pressure sensing matrix, according to some implementations of the present disclosure;

[0032] FIG. 12 illustrates behavior curves of two different piezoresistive materials, according to some implementations of the present disclosure;

[0033] FIG. 13 illustrates a working principle of an anti-ghosting electronics circuit, according to some implementations of the present disclosure;

[0034] FIG. 14 is a schema of a circuitry of a pressure mapping system, according to some implementations of the present disclosure;

[0035] FIG. 15 illustrates an electronics circuit of compensation (2V5+e) for a pressure mapping system, according to some implementations of the present disclosure;

[0036] FIG. 16A shows a pictogram on a display of a smart mat system, according to some implementations of the present disclosure;

[0037] FIG. 16B shows a pictogram on a display of a smart mat system, according to some implementations of the present disclosure;

[0038] FIG. 16C shows a pictogram on a display of a smart mat system, according to some implementations of the present disclosure;

[0039] FIG. 17 shows a data savvy display of a smart mat system, according to some implementations of the present disclosure;

[0040] FIG. 18A illustrates a first offset between the center of pressure and the geometric center, according to some implementations of the present disclosure; [0041] FIG. 18B illustrates a second offset between the center of pressure and the geometric center, according to some implementations of the present disclosure;

[0042] FIG. 19 illustrates a center of pressure ellipse, according to some implementations of the present disclosure;

[0043] FIG. 20 is a schema of a circuitry of a pressure mapping system for anti-ghosting, according to some implementations of the present disclosure; and

[0044] FIG. 21 illustrates a partial perspective view of a smart mat system, according to some implementations of the present disclosure.

[0045] While the present disclosure is susceptible to various modifications and alternative forms, specific embodiments have been shown by way of example in the drawings and will be described in detail herein. It should be understood, however, that the present disclosure is not intended to be limited to the particular forms disclosed. Rather, the present disclosure is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.

DETAILED DESCRIPTION

[0046] According to some implementations of the present disclosure, a smart mat for a user to stand on can determine and/or monitor the user’s balance, posture, pressure points, weight, and more. Instead of a simple scale, the smart mat provides a holistic body health measuring system.

[0047] The present disclosure is described with reference to the attached figures, where like reference numerals are used throughout the figures to designate similar or equivalent elements. The figures are not drawn to scale, and are provided merely to illustrate the instant disclosure. Several aspects of the disclosure are described below with reference to example applications for illustration. It should be understood that numerous specific details, relationships, and methods are set forth to provide a full understanding of the disclosure. One having ordinary skill in the relevant art, however, will readily recognize that the disclosure can be practiced without one or more of the specific details, or with other methods. In other instances, well-known structures or operations are not shown in detail to avoid obscuring the disclosure. The present disclosure is not limited by the illustrated ordering of acts or events, as some acts may occur in different orders and/or concurrently with other acts or events. Furthermore, not all illustrated acts or events are required to implement a methodology in accordance with the present disclosure. [0048] The term “coupled” is defined as connected, whether directly or indirectly through intervening components, and is not necessarily limited to physical connections. The connection can be such that the objects are permanently connected or releasably connected. The term “substantially” is defined to be essentially conforming to the particular dimension, shape or other word that substantially modifies, such that the component need not be exact. For example, substantially cylindrical means that the object resembles a cylinder, but can have one or more deviations from a true cylinder. The term “comprising” means “including, but not necessarily limited to”; it specifically indicates open-ended inclusion or membership in a so-described combination, group, series and the like.

[0049] Aspects of the present disclosure can be implemented using one or more suitable processing device, such as general purpose computer systems, microprocessors, digital signal processors, micro-controllers, application specific integrated circuits (ASIC), programmable logic devices (PLD), field programmable logic devices (FPLD), field programmable gate arrays (FPGA), mobile devices such as a mobile telephone or personal digital assistants (PDA), a local server, a remote server, wearable computers, tablet computers, or the like.

[0050] Memory storage devices of the one or more processing devices can include a machine- readable medium on which is stored one or more sets of instructions (e.g., software) embodying any one or more of the methodologies or functions described herein. The instructions can further be transmitted or received over a network via a network transmitter receiver. While the machine- readable medium can be a single medium, the term “machine -readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) that store the one or more sets of instructions. The term “machine- readable medium” can also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that cause the machine to perform any one or more of the methodologies of the various implementations, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such a set of instructions. The term “machine -readable medium” can accordingly be taken to include, but not be limited to, solid- state memories, optical media, and magnetic media. A variety of different types of memory storage devices, such as a random access memory (RAM) or a read only memory (ROM) in the system or a floppy disk, hard disk, CD ROM, DVD ROM, flash, or other computer readable medium that is read from and/or written to by a magnetic, optical, or other reading and/or writing system that is coupled to the processing device, can be used for the memory or memories.

[0051] Referring generally to FIG. 1, an illustrative block diagram of a smart mat system 100 is shown. In some implementations, the smart mat system 100 is similar to what is described in International Patent Publication WO 2020/212950 Al, which is hereby incorporated by reference herein in its entirety. In some aspects, the smart mat system 100 further reduces costs and improves performance. The smart mat system 100 includes a mat 110, a processor 132, and a memory device 140. The mat 110 includes one or more sensors 112 (hereinafter “the sensor”) configured to output pressure data. The memory device 140 can store machine-readable instructions that are configured to cause the processor 132 to determine that a portion of the user is in contact with the mat 110 based on the pressure data.

[0052] In some implementations, the sensor 112 can be a CMOS integrated silicone pressure sensor, or a piezoelectric sensor. In some implementations, the sensor 112 includes an embedded layer of liquid capable of sensing pressure. For example, the sensor 112 can be a layer stacked pressure sensor comprising a liquid metal-embedded elastomer. In some implementations, the sensor 112 incorporates a pressure sensor matrix technology, which is described in further detail with respect to FIGS. 4 and 7 to 15.

[0053] The processor 132 can be further caused to determine a user profile for the user based on the pressure data. In some implementations, the smart mat system 100 may optionally include a display device 130. The processor 132 can also be caused to display, on the display device 130, information associated with the determined user profile. Additionally, or alternatively, in some implementations, the memory device 140 can be configured to cause the processor 132 to determine an identity of the user based on the pressure data. The determining process can be carried out by, for example, a machine learning algorithm.

[0054] As an example, the user profile can include a shape of the portion of the user (e.g., a foot, a hand, or the like) that is in contact with the mat 110, a dimension of the portion of the user that is in contact with the mat 110, or the like, or any combination thereof. In such example, the displayed information associated with the determined user profile includes a first indicium indicative of the weight of the user, a second indicium indicative of the posture of the user, a third indicium indicative of the shape of the portion of the user, a fourth indicium indicative of the dimension of the portion of the user, or the like, or any combination thereof. [0055] The memory device 140 of the smart mat system 100 can be further configured to cause the processor 132 to determine a wellness plan for the user based on the determined user profile, and the displayed information associated with the determined user profile can then include an indicium indicative of wellness of the user. For example, the wellness plan is an exercise schedule. [0056] In some implementations, the memory device 140 of the smart mat system 100 can also be configured to cause the processor 132 to determine a posture score, for example, by comparing the pressure data to one or more predetermined postures stored in a database 142 of the memory device 140. For example, the posture score can be indicative of poor posture of the user. In some implementations, the memory device 140 can be configured to cause the processor 132 to determine a posture correction plan associated with the user based on the comparing the pressure data to the one or more predetermined postures.

[0057] Additionally, or alternatively, in some implementations, the memory device 140 of the smart mat system 100 can also be configured to cause the processor 132 to determine a balance score, for example, by comparing the pressure data to one or more predetermined balance patterns stored in a database 142 of the memory device 140. For example, the balance score can be indicative of poor balance of the user. In some implementations, the memory device 140 can be configured to cause the processor 132 to determine a balance enhancement plan associated with the user based on the comparing the pressure data to the one or more predetermined balance patterns.

[0058] Additionally, or alternatively, in some implementations, the memory device 140 of the smart mat system 100 can also be configured to cause the processor 132 to determine a stability score, for example, by comparing the pressure data to one or more predetermined stability patterns stored in a database 142 of the memory device 140. For example, the stability score can be indicative of poor stability of the user. In some implementations, the memory device 140 can be configured to cause the processor 132 to determine a stability training plan associated with the user based on the comparing the pressure data to the one or more predetermined stability patterns. [0059] In some implementations, the smart mat system 100 includes a power source 134 and a user interface 136. In some such implementations, for example, the user interface 136 is coupled to the display device 130. The user interface 136 can be configured to receive input data associated with the user. As an example, the input data may include age or gender of the user. Additionally, or alternatively, in some other such implementations, the power source 134 includes a battery and/or an energy harvesting element configured to harvest energy for charging the battery. The energy harvesting element can be a transducer configured to convert thermal energy into electrical energy for charging the battery. For example, the transducer can be coupled to a sensor (e.g., the sensor 112 or a different sensor) configured to detect temperature and/or output temperature data. Alternatively, or additionally, in some implementations the energy harvesting element can be a transducer configured to convert mechanical energy (e.g., vibrations from someone standing on the mat 110 or exercising on the mat 110) into electrical energy for charging the battery.

[0060] In some implementations, the display device 130 is coupled to the mat 110. For example, the mat 110 may include one or more LED lights as a portion of the display device 130. The processor 132 can be configured to cause the display device 130 to display a shape indicative of a position for the user to place his or her hands or feet on the mat 110. This can be useful in various situations, such as in the instance where the mat 110 is a yoga mat, and the smart mat system 100 is configured to display yoga postures suggested to the user by recommending placement for the user’s hands and/or feet.

[0061] In some implementations, the memory device 140 of the smart mat system 100 can be configured to cause the processor 132 to determine an active period based on the determining that the portion of the user is in contact with the mat 110. In some implementations, the smart mat system 100 optionally includes an imaging device 120 (such as a camera, a video recorder, or the like). The imaging device 120 can be configured to generate image data reproducible as one or more images of a user. The memory device 140 can be configured to receive and store therein the pressure data from the sensor 112 and the image data from the imaging device 120. In some such implementations, the image data (generated by the imaging device 120) may be used to supplement and/or confirm analysis performed using the pressure data (generated by the sensor 112).

[0062] In some implementations, the mat 110 of the smart mat system 100 is configured to pair with a mobile device, such as a mobile phone, a smartwatch, a smart TV, a tablet, etc. For example, the display device 130 may be coupled to and/or integrated in the mobile device. The mat 110 can be paired with one, two, three, or any other number of mobile devices. The mat 110 can also be paired with one or more different electronic devices. In some other implementations, the mat 110 of the smart mat system 100 works in a standalone mode (e.g., without a mobile device). [0063] While the smart mat system 100 is shown in FIG. 1 as including the mat 110, the sensor 112, the imaging device 120, the display device 130, the processor 132, the memory device 140, the database 142, the power source 134, and the user interface 136, alternative systems that are the same as, or similar to, the smart mat system 100 of the present disclosure can be constructed with more or fewer components. For example, a first alternative smart mat system includes a mat, a pressure sensor, a processor, and a memory device. As another example, a second alternative smart mat system includes a mat, a pressure sensor, a display device, a processor, a memory device, and a power source.

[0064] FIG. 2 illustrates an example implementation of the present disclosure. A smart mat system 200 is the same as, or similar to, the smart mat system 100, where like reference numbers are used to designate similar or equivalent components, except that the various components of the smart mat system 100 can be coupled to different devices. For example, the smart mat system 200 may include one of more of the following as separate devices: a mat (e.g., the mat 110), an imaging device (e.g., the imaging device 120), and a mobile device 150. The mat 110 includes one or more sensors (e.g., the sensor) 112, a power source (e.g., the power source 134), and a communications module 114. The mobile device 150 includes a processor (e.g., the processor 132), a memory device (e.g., the memory device 140), a display device (e.g., the display device 130), a user interface (e.g., user interface 136), and a communications module 116. The mat 110 can be communicatively coupled to the mobile device 150 via the communications modules 114 and 116. Similarly, the imaging device 120 can be communicatively coupled to the mobile device 150. Additionally or alternatively, the imaging device 120 can be directly coupled to the mobile device 150. As another example, these devices can be coupled to one another via Bluetooth or Bluetooth Low Energy (BLE).

[0065] While the smart mat system 200 is shown in FIG. 2 as including the sensor 112, the power source 134, the communications module 114, the processor 132, the memory device 140, the display device 130, the user interface 136, the communications module 116, and the imaging device 120, alternative systems that are the same as, or similar to, the smart mat system 200 of the present disclosure can be constructed with more or fewer components. Additionally, while the mat 110 is shown in FIG. 2 to include the sensor 112, the power source 134, and the communications module 114, a mat of the present disclosure can include more or fewer components. For example, a first alternative mat of the present disclosure includes the sensor 112, the processor 132, and the memory device 140. As another example, a second alternative mat of the present disclosure includes the sensor 112, the processor 132, and the communications module 114. Similarly, while the mobile device 150 is shown in FIG. 2 to include the processor 132, the memory device 140, the display device 130, the user interface 136, and the communications module 116, a mobile device of the present disclosure can include more or fewer components.

[0066] Referring to FIG. 3A, a top perspective view of an assembled smart mat system 300 is shown. In some implementations, the smart mat system 300 is the same as, similar to, or used in conjunction with the smart mat systems shown in FIGS. 1-2. In some implementations, the smart mat system 300 includes one or more components of the smart mat system 100 and/or the smart mat system 200. While the smart mat system 300 can be of any suitable shape or size, in this example, the round corners and the slope of the edges give the smart mat system 300 a unique appearance.

[0067] Referring to FIG. 3B, a disassembled view of the smart mat system 300 of FIG is shown. In this example, the smart mat system 300 can be disassembled into a mat cover 360 and a tech device 362. The mat cover 360 is configured to be placed directly above, and covering, the tech device 362. In some implementations, at least a portion of the mat cover 360 is made of fabric, rubber, conductive metallic thread, or any other suitable material. In some implementations, the mat cover 360 is washable.

[0068] In some implementations, the smart mat cover 360 includes one or more conductive thread electrodes, such as the four conductive thread electrodes 378a, 378b, 378c, and 378d as shown. In some such implementations, the one or more conductive thread electrodes are the same, or similar to, electrically conductive fabric portions 578 A, 578B in FIGS. 5A-5B. Additionally, or alternatively, in some implementations, the one or more conductive thread electrodes includes exposed conductive electrodes for biodata measurement though the portion of the user that is in contact with the conductive electrodes. Biodata can include bioimpedance, or body impedance, which will be used for body composition calculation.

[0069] In some implementations, the edges of the mat cover 360 includes a rubber material, or is otherwise anti-slip and toe-protecting. In some implementations, the edges of the mat cover 360 are sloped such that the likelihood of a user tripping over the edges is reduced, and the overall smart mat system 300 has a unique appearance when assembled. In some implementations, the corners of the mat cover 360 are rounded, so not to cause discomfort in the user should the user steps on the corners.

[0070] Referring to FIG. 4A, a smart mat system 400 is illustrated. The smart mat system 400 is the same as, similar to, or used in conjunction with the smart mat systems of FIGS. 1-3B, where like reference numbers are used to designate similar or equivalent components. In some implementations, one or more components of the smart mat system 400 form a smart mat, which may take the form of a portion of a rug, a bath mat, a yoga mat, or the like. The smart mat system 400 is used to determine the normalized weight, the balance, the stability, the posture, or another health-related metric of a user, among other uses. The smart mat system 400 includes a control system 418, a memory device 440, one or more processors 432, a weight system 402, and a pressure sensing system 404. In some implementations, the smart mat system 400 further includes a bio-impedance system 406. In some implementations, the smart mat system 400 further includes a communications network 414.

[0071] As shown in FIG. 4A, the control system 418 includes the one or more processors 432 (hereinafter, processor 432). The control system 418 is generally used to control (e.g., actuate) the various components of the smart mat system 400 and/or analyze data obtained and/or generated by the components of the smart mat system 400. The processor 432 can be a general or special purpose processor or microprocessor. While only one processor 432 is shown in FIG. 5A, the control system 418 can include any suitable number of processors (e.g., one processor, two processors, five processors, ten processors, etc.) that can be in a single housing, or located remotely from each other. The control system 418 can be coupled to and/or positioned within a mat of the smart mat system 400, within a housing of one or more load cells 421 of the weight system 402, within a housing of one or more of the sensors 412 of the sensing system 404, or any combination thereof. The control system 418 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct). In such implementations including two or more housings containing the control system 418, such housings can be located proximately and/or remotely from each other.

[0072] The memory device 440 stores machine -readable instructions that are executable by the processor 432 of the control system 418. The memory device 440 can be any suitable computer readable storage device or media, such as, for example, a random or serial access memory device, a hard drive, a solid state drive, a flash memory device, etc. While only one memory device 440 is shown in FIG. 5A, the smart mat system 400 can include any suitable number of memory devices 440 (e.g., one memory device, two memory devices, five memory devices, ten memory devices, etc.). The memory device 440 can be coupled to and/or positioned within a mat of the smart mat system 400, within a housing of one or more load cells 421 of the weight system 402, within a housing of one or more of the sensors 412 of the sensing system 404, within a housing of a user interface (e.g., a mobile phone, a smart mirror), or any combination thereof. Like the control system 418, the memory device 440 can be centralized (within one such housing) or decentralized (within two or more of such housings, which are physically distinct.

[0073] In some implementations, the smart mat system 400 further includes an electronic interface (such as the user interface 136 of FIGS. 1-2). The electronic interface is configured to receive data (e.g., user input data) such that the data can be stored in the memory device 440 and/or analyzed by the processor 432 of the control system 418. The electronic interface can communicate one or more components of the smart mat system 400 using a wired connection or a wireless connection (e.g., using an RF communication protocol, a WiFi communication protocol, a Bluetooth communication protocol, over a cellular network, etc.). The electronic interface can include an antenna, a receiver (e.g., an RF receiver), a transmitter (e.g., an RF transmitter), a transceiver, or any combination thereof. The electronic interface can also include one more processors and/or one more memory devices that are the same as, or similar to, the processor 432 and the memory device 440 described herein. In other implementations, the electronic interface is coupled to or integrated (e.g., in a housing) with the control system 418 and/or the memory device 440.

[0074] In some implementations, the weight system 402 of the smart mat system 400 includes a plurality of load cells 421. For example, as shown in FIG. 4A, the weight system 402 includes four of four-by-four arrays of load cells: 422a, 422b, 422c, and 422d. Each of the four-by-four arrays of load cells is coupled to a respective analog to digital converter (ADC). In some such implementations, the ADC is a sigma-delta ADC, which may be used specifically for load cells, although other types of suitable ADCs are contemplated. For example, the array of load cells 422a is coupled to the ADC 424a; the array of load cells 422b is coupled to the ADC 424b; the array of load cells 422c is coupled to the ADC 424c; and the array of load cells 422d is coupled to the ADC 424d. [0075] In some implementations, the pressure sensing system 404 of the smart mat system 400 includes an array of pressure sensors. In some such implementations, the array of pressure sensors includes a matrix of pressure sensors of any suitable number. As an example, as shown in FIG. 4A, the array of pressure sensors includes a 3x2 matrix of pressure sensors: 412a-412f. As another example, the array of pressure sensors includes a 100x70 matrix of pressure sensors. Additional details and/or alternative implementations of the pressure sensing system 404 is discussed with regard to FIG. 4B, FIGS. 5A-5B, FIG. 6, FIGS. 8A-8F, FIGS. 9A-9C, FIGS. 10A-10B, and their corresponding description.

[0076] In some implementations, the control system 418 is configured to receive weight data from the weight system 402, and to receive pressure data from the pressure sensing system 404. Every user has a unique pressure map (e.g., like a finger print), as generated by the pressure data associated with the user. Based at least in part on the pressure data (received from the pressure sensing system 404) and registered user data (stored on the memory device 440 and/or transmitted from the communications network 414), the control system 418 is configured to determine whether that the user is a registered user or a non-registered user of the smart mat system 400.

[0077] If the user is a non-registered user of the smart mat system 400, in some implementations, based at least in part on a determination that a portion of the user is in contact with the smart mat system, the smart mat system 400 is configured to generate image data of the user. The determination can be made based at least in part on the weight data, the pressure data, or both. The generated image data is then compared with the registered user data, thereby verifying that the user is a non-registered user of the smart mat system 400.

[0078] In some implementations, a weight system can be more or less accurate depending on the weight location of the object being measured relative to the load cells’ location. The pressure sensing system of the present disclosure can determine the weight location and then correct (e.g., normalize) the weight inaccuracy caused by any suboptimal location of the object being measured. If the weight location is good (e.g., centered relative to the load cells), a display may show load cell weight as an actual weight of the user. If the weight location is determined to be suboptimal, a balanced weight is estimated based on the received pressure data and the received weight data, and the balanced weight is displayed as the actual weight of the user. Therefore, in some implementations, a load cell weight for the user is determined based on the received weight data. If the weight location meets a predetermined criteria (e.g., centered relative to the load cells, or within a predetermined distance range from any of the load cells), the load cell weight is displayed on a display device as an actual weight for the user. If the weight location does not meet the predetermined criteria (e.g., too close or too far from any of the load cells), in some implementations, a pressure sensor weight is estimated for the user based at least in part on the received pressured data, and the pressure sensor weight is displayed on the display device as the actual weight for the user; in other implementations, a balanced weight is estimated based on the received pressure data and the received weight data (e.g., the load cell weight), and the balanced weight is displayed as the actual weight of the user.

[0079] In some implementations, the weight for the user, as measured by the weight system 402, is not accurate enough to reflect the true weight of the user, and the smart mat system 400 can normalize it. First, a load cell weight for the user is determined based on the received weight data. The determined load cell weight is received as a first input for a machine learning algorithm. In addition, a reason for adjustment is received as a second input for the machine learning algorithm. The machine learning algorithm then generates an output, which is a normalized weight of the user. Additionally, in some implementations, the load cell weight for the user, and/or the normalized weight for the user, and/or the reason for adjustment are displayed on a display device. [0080] The reason for adjustment can include (i) a state of the user being dressed or undressed (e.g., clothes may add weight, different types of clothes may add various amounts of weight), (ii) a status of the user’s recent use of bathroom (e.g., lack of bowel movement may add weight), (iii) a time when the user last ate and/or drank (e.g., recent consumption of food or beverage may add more weight), (iv) a type of food of the user’s last meal (e.g., consuming carbohydrates and/or sodium may increase water retention), (v) a shower status (e.g., having wet hair may add weight); or (vi) any combination thereof.

[0081] Further, the machine learning algorithm can be trained with historical data. For example, in some implementations, the historical data includes historical load cell weight data and historical normalized weight data. The historical data can be associated with other users (e.g., registered user data of other users), and/or the current user of the smart mat system (e.g., from a third party activity tracker associated with the current user, or other activity tracking databases). In some implementations, the machine learning algorithm can be trained with sensor data measured by other sensors of the smart mat system. For example, in some such implementations, the data given by the plurality of electrodes 444 can be used to determine whether the user is wet or not.

[0082] If the user is a registered user of the smart mat system 400, a prompt is displayed on a display device for the user to input information to be associated with the received weight data. User information is then received in response to the prompt. Based at least in part on the received user information, the weight data is modified to output a normalized weight. Additionally, in some implementations, the modified weight data is displayed on the displayed device. The user information can include the same, or similar information as the reason for adjustment discussed herein. Similarly, the machine learning algorithm can be used if the user is a registered user. The machine learning algorithm can be further or alternatively trained using user specific data that is generated, over a period of time, by the user of the smart mat system 400.

[0083] In some implementations, the bio-impedance system 406 of the smart mat system 400 is configured to generate bioelectrical impedance data associated with the user. The bioelectrical impedance system 406 includes a plurality of electrodes 444 configured to conductively contact the user and form one or more closed circuits with the user. For example, as shown in FIG. 4A, a first pair of electrodes 444a, 444b forms a first closed circuit with the user (e.g., with a first foot of the user), and is configured to generate current data to be transmitted to a bioelectrical impedance module 438. A second pair of electrodes 444c, 444d forms a second closed circuit with the user (e.g., with a second foot of the user), and is configured to generate voltage data to be transmitted to the bioelectrical impedance module 438. Based at least in part on the bioelectrical impedance data (including the current data and the voltage data), the control system 418 is configured to determine an estimated body composition of the user, such as a body fat and a muscle mass of the user.

[0084] In some implementations, the communications network 414 of the smart mat system 400 includes a wireless communications module 426 and a pairing button 428. The wireless communications module 426 an include a BLE module, and/or a Wi-Fi module. The pairing button 428 can be physical or virtual. In some implementations, actuation of the pairing button 428 enables the control system to transmit data to and/or from the wireless communications module 426. In some implementations, the pairing button 428 is a wireless button. For example, in some such implementations, the pairing button 428 includes a Near Field Communication (NFC) button. [0085] While the smart mat system 400 in FIG. 4A is shown to include the control system 418, the memory device 440, the weight system 402, the pressure sensing system 404, the bioimpedance system 406, and the communications network 414, a smart mat system can include more or fewer components. As an example, in some implementations, a first alternative smart mat system can include a control system, a weight system, and a sensing system. As another example, in some implementations, a second alternative smart mat system can include a control system, a memory device, a weight system, a pressure sensing system, and a display device. As yet another example, in some implementations, a third alternative smart mat system can include a control system, a memory device, a weight system, a pressure sensing system, a bio-impedance system, and a user input module.

[0086] Referring to FIG. 4B, an illustrative block diagram of the electronic pressure sensing system 404 is shown, according to some implementations of the present disclosure. In some implementations, one or more components of the pressure sensing system 404 are coupled to a printed circuit board (“PCB”), which is in turn connected to the control system 418. In some implementations, the PCB is wired to another PCB (“PM PCB”) composed of a conductive row sheet (e.g., the first sheet 583 of FIGS. 5A-5B) and a conductive column sheet (e.g., the third sheet

585 of FIGS. 5A-5B). In some such implementations, the PCB is positioned between the substrate

586 and the base cover 589 (FIGS. 5A-5B). The conductive row sheet and the conductive column sheet can be made of any suitable materials, such as copper, gold, pewter, silver, etc.

[0087] In some implementations, a shift register is configured to multiply the number of outputs available; a SPDT MUX is a digitally controlled switch, such as a multiplexer where two possible output is chosen for each input; an AO is an operational amplifier; and a multiplexer is a multiplexer where one output is chosen out of several input. A main goal of the AO and the SPDT MUX is to avoid an electrical issue common to matrixes of sensors, such as cross-talk. A main goal of the shift register and the simple MUX is to make the reading simpler (fewer inputs and outputs of the control system are needed thanks to them).

[0088] In some implementations, the control system 418 force a voltage to the conductive rows through the shift registers and the SPDT MUXs. The control system forces an almost identical voltage as the positive input of the AO, and the negative input of the AO is coupled to the conductive columns. The control system selects one row (e.g., a shift register, and one column (e.g., a multiplexer), and apply a different voltage for this selection. In this configuration, the only electrical current present is forced to be between this one row and one column.

[0089] Referring to FIGS. 5A-5B, a smart mat system 500 is illustrated, where FIG. 5 A shows an illustrative partial exploded diagram of the smart mat system 500, and FIG. 5B shows a reversed partial exploded diagram of the smart mat system 500. The smart mat system 500 is the same as, or similar to, the smart mat system 400, where like reference numbers are designated for similar or equivalent elements.

[0090] The smart mat system 500 includes a cover layer 580 (e.g., a mat cover, a top layer), a generally opaque layer 581, an array of pressure sensors (including a first sheet 583, a second sheet 584, and a third sheet 585), a substrate 586, a plurality of load cells (including the load cell 521), a plurality of load feet (including the load foot 592), and a base cover 589. As shown, the plurality of load cells is coupled to a first side of the substrate 586. The array of pressure sensors is coupled to a second opposing side of the substrate 586. In some implementations, the substrate 586 is one or more pieces of glass, such as two pieces, four pieces, eight pieces, etc. In some implementations, the substrate 586 includes two pieces of glass coupled together via one or more hinges, so that the smart mat system 500 can be folded in half for easy transportation.

[0091] The number of layers provided in FIG. 5A is an example and some of the layers can be omitted or combined. Reducing the number of layers can reduce manufacturing cost and reduce manufacturing complexity. Reducing the number of layers can also reduce the thickness of the smart mat system 500. In some implementations, the conductive column sheet (i.e., third sheet 585) is omitted. The conductive columns can be applied directly to the surface of the substrate 586. For example, the substrate 586 can be glass, and the conductive columns can be provided directly on the glass. The conductive columns can be printed on the glass, sprayed on the glass, glued on the glass, or chemically deposited or chemically adhered on the glass. Example conductive material include copper, gold, pewter, silver, titanium, etc.

[0092] In some implementations, the conductive row sheet (i.e., the first sheet 583) is omitted. The conductive rows can be applied directly to the surface of the pressure sensors (e.g., the second sheet 584). For example, the second sheet 584 can be plastic or piezo resistive material, and the conductive rows can be provided directly on the plastic. There are different methods for applying the conductive rows to the second sheet 584, and these methods include printing, spraying, gluing, chemical abrasion, etc. Gluing can be used for low temperature processes in the situations where the second sheet 584 is plastic that cannot handle direct printing that can involve high temperature processes. Conductive material include copper, gold, pewter, silver, titanium, etc.

[0093] In some implementations, the cover layer includes a sheet of fabric. As shown, the cover layer 580 includes two electrically conductive fabric portions 578A, 578B spaced from each other. In some implementations, the two electrically conductive fabric portions 578 A, 578B are spaced from each other by a suitable distance, such as one inch, two inches, three inches, four inches, five inches, six inches, and up to a width of the cover layer 580. In some implementations, the two electrically conductive fabric portions 578 A, 578B are spaced from each other at least three inches. In some implementations, the two electrically conductive fabric portions 578 A, 578B are spaced from each other at a distance that a user’s feet are typically spaced apart.

[0094] In some implementations, a plurality of electrodes 544A, 544B is positioned between the opaque layer 581 and the cover layer 580. Two electrodes 544A, 544B are shown in FIGS. 5A- 5B. Each of the electrodes 544A, 544B is positioned directly below a respective one of the conductive fabric portions 578A, 578B. As such., even though the electrodes 544A, 544B are not exposed to the user, the electrodes 544A, 544B are still conductive via the conductive fabric portions 578A, 578B. In other words, in some implementations, the conductive fabric portions are configured to be in electrical physical connection with the electrodes. In some implementations, beneath the cover layer 580, the opaque layer 581 is positioned above the various components so that the various components underneath are not visible to human eye.

[0095] In this example, the electric current goes from the first sheet 583 (e.g., the upper sensor sheet) to the third sheet 585 (e.g., the lower sensor sheet) through the second sheet 584 (e.g., the pressure sensing layer). This sensor type may be called the “sandwich sensor,” in contrast to the “coplanar sensor” illustrated below (e.g., in FIG. 6), because for the “sandwich sensor” the pressure sensing layer is in a sandwich between the two sensor sheets.

[0096] The array of pressure sensors is configured to generate pressure data associated with the user. In some implementations, the array of pressure sensors is configured to generate the pressure data in response to the user engaging the smart mat system (e.g., standing on the cover layer 580). In some implementations, the array of pressure sensors includes the first sheet 583, the second sheet 584, and the third sheet 585. In some implementations, the first sheet 583 is a copper rows layer, and includes a plurality of electrically conductive rows 587. In some implementations, the third sheet 585 is a copper columns layer, and includes a plurality of electrically conductive columns 588. In some implementations, the second sheet 584 is a pressure sensitive sheet, and includes a piezoresistive sheet that is positioned between the first sheet 583 and the third sheet 585. The piezoresistive sheet is configured to change its electrical resistance in response to pressure being applied thereto. In some such implementations, the intersection of each of the plurality of electrically conductive rows 587 with each of the plurality of electrically conductive columns 588 forms and/or defines a pressure sensor (e.g., the pressure sensor 412 of FIG. 4A) of the array of pressure sensors.

[0097] The plurality of load cells being is to generate weight data associated with a user. In some implementations, the plurality of load cells is configured to generate the weight data in response to the user engaging the smart mat system (e.g., standing on the cover layer 580). In some implementations, each of the plurality of load feet is rigid, and is directly coupled to a respective one of the plurality of load cells. For example, as shown in FIGS. 5A-5B, the rigid load foot 592 is directly coupled to the load cell 521. The base cover 589 is coupled to the substrate 586 such that the plurality of load cells 521, the memory, and the control system are at least partially positioned between the base cover 589 and the substrate 586. In some implementations, the base cover 589 includes a plurality of apertures. Each of the plurality of rigid load feet protrudes at least partially through at least one of the plurality of apertures. For example, the load foot 592 protrudes partially through the aperture 591 of the base cover 589. As such, while the load cell 521 is not exposed to the ground, the load cell 521 is stabilized via its contact with the load foot 592, and is effectively stabilized on the ground.

[0098] In some implementations, one or more components of the smart mat system 500 form a smart mat, for example, a bath mat, a yoga mat, a doormat, an anti-fatigue mat, a chair cushion, a body pillow, a shoe insole, a portion of a carpet, one or more pieces of tile, one or more pieces of hardwood flooring, part of a mattress, part of a shower (e.g., coupled to or embedded in a shower pan or a bath tub), or the like. This is advantageous because some people and/or animals can have weight anxiety. Hiding the smart mat system in everyday items can also encourage continual monitoring of the weight, body fat distribution, and/or any health changes of the user. Furthermore, energy harvesting can be included in some of the above-referenced implementations, for example, using heat of the feet and/or dynamic pressure with a piezoelectric collector.

[0099] In some implementations, the smart mat includes all of the components shown in FIGS. 5A-5B. The smart mat can be of any suitable dimensions. For example, a length of the smart mat is between about 20 cm to about 250 cm, preferably between about 40 cm to about 120 cm, and most preferably about 80 cm. A width of the smart mat is between about 15 cm to about 120 centimeters, preferably between about 30 cm to about 80 cm, and most preferably about 50 cm. A thickness of the smart mat is between about 5 mm to about 5 cm, preferably between about 8 mm to about 2 cm, and most preferably about 1.5 cm. Additionally or alternatively, in some implementations, the load foot 592 extends from the base cover 589 by about 2 mm to about 3 mm.

[00100] In some implementations, the smart mat includes actuators for providing haptic feedback to users. Haptic feedback can be used in multiple situations. For example, haptic feedback (e.g., vibration) can be used to encourage the user to make a periodic measurement. For example, such haptic feedback can signal to the user that a daily measurement is completed. The user can stay on the smart mat until the vibration is provided. In such embodiments, the haptic feedback can be used as a cue by the user that the measurement process is complete and encourage the user to remain on the mat for a time period sufficient to capture the measurement. In some implementations, the haptic feedback is provided throughout the measurement, and absence of haptic feedback is an indication that the smart mat has finished taking measurements.

[00101] In some implementations, the haptic feedback is used to encourage the user to make measurements. For example, the haptic feedback can be a vibration that massages the user’s feet. In some implementations, the user’s mobile phone can provide notifications or prompts for obtaining subjective feedback from the user. The haptic feedback can continue massaging the user’s feet so long as the user engages with the mobile phone in responding to the provided prompts.

[00102] In some implementations, haptic feedback can be used to communicate different statuses to the user. For example, multiple haptic devices can be incorporated in the smart mat at different spatial locations in the smart mat. These different spatial locations can result in different haptic zones along the smart mat, so that a first zone vibrating and a second zone vibrating communicate different information. In one example, the first zone vibrating can be an indication for the user to rebalance on the smart mat, and the second zone vibrating can be an indication that the measurement is completed.

[00103] In some implementations, the cover layer 580, the opaque layer 581, the first sheet 583, the second sheet 584, and/or the third sheet 585 can be fabric that includes circuitry. FIG. 21 provides a partial perspective view of an example smart mat system 2100 that includes one or more fabric layers. The smart mat system 2100 will be described with reference to the smart mat system 500. The smart mat system 2100 includes a cover layer 2180. The cover layer 2180 can be a combination of the cover layer 500 and the first layer 583. The cover layer 2180 can have integrated a plurality of electrically conductive rows 2187. The second layer 584 is similar to a second layer 2184. The smart mat system 2100 includes an opaque layer 2181. The opaque layer 2181 can be a combination of the opaque layer 581 and the third layer 585. The opaque layer 2181 can have integrated a plurality of electrically conductive columns 2188. The opaque layer 2181 can include one or more electrodes 2144A or 2144B in some implementations. The different layers can be integrated via embroidery.

[00104] Referring to FIG. 6, a partial perspective view of a smart mat system 600 is shown. The smart mat system 600 is the same as, or similar to, the smart mat system 500, where like reference numbers refer to like elements and/or components. However, the array of pressure sensors of the smart mat system 600 includes two sheets (e.g., a first sheet 684 and a second sheet 682 in FIG. 6) instead of three (e.g., the first sheet 583, the second sheet 584, and the third sheet 585 in FIGS. 5A-5B).

[00105] As shown, the first sheet 684 of the smart mat system 600 is the same as, or similar to, the second sheet 584 of the smart mat system 500. In some implementations, the first sheet 684 is a pressure sensitive sheet, such as a piezoresistive sheet. In some implementations, the first sheet 684 is flexible. The second sheet 682 of the smart mat system 600 replaces the first sheet 583 and the third sheet 585 of the smart mat system 500 at once. In some implementations, the second sheet 682 includes a printed circuit board (PCB) having a plurality of electrically conductive trace patterns (e.g., 612A-612E) thereon. In some such implementations, each of the plurality of electrically conductive trace patterns forms and/or defines a pressure sensor (e.g., the pressure sensor 412 of FIG. 4A) of the array of pressure sensors.

[00106] In this example, for each electrically conductive pattern (e.g., each of 612A-612E), the electric current goes from the inner circle to the outer ring of the electrically conductive trace pattern, next to the first sheet 684 (e.g., the pressure sensing layer). The circle and the ring can change shape, such as the additional examples shown in FIGS. 8A-8F. Because the circles and the rings are not connected to each other (e.g., only the rings are connected to adjacent rings, and the circles are connected to adjacent circles), the electric current needs to go through a PCB layer (e.g., to avoid crosstalk because the density of the electrically conductive patterns). Thus, this sensor configuration can be called the “coplanar sensor,” as the circles and rings are all on a single layer (e.g., the second sheet 682). This coplanar configuration is described in further detail below with reference to FIGS. 10A-10B.

[00107] Referring to FIG. 7, an example pressure sensor formed and/or defined by an electrically conductive trace pattern 612 is illustrated, according to some implementations of the present disclosure. As shown, the electrically conductive trace pattern 612 includes an inner disk 605 (e.g., the “circle” FIG. 6) and an outer ring 603 (e.g., the “ring” in FIG. 6). In some implementations, the inner disk is also called the “inner pad,” and the outer ring is also called the “outer pad.” As disclosed herein, the inner pads and outer pads can take various shapes, and are not necessarily in circles / disks and rings. Additional shapes are disclosed with reference to FIGS. 8A-8F.

[00108] The inner disk 605 is spaced radially apart from the outer ring 603 by a first distance d. Examples of the first distance d include: 100 pm, 150 pm, 200 pm, 250 pm, 300 pm, 400 pm, 500 pm, 600 pm, 700 pm, 800 pm, 900 pm, 1000 pm, 1100 pm, 1200 pm, 1300 pm, 1400 pm, 1500 pm, 1600 pm, 1700 pm, 1800 pm, 1900 pm, and 2000 pm. In some implementations, the first distance d is 0.8 mm, which is 800 pm. In other implementations, the first distance d is 1.5 mm, which is 1500 pm. The outer ring 603 is formed around the inner disk 605 for a second distance 9. Examples of the second distance 9 include 90°, 135°, 180°, 225°, 270°, 315°, and 360°. As shown in FIG. 6, the electrically conductive trace pattern 612A has an outer ring that forms a perfect circle around the inner disk, where the second distance 9 is 360°. Alternatively, in some implementations, the outer ring 603 does not form all the way around the inner disk 605, and therefore has a second distance 9 smaller than 360° (best shown in FIG. 7).

Pressure Mapping System Test Results

Design Parameters

[00109] The design parameters for a single pressure sensor unit were investigated for their individual influence on performance of the pressure sensor unit. Performance of the pressure mapping unit can be defined by one or more of the following performance parameters: minimal detectable pressure, maximal detectable pressure, resolution, accuracy, repeatability, or the like. In this example, performance of the pressure mapping unit is defined by the minimal detectable pressure and the maximal detectable pressure. [00110] Referring to FIG. 8A, a pressure sensor unit of a smart mat system (e.g., the smart mat system 700) is shown. Using FIG. 8A as an example, the design parameters are: (i) line length L measured from the perimeter of the middle circle between the outer pad and the inner pad, (ii) line thickness e measured from the distance between the outer pad and the inner pad, (iii) external diameter d measured from the diameter of the circle such as one sensor is included in this circle and the 2-dimensionnal pavement of the sensor is based on this circle, (iv) internal diameter di measured from the diameter of the circle such as one sensor is inscribed in this circle, (v) pad thickness e_pad defined as the thickness of any given pad (e.g., the thickness of the outer pad, or the thickness of the inner pad). The pressure sensor unit in FIG. 8A includes an inner pad and an outer pad. The inner pad is shaped as a circular disk. The outer pad is shaped as a circular ring. As disclosed herein with reference to FIG. 7, the inner pad and/or the outer pad is made of an electrically conductive surface (or an electrically conductive trace pattern) on the same side of a PCB.

Impact of the Design Parameters

[00111] The test results show that the greater the pad thickness e_pad is, the more disparity resulted in the output measure (e.g., the calculated and/or estimated pressure). In fact, if e_pad is too high (e.g., > 0.5 mm for either the outer pad or the inner pad), the inaccuracy about the contact point between the pad and the pressure sensing sheet (684 in FIG. 6) is high, thereby causing poor performance. However, if the pad thickness e_pad is too low (e.g., < 0.1 mm for either the outer pad or the inner pad), there is not enough space for the connection between the pad and the pressure sensing sheet 684. That is why an optimal range for the pad thickness e_pad is needed for improved performance. In our tests, we found values between 0.1 mm and 0.4 mm to be the optimal range for the pad thickness e_pad, and preferably 0.2 mm.

[00112] In addition, in some implementations, the longer the line length L is, the less resistive the sensor unit will be. That is because the current has more available surface to travel. Therefore, the sensor unit with a longer line length L is better fit for a low pressure environment (e.g. , the pressure under the back of the body of a user during sleep). For example, for a single pressure sensor unit, if the line length L is increased, that pressure sensor unit will be more fit for a lower pressure environment than another pressure sensor with a shorter line length. However, conversely, the sensor unit with a longer line length L is less fit for a high pressure environment. [00113] Similarly, in some implementations, the greater the line thickness e is, the higher is the resistance of the sensor unit, which in turn means the sensor unit is better fit for a high pressure environment (e.g., the pressure under tires of an automobile).

Tested Sensor Designs

[00114] More than 20 sensor units were designed using the design parameters described in the section above. Six of those were selected for further testing, using various versions of the design parameters by changing slightly the shapes of the pads and/or the relative ratios. Specifically, in some such implementations, the e/L ratio was manipulated. The six sensor unit designs are illustrated in FIGS. 8A-8F. For example, in FIG. 8A, the line length L is 12 mm, and the line thickness e is 1 mm. In FIG. 8B, the line length L is 2 mm, and the line thickness e is 0.2 mm. In FIG. 8C, the line length L is 30 mm, and the line thickness e is 0.5 mm. FIG. 8D, the line length L is 9 mm, and the line thickness e is 0.25 mm. FIG. 8F, the line length L is 12 mm, and the line thickness e is 1 mm. FIG. 8G, the line length L is 12 mm, and the line thickness e is 1 mm.

[00115] To achieve high performance, the inner pad tends to be approximately in the shape of a generally circular disk. That is because this shape provides the best pavement density, in order to have a high pressure map resolution. In some examples, one, two, three, or four sensors per square centimeter produces the best pavement density. In addition, the test results show that the best pavement density with the best sensor coverture can be achieved with hexagonal sensor units, such as shown in FIG. 8C and FIG. 8D, the sensor coverture being the ratio of the sensing area (sensors) with the non-sensing area (between sensors).

[00116] Further, three sensor units (FIGS. 9A-9C) were selected and tested in a matrix. FIG. 9A illustrates a Hexagon sensor unit of a smart mat system (e.g., the smart mat system 700), with values shown in mm. For FIG. 9A, the line length L is 16.5 mm, and the line thickness e is 1.5 mm. Thus, the e/L ratio is 0.09. The optimal values can vary ±50% depending on the desired results, and the preferred values for this example are shown in the figure. The hexagon shape allows higher density pavement, as disclosed above. The outside ring (e.g., outside pad) connects to other outside rings using the top layer, whereas the inside ring (e.g., inside pad) connects to other inner ring using the bottom layer. The center dot marks the location of a via, which connects the top layer and the bottom layer and provides routing of the PCB.

[00117] FIG. 9B illustrates a comb sensor unit of a smart mat system (e.g., the smart mat system 700). The inner pad is in the center, encircled by the shape of the outer pad. For FIG. 9B, the line length L is 18 mm, and the line thickness e is 1 mm. Thus, the e/L ratio is 0.055.The optimal values can vary ±50% depending on the desired results, and the preferred values for this example are shown in the figure. The comb sensor unit, as shown, has five dots, which indicate the locations for the five vias, each of which connects the top layer and the bottom layer and provides routing of the PCB.

[00118] FIG. 9C illustrates a Double-C sensor unit of a smart mat system (e.g., the smart mat system 700). In this configuration, the inner pad and the outer pad can be either of the pads shown. For FIG. 9C, the line length L is 10 mm, and the line thickness e is 1.5 mm. Thus, the e /L ratio is 0.15. The optimal values can vary ±50% depending on the desired results, and the preferred values for this example are shown in the figure. The Double-C sensor unit, as shown, has four dots, which indicate the locations for the four contact points that connect inner pads to each other and outer pads to each other. The advantages of the Double-C sensor unit include that it does not need any vias, therefore it can be used on a single side of a sheet.

[00119] FIG. 10A illustrates a portion of a pressure sensing matrix of a smart mat system, such as in the smart mat system 600 shown in FIG. 6. The arrangement of the pressure senor units shown in FIG. 10A is the same as, or similar to, how the pressure sensor units (e.g., the pressure sensor units shown in FIG. 6, FIGS. 8A-8F, FIGS. 9A-9C) can be arranged. For example, for each of the pressure sensor units (e.g., 1012a, 1012b, 1012c, 1012d), both the inner pads and the outer pads are located on a same layer (e.g., similar to the second sheet 682 of the smart mat system 600) that is on one side of a PM PCB 682 and in contact with a pressure sensing sheet (e.g., similar to the first sheet 684 of the smart mat system 600). The pressure sensor units include electrically conductive trace patterns. In this example, each pressure sensor unit has an inner pad shaped in a hexagon, and an outer pad shaped also in a hexagon.

[00120] FIG. 10B shows an exploded view of the pressure sensing matrix of FIG. 10A. Each outer pad is connected to the adjacent outer pad in the column direction via, for example, an electrically conductive trace forming the column (e.g., a “column trace”), which is also on the same layer as the inner pads and the outer pads. As shown, the outer pad of the sensor unit 1012a is connected to the outer pad of the sensor unit 1012b via a first column trace 1032 on the same layer as the outer pads themselves; and similarly, the outer pad of the sensor unit 1012c is connected to the outer pad of the sensor unit 1012d via a second column trace 1034 on that same layer. [00121] The inner pad is connected to the adjacent inner pad in the row direction on the opposite side of the PCB layer 1082, via an electrically conductive trace forming the row (e.g., a “row trace”) and through their vias (marked by the center dots). The vias provide the connection from the top layer (where the column traces, the inner pads, and the outer pads are) to the bottom layer (where the row traces are). As shown, the inner pad of the sensor unit 1012a is connected to the inner pad of the sensor unit 1012c via a first row trace 1036 on a separate layer from the inner pads themselves; and similarly, the inner pad of the sensor unit 1012b is connected to the inner pad of the sensor unit 1012d via a second row trace 1038 on that separate layer. The row traces are located on a separate layer from the rest of the traces (e.g., the column traces, the inner pads, and the outer pads), because the row traces need to overlap a portion of the outer pads without touching the outer pads.

[00122] Because the inner pads and the outer pads are not connected to each other (e.g., only the inner pads are connected to adjacent inner pads, and the outer pads are connected to adjacent outer pads), the electric current needs to go through the PCB layer 1082. Thus, this sensor configuration is called the “coplanar sensor,” as the circles and rings are all on a single layer. When pressure is applied on the conductive material comprising the inner pads and the outer pads, the parameters of the connection are changed, and the pressure is then calculated and/or estimated.

[00123] Referring briefly to FIG. 9C, for the Double-C sensor, both the inner pad and the outer pad of each sensor unit can be joined with respective adjacent sensor units without having to overlap, thus only one layer is needed for this configuration.

[00124] After testing the sensor units shown in FIGS. 9A-9C, the best sensor unit for use in the smart mat system 700 is the Hexagon sensor unit (FIG. 9A). The threshold for detecting pressure (e.g., the minimal detectable pressure) is low enough to be able to detect an arch of a foot of an individual (e.g. , between the plant and the heel), where the pressure is below 15 kPa. The range of detectable pressure for the Hexagon sensor unit with the measurements shown in FIG. 9A is 5 kPa - 120 kPa. If we raise the line length L or if we decrease the line thickness e, those values will decrease (e.g., both the minimal detectable pressure and the maximal detectable pressure will decrease). If we decrease the line length L or if we increase the line thickness e, those values will increase (e.g., both the minimal detectable pressure and the maximal detectable pressure will increase). [00125] The superior performance of the Hexagon sensor unit is illustrated in FIGS. 11A-1 IB. FIG. 11 A shows an image of the generated pressure map of a foot using the Comb sensor (FIG. 9B) matrix or the Double-C sensor (FIG. 9C) matrix (which have similar performance); whereas FIG. 11 B shows an image of the generated pressure map of the same foot using the Hexagon sensor (FIG. 9A) matrix. Each square on the pressure map represents pressure detected by one pressure sensor unit in the pressure sensor matrix. The output of each pressure sensor unit is a value of the detected pressure. In the pressure map, the intensity of the color indicates the relative detected pressure (e.g., the higher the detected pressure, the darker the color). As shown, the Hexagon sensor matrix is able to detect the arch of the foot (see FIG. 1 IB), whereas the Comb sensor matrix or the Double-C sensor matrix is not (see FIG. 11 A).

Piezoresistive Material

[00126] In addition to the pressure sensor designs disclosed above, the pressure sensing system of the present disclosure can be further optimized using piezoresistive material. Even though any piezoresistive sheet could be used, because the electrical specifications change with the specific material, a pressure sensor unit may not be adapted to all materials. Thus, various piezoresistive materials were tested, and MVCF-4S50K was found to be optimal to enhance the pressure sensing system. FIG. 12 illustrates behavior curves of two different piezoresistive materials. X-axis shows the pressure applied (kPa), and Y-axis shows the measured electrical voltage (mV). As shown, MVCF-4S50K is more linear compared to the conventional piezoresistive material, which means MVCF-4S50K is able to capture more information and is easier to calibrate.

[00127] In some implementations, MVCF-8S50K and MVCF-8S10K are also optimal. These materials (e.g., MVCF-4S50K, MVCF-8S50K, and MVCF-8S10K) provide the best dynamic performance because they tend to have greater thickness compared to other similar piezoresistive materials in the market (e.g., 8 mil instead of 4 mil), thus the dynamic behavior of the thicker materials, when subject to pressure, is more linear, which in turn means the dynamic range of measured pressure would be wider.

Electronics Circuit

[00128] The electronics circuit for the pressure mapping system is improved by this disclosure. The electronic circuit is needed to address the sensor matrix, and to read the resistance from each pressure sensor unit of the matrix. One objective of the electronics circuit is that it needs to suppress cross-talk (or ghosting). Ghosting is a common phenomenon for electronic matrixes. The issue is that one sensor unit can pollute and/or change the value of another sensor unit by sending a tiny current, thus creating a ghost value that the system cannot determine whether the detected value by the affected sensor is real or polluted.

[00129] The disclosed analog circuit makes it difficult or even impossible for the pressure sensing matrix to create ghosting, which is achieved by imposing exactly the same voltage at the positive and negative input of the operational amplifier of the matrix. When the same voltage (plus or minus input voltage difference of the Operational Amplifier) is imposed at two points of a branch of an electronic circuit, no current can flow in that branch of the circuit. In other words, the only current possible between those two points of the branch is between the row and the column the system is supposed to read, so that the system can be sure that the read value is the real one without ghosting.

[00130] FIG. 13 illustrates the working principle of this anti-ghosting electronics circuit. VA is applied to both positive and negative input of the amplifiers. This annihilates the current flowing in the branches. For the selected branch, the input voltage is VB, SO the current can flow through the branch and therefore is impacted by the pressure sensing sensor. When reading the output of the global matrix, the control system knows where the current come from as it can only come from the selected branch.

[00131] FIG. 14 illustrates the schema of the circuitry of the pressure mapping. The Row Selection is used to apply a certain voltage to the branch (VA or VB). The Velostat is the pressure sensing sheet. The Column OpAmp annihilates the current in case the input voltage is VA. The Column Selection redirect a column signal to the control unit. The Non-Linear OpAmp, Voltage Inverter and Voltage Divider are noise-filtering circuit. The ADC signal is read by the control system.

[00132] Additionally, or alternatively, the pressure mapping system of the present disclosure can incorporate another specific circuit that provides compensation for the pressure mapping system. FIG. 15 illustrates this circuit of compensation (2V5+e) for the pressure mapping system. Thanks to this compensation circuit, the error in voltage due to components is absorbed, and the pressure mapping system can be sure that the voltage in input and output of the circuit are exactly the same. The compensation generates a voltage equal to the input voltage difference which is specific to the used Operational Amplifiers. In addition, in some implementations, the smart mat system of the present disclosure may incorporate a semiconductor chipset that integrates all of this circuitry to reduce cost of the pressure mapping system.

[00133] The control system harvests the signal from the pressure sensing circuit using board- to-board connector, as electronics are placed on two different PCB (PCB and PM PCB 682 as they are called in claim 77). The board-to-board connectors could be replaced by any other connectors: ribbon, header, VGA, HDMI etc. However, the replacement of board-to-board connector will raise signal noise. Board-to-board connectors are the most efficient ones.

[00134] FIG. 20 provides an alternative circuitry of a pressure mapping system for antighosting, according to some implementations of the present disclosure. A resistive divider (e.g., R1 and R3) can be used to generate a voltage that is provided to the analog to digital converter (ADC). The piezoresistive material is modeled as a variable resistor that changes resistance in response to an input pressure. lin one embodiment, the resistance of the variable resistor is around 1 kOhm. The resistors R1 , R2, and R3 are chosen to be around the same order of magnitude of the variable resistor. That is, Rl, R2, and R3 can be between 100 Ohms and 20 kOhms. In some embodiments, the alternative circuity of FIG. 20 takes up a much smaller area compared to the circuit of FIG. 14 which requires operational amplifiers (opamps). In some implementations, the alternative circuitry of FIG. 20 takes up to 90% less space and cost than the circuitry of FIG. 14. Furthermore, becasue opamps are avoided, the alternative circuitry of FIG. 20 can be printed on fabric, glass, etc., thus removing a need for a printed circuit board. The resistors Rl , R2, and R3 may not eliminate all ghosting currents, but they improve the signal to ghosting current ratio by reducing the magnitude of ghosting current by one or more orders of magnitude compared to the signal.

Software App and Datavision

[00135] In some implementations, the vision of the disclosed smart mat system is: “Weight is just an index, balance is the real purpose. Stop being anxious because of your weight.” To achieve this vision, we must allow the user to visualize their data better than simply displaying raw numbers and matrices. In some implementations, the user’s progress may also be visualized, which will not be using numbers but colors or other illustration. For example, FIGS. 16A-16C illustrate some pictograms for a user’s journey story, where FIG. 16A shows the user can step off their shower on the smart mat system, FIG. 16B shows the smart mat system provides footprint recognition to identify the user, and FIG. 16C shows the smart mat system can record data every day and provide consultation any day.

[00136] In some implementations, the software is running on the backend and/or in the app itself. In some implementations, the software is run in the device (e.g. processor 132, memory device 140) as an embedded software. The software may include several algorithms that calculate indexes, output, values, and images. For example, in some implementations, the generated pressure map may be compressed first (e.g., using embedded software), to allow faster transfer because the file size is smaller. In additional, in some implementations, in the backend, the pressure map may be used to recognize the user. The pressure map may also be used to calculate the center of pressure, the center of gravity, to analyze a posture index, a balance index, a shoe size, etc. Algorithms of deep learning can also be used to recommend exercises to the user and/or measure feedback to offer more personalized and efficient exercises day after day.

[00137] For example, a new measurement (e.g., raw data measured by the disclosed smart mat system) may be used to determine gross weight, pressure map, body impedance, etc. In some implementations, an analysis module comprising analysis algorithms then generates user ID, beautified heat map, center of pressure, center of gravity, body composition, balanced weight, posture, balance, foot characteristics, Body Mass Index (BMI), or any combination thereof. In some implementations, a personalized module comprising personalized algorithms generates posture exercises, balance exercises, medical checkup, foot exercises, or any combination thereof. In some implementations, personalization can further include coaching, exercises, index calculations, or any combination thereof. Additionally, or alternatively, in some implementations, personalization shines through its unique and innovative data display, which is disclosed in more detail below.

[00138] Individuals differ in their emotional apprehension of health data; thus their health data could be considered as emotional data for some people. A main example is the weight: some people want to know their weight several times a day (e.g. weight-addicted people), every day or so (e.g. athletes) and never (e.g. weight-anxious people). The smart mat system of the present disclosure offers several modes of displays, which may appeal to different types of people depending on how they view their health data. In some implementations, the app offers two may display options: data savvy display, and data anxious display. In the data savvy display, the user is able to see all the raw numbers, such as the raw data measured, the evolution of the data through time, the complex analysis, etc. This is to satisfy the need of the user to understand their body, to follow up their health data, to always have direct access to all information. An example of such data savvy display is illustrated in FIG. 17, according to some implementations of the present disclosure.

[00139] Conversely, in the data anxious display, raw numbers are not displayed to the user. Even a color indication (such as red or yellow), and/or a message saying the user is not going toward their objective, may hurt the feeling of the user, thereby instilling a bad mood and breaking the trust rapport between the scale/coach and the user. Showing the wrong display may jeopardize the user’s benefits, as it will not help the user going towards its objective. Thus, a data anxious display of the present disclosure hides the raw numbers, and only alerts the user of the evolution of their progress with something encouraging, such as a pep talk sentence, and/or alerting the user of their body health using non-traditional color (e.g., outside of the classic green vs. red colors). In some implementations, the data anxious display represents the evolution through time without the need of a traditional graphic that is typically associated with expected progress.

Additional Software Aspects

[00140] As disclosed herein, in some implementations, a posture index is determined by the system, and may also be displayed to the user. The posture index is an index for the user to understand how he/she stands, which in turn helps the user monitor their posture evolution. For example, the posture index may be determined based on the center of pressure and the geometric center of the user (e.g., the more the distance between the two, the worse the posture is), the center of pressure being the point where the weighted relative position of the distributed pressure sums to zero and the geometrical center being the middle point of the bisector line between the two feet. [00141] In some implementations, the posture index may be determined based on the center of pressure of the user, the feet location of the user, a statistically determined point for the user, or any combination thereof. The statistically determined point is determined by, for example, previously collected data (e.g., tests) where users are asked to stand at specified location of a specific material, and a point is determined based on the posture evaluated by a doctor and the characteristic of the user (e.g. shoe size). A first index is then qualified based on the distance between the center of pressure of the user and the statistically determined point. If the user cannot replicate the specified location on a smart mat, the smart mat system (e.g., via an algorithm) moves the footprint to artificially replicate the test situation. Then, the smart mat system can determine the first index. The final posture index is determined based on the first index and the move applied: the more the footprint has been modified (e.g., moved by the algorithm), the worse the posture.

[00142] For example, as shown in FIG. 18A, a first offset between the center of pressure and the geometric center is illustrated, where X-axis unit is pixels, and Y-axis unit is pixels. As shown in FIG. 18B, a second offset between the center of pressure and the geometric center is illustrated, where X-axis unit is pixels, and Y-axis unit is pixels. The pressure heat map of two feet of a user is shown in each plot. Based on the generated pressure heat map, the center of pressure and the geometric center are calculated. In the example of FIG. 18A, the distance (or offset) between the center of pressure and the geometric center is 0.71 mm. In the example of FIG. 18B, the distance (or offset) between the center of pressure and the geometric center is 4.845 mm. Because the offset in FIG. 18B is longer than the offset in FIG. 18A, the posture of the user in FIG. 18B is worse than that of FIG. 18A. The distance (e.g., offset) can also be compared to global data to assess the posture of an individual regarding normal posture. The distance can also be analyzed through time, to determine an evolution of posture.

[00143] In some implementations, machine learning algorithms may be trained on data recommended by doctors and/or scholars. Clinical studies may also be utilized in which the pressure map may be crossed with 3D scanners of the user to extrapolate the 3D posture of the human body based on the pressure map. In some implementations, the data of 3D scanners coupled with plantar pressure may be used to assess the 3D posture based on plantar pressure data only. The algorithm for this assessment can be a mathematic search within a dataset, a supervised machine learning, an unsupervised machine learning, or other.

[00144] In some implementations, a balance index is determined by the system, and may also be displayed to the user. For example, the center of pressure of the user may be observed over a period of time. For example, the balance index may be determined based of the representative area where the center of pressure evolves during a period of time and the acceleration of the center of pressure during a period of time. For example, FIG. 19 illustrates a center of pressure ellipse, where X-axis is the X-location of the points, in cm, and Y-axis is the Y-location of the points, in cm. The wiggly line is the trajectory of the center of pressure. The ellipse (broken line) is the representative area where the center of pressure evolves. The thicker straight line, the thinner straight line, and three data points are data used for the construction of the ellipse. If the user moves a lot, long and fast, the system may determine that the user has a bad balance. This can be determined by a long trajectory of the center of pressure, or a wide representative area where the center of pressure evolves. The balance of the user may be observed in time to analyze its evolution. In some implementations, the bad evolution of balance could also be the sign of a pathology or an increased fall risk. Thus, in some such implementations, the balance index may also provide a fall prevention feature, a pathology prevention feature, a treatment evaluation feature, a mobility training feature, or more.

[00145] In some implementations, a balance index can be determined that describes the trajectory of the center of pressure. Parameters include the length of the trajectory can be calculated, an area of the smaller ellipsis containing 95% of the trajectory can be determined, and/or a stabilization time of the trajectory can be determined. One or more of these parameters can be combined to determine the balance index. For example, the balance index can be a number that ranges from 0 to 100. The balance index can be related, for example, to a senior fall risk as the probability of an individual to have a fall. In some implementations, higher stabilization times of the trajectory and larger areas of the smaller ellipsis can indicate that the user has some trouble maintaining a stable position, which can signify that the individual may have vestibular symptoms. [00146] In some implementations, a balanced and/or normalized weight is determined by the system, and may also be displayed to the user. Some traditional scales take bad (e.g., inconsistent or inaccurate) measurements due to different postures of the user and location of each foot. The smart mat system of the present disclosure solves this problem by providing post-measurement calibration and correction using the pressure map. As the smart mat system knows where the user stands, it can calculate how the weight measurement has been impacted by the user’s foot location, and the weight of the user can then be balanced and/or normalized using an algorithm.

[00147] Additional data may also be measured and cross among one another to train algorithms to improve at-home medical devices. For example, the pressure mapping can be trained such that a 3D posture can be extrapolated based on a given foot pressure. In some implementations, pathology schemes may also be detected and/or recognized. For example, early detection of Alzheimer’s Disease is made possible based on the balance index; early detection of scoliosis is made possible based on the posture index; etc. The collected data may also be used to coach the user doing rehabilitation after an injury, to qualify the effectiveness of a treatment, etc.

[00148] In some implementations, the smart mat system 100 can recognize and/or distinguish between different users of the smart mat system 100. For example, the smart mat system 100 may be provided in a medical facility, an elderly home, a multi-resident care facility, or a multi-resident home. Because multiple users can monitor and track their health using the smart mat system 100, these users may share the same smart mat system 100. Instead of requiring users to identify themselves (e.g., such as physically selecting their user profiles prior to obtaining health data from the users), the smart mat system 100 can recognize users based on one or more features detected from data associated with the user.

[00149] The smart mat system 100 can store reference features associated with registered users. For example, the reference features may be derived from health data including a pressure map of the left foot of a user, a pressure map of the right foot of the user, a measured weight of the user, a center of gravity (i.e., a center of pressure) associated with the user, an arch type associated with the user, a foot size of the user, a distance between the feet of the user, a shape of the feet of the user, widest part of the user’s foot, length of the user’s foot, or any combination thereof. When the smart mat system 100 obtains an input, for example, a first user steps on the MAT 110 and a weight and pressure maps of left and right feet are generated for the first user. The smart mat system 100 can then compare the obtained input to the stored reference features to automatically recognize the first user as the user that stepped on the MAT 110.

[00150] The process of comparing the reference features to the obtained input can be performed in multiple ways. For example, features of the obtained input can be determined using principal component analysis (PCA) algorithm. Applying the PCA algorithm results in vectors that describe features of the obtained input. The vectors are then compared against vector representations of the reference features. For example, a convolutional neural network (CNN) can be used to determine distances between vectors. The distance between the vector representation of the reference features and the vector representation of the features of the obtained input is then used for identifying the user associated with the obtained input.

[00151] In an example, there are five registered users (userl, user2, user3, user4, and user5) in a home. Each of the registered users has an associated vector representation of reference features (vector_refl , vector_ref2, vector_ref3, vector_ref4, and vector_ref5) stored in the smart mat system 100. When an unidentified user steps on the MAT 110 of the smart mat system 100, the smart mat system 100 obtains inputs and determines a vector representation of the obtained inputs (vector input) as previously described. The smart mat system 100 compares vector input to each of vector_refl, vector_ref2, vector_ref3, vector_ref4, and vector_ref5 to determine the distances between each of the reference features and the obtained inputs. That is, the smart mat system 100 will determine a distance 1, distance2, distances, distanced, and distances pertaining to the distances between the stored reference features and the obtained inputs.

[00152] From the measured distances, in some implementations, the smart mat system 100 determines that the unidentified user is a registered user of the smart mat system 100 if the corresponding distance is below a threshold. For example, the distances {distance 1, distance2, distances, distanced, distances} can be normalized such that a distance of 1 or less between, for example, vector_refl and vector_input indicates that vector_input belongs to registered user userl . [00153] In some implementations, the distances determined are normalized to values such as the distances can be interpreted with threshold as previously discussed. The normalized values can be indicative of similarity or a confidence that the smart mat system 100 has in its prediction. For example, if {distancel, distance2, distances, distanced, distances} computes to {2.7, 4.8, 3.2, 0.7, 7.3}, then the smart mat system 100 can determine that one or more of the calculated distances meets a similarity threshold. For example, the similarity threshold can be set to a value below 1, inclusively, indicating a high confidence, typically above 90% or above 95%. That is, if the similarity threshold of 1 is met and/or exceeded, then the minimum distance that meets or exceeds this similarity threshold is indicative of the user. In some implementations, the similarity threshold can be viewed as a consideration threshold whereby values exceeding the consideration threshold are eliminated from consideration. So that the minimum distance check is performed only on the subset of distances that are less than or equal to the consideration threshold. In the example above, distanced corresponds to 0.7, thus, the unidentified user is determined to be user4. A similarity threshold of 1 is used here as an example, but other thresholds can be used, for example 0.8, 1.5, 10, 100, or any values for which the distances are normalized for.

[00154] In some implementations, similarity can be interpreted differently based on the distances normalized to values between 0 and 1. The normalized values can be indicative of similarity or a confidence that the smart mat system 100 has in its prediction. For example, if {distancel, distance2, distances, distanced, distances} computes to {0.6, 0.5, 0.7, 0.02, 0.3}, then the smart mat system 100 can determine that one or more of the calculated distances meets a similarity threshold. For example, the similarity threshold can be set to a value between 0 and 0.1, inclusively, indicating a 100% to 90% confidence, respectively. That is, if the similarity threshold of 90% is met or exceeded, then the minimum distance that meets or exceeds this similarity threshold is indicative of the user. In the example above, distanced corresponds to 0.02, indicating a 98% similarity, and only distanced meets or exceeds the 90% similarity threshold. Thus, the unidentified user is determined to be userd. 90% similarity threshold is used here as an example, but other thresholds can be used. For example, 85%, 95%, 97%, etc. These values are merely used as examples, and other thresholds and normalization techniques can be applied.

[00155] In some implementations, if all distances are equal to or above the consideration threshold, then the smart mat system 100 can determine that the unidentified user is a new user. The smart mat system 100 can generate a profile for the new user. For example, if the distances {distancel, distance2, distances, distanced, distances} correspond to {2.3, 1.7, 6.d, 2.8, 4.2}, then the smart mat system 100 can determine that a user6 profile should be created. In some implementations, the smart mat system 100 provides an indication to whether a new user profile should be created. The numbers provided herein are merely examples, and other thresholds or normalization schemes may be used. For example, the distances can be normalized between 0 and 1 such that a value below 0.1 indicates similarity, while a value above 0.1 indicates dissimilarity. [00156] Similarity and consideration thresholds are merely provided as examples for determining confidence. Other methods for determining confidence are contemplated. For example, a convolutional neural network algorithm that provides the distances can also provide an associated confidence score associated with each of the distances. The confidence score can be an indication of an error associated with the methodology used by the convolutional neural network in determining the distance. The confidence score can also be an indication of an error associated with noise in the obtained inputs used in determining the distance.

[00157] In some implementations, the number of registered users can directly affect the determined confidence scores. For example, as the number of registered users increases, the magnitude of the determined confidence scores decreases. That is, as more users are added to the smart mat system 100, the feature space for the different reference vectors associated with the users becomes more crowded. With increased crowding, there is a higher probability of confusing two or more users, especially if the feature space has a low dimensionality. For example, a feature space that only considers two dimensions (e.g., a left foot pressure map and a weight) can get crowded quicker than one that considers three dimensions (e.g., a right foot pressure map, a left foot pressure map, and a weight). Increasing dimensionality can also help distinguish between users that have similar measurements. For example, two users may have a similar shoe size and weight, so if only these two features were considered, then the smart mat system 100 may not be able to distinguish between the two users with high confidence. But if the two users have different spacing between their feet or different center of gravity, then the additional information provided by these features can help distinguish between the two users.

[00158] In some implementations, the smart mat system 100 determines a confidence associated with the distances by using features not included in the distances. For example, if each of the distances {distancel, distance2, distances, distanced, distances} was determined using the arch type associated with the user, a foot size of the user, and a center of pressure associated with the user, then the confidence score can be determined using the widest part of the user’s foot and/or the length of the user’s foot. That is, different features can be used for determining features vs. confidence scores. In some implementations, the features and confidence scores can have an overlap with the features used to determine both. For example, the foot size of the user can be used for both determining the features and the confidence scores.

[00159] In some implementations, the smart mat system 100 determines a confidence associated with the distances. That is, each of the distances (distancel, distance2, distances, distanced, distances) is a distance vector rather than a magnitude. For example, distancel can be a distance vector that describes on a first dimension a difference between userl ’s reference weight and an obtained input weight, on a second dimension a difference between userl ’s reference foot length and an obtained input foot length, on a third dimension a difference between userl ’s reference center of gravity and an obtained input center of gravity. The smart mat system 100 can determine to have a higher confidence in a distance vector based on one or more of the dimensions having a normalized distance close to zero. For example, if the first and second dimensions of userl ’s distance vector have normalized distances of 0.03 and 0.04, respectively, then the smart mat system 100 can have a first confidence score associated with the distance vector of userl . If the third dimension of userl ’s distance vector has a normalized distance of 0.9 and no other distance vector matches the obtained inputs better than userl ’s distance vector, then the smart mat system 100 can determine that there is an error in the obtained inputs associated with the third dimension. [00160] In some implementations, the smart mat system 100 can update the reference values associated with the third dimension after determining that the obtained inputs reflect a most accurate representation of userl ’s health data. That is, over time, the smart mat system 100 can update the stored references used for comparison. For example, the stored references can be updated after obtaining three measurements in three different sessions that agree with one another. In some implementations, the update can occur after one, two, four, six, etc., measurements that agree with each other.

[00161] Because convolutional neural networks can be used in determining distance and similarity, the smart mat system 100 can use a number of different input formats. For example, pressure maps can be provided as images as illustrated in the examples of FIGS. 11 A, 11B, 18A and 18B. The images can be preprocessed prior to analysis by the convolutional neural networks. For example, the images can be resized to a standard or normal size for image processing. The images can be rotated to obtain a standard orientation for image processing. The images can be filtered to remove outlier pixels, for example, the images can be low-pass filtered prior to image processing.

[00162] The obtained inputs used for identifying and/or recognizing users can be secondarily obtained. For example, a pressure map for a left and a right foot can be used to determine shoe sizes for the left and the right foot, respectively, and/or arch indices for the left and the right foot. Left and/or right foot shoe sizes can be used as features when identifying a user. That is, a left and/or right foot reference shoe size can be stored or represented as part of a reference vector. The smart mat system 100 can use obtained pressure maps to determine shoe size for comparing against the reference.

[00163] In some implementations, when determining the reference shoe size, a user is asked to lean on her heels and toes for a fixed amount of time, with multiple measurements made to capture feet geometry of the user. The multiple measurements are preprocessed by combining all measured parts of the feet into a single measure of the whole foot. The single measure is then passed through a convolutional neural network that determines whether the toes and heels are sufficiently visible in the single measure of the whole foot. If the toes and heels are sufficiently visible, then both feet are separated, rotated and cropped using the inner foot tangent. This is performed to make the toes point upward. After the processed steps, the feet are upscaled and blurred with a Gaussian blur to enhance granularity of the measured foot lengths. After these steps, then each foot image is ready for computing of geometric features. Geometric features that can be obtained include a vertical foot length, a foot diameter (i.e., maximum length of a line that fits inside the foot which also defines a top and bottom pixel), a foot diameter angle with the inner foot tangent, foot width samples at fixed intervals, top and/or bottom pixel pressure, top and/or bottom mean pressure in a defined neighborhood or area of the foot, or any combination thereof. The geometric features can be provided to a classifier that outputs shoe size of the user.

[00164] In some implementations, arch indices for the left and the right foot can be determined from the pressure. An arch index can classify an arch of a foot as low-arch, normal-arch, or high- arch. A classifier can look at different features of the foot to determine the arch index. Examples of features include foot surface, arch surface, plantar arch index, plantar arch average pressure, or any combination thereof. The foot surface can be determined by counting a number of pixels representing the foot of the user and dividing this value by a total number of pixels in the captured image. In some implementations, the pressure map is cropped to include mostly the foot (e.g., as shown in FIG. 11B). The arch surface can be determined by counting a number of pixels representing a middle of the foot of the user and dividing this value by the number of pixels representing the foot of the user. The plantar arch index compares the width of the arch middle of the foot) to the width of the heel (at a tangent). The plantar arch average pressure compares the average pressure at the arch to the average pressure of the heel (at a tangent).

Additional Implementations

[00165] The smart mat system may be implemented in other situations that do not involve a specific individual and their feet. For example, the smart mat system may be a trigger device for detecting flood in a building (e.g., a commercial building, a residential home). For example, the smart mat system may be equipped with a flood detection component to alert the hotel when a flood occurs in the bathroom or in the room.

[00166] In some implementations, the PM PCB 682 in the smart mat system could be suppressed. In that case, the scheme for the pressure mapping sensors would be printing in metallic material (e.g., spray metalization used for glass bottles, packaging, etc.). In some implementations, the support surface replacing the PM PCB 682 could be glass 686, plastic 689, directly the piezoresistive material 684, or any new component or material not included in the current design. In some implementations, to make the product thinner without losing resistance, the glass plate 686 may be engraved to introduce the load cell directly inside the glass plate instead of on it.

[00167] In some implementations, the pressure data could be used to measure the weight without the need of any other data. In that case, the mechanical design of the product is strongly optimized to be thinner, more flexible, more resistant, and more practical. For example, the bottom plastic cover 689, the foot 692, the load cells 621, the glass plate 686 could be suppressed. In that case, all the electronics and the pressure sensing system could be sealed in a mat. This product could have all of the features mentioned above.

[00168] The pressure mapping may also be implemented in a variety of scenarios. For example, golf teaching and training platform, video games controller, footprint lock for doors, doormat, mattress, shower pan, tiles, couch, office chair, dining chair, wheelchair, car seat, etc. To achieve all these applications, for example, the pressure sensors may be disposed on a flexible plastic, such as by putting the pressure sensors on a flexible PCB, etc.

[00169] In some implementations, the smart mat system is paired with a camera (or a smart mirror) to measure both foot pressure and image of the body, which will in turn be used to generate a 3D map of the human body. For example, a time visualization of their body may be generated (e.g., tracking of physical weight, of posture deformity, of a mole, of a broken limb, and how the various changes affect the user’s balance, posture, stability, etc.).

[00170] In some implementations, the smart mat system of the present disclosure also includes a device using laser or ultrasound to measure the distance. This device would be installed just at the top of the smart mat system to measure the user’s height while he or she performs an action (e.g., a pose or an exercise). The body evolution of the user (e.g., the growth trajectory and the weight evolution of children) can be tracked seamlessly and daily, and uploaded in a digital health record, such as for creating memories, or even helping the doctors in case of emergency or for their expertise.

Computer & Hardware Implementation of Disclosure

[00171] It should initially be understood that the disclosure herein may be implemented with any type of hardware and/or software, and may be a pre-programmed general purpose computing device. For example, the system may be implemented using a server, a personal computer, a portable computer, a thin client, or any suitable device or devices. The disclosure and/or components thereof may be a single device at a single location, or multiple devices at a single, or multiple, locations that are connected together using any appropriate communication protocols over any communication medium such as electric cable, fiber optic cable, or in a wireless manner. [00172] It should also be noted that the disclosure is illustrated and discussed herein as having a plurality of modules which perform particular functions. It should be understood that these modules are merely schematically illustrated based on their function for clarity purposes only, and do not necessary represent specific hardware or software. In this regard, these modules may be hardware and/or software implemented to substantially perform the particular functions discussed. Moreover, the modules may be combined together within the disclosure, or divided into additional modules based on the particular function desired. Thus, the disclosure should not be construed to limit the present disclosure, but merely be understood to illustrate one example implementation thereof.

[00173] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. In some implementations, a server transmits data (e.g., an HTML page) to a client device (e.g., for purposes of displaying data to and receiving user input from a user interacting with the client device). Data generated at the client device (e.g., a result of the user interaction) can be received from the client device at the server.

[00174] Implementations of the subject matter described in this specification can be implemented in a computing system that includes a back end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front end component, e.g., a client computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the subject matter described in this specification, or any combination of one or more such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include a local area network (“LAN”) and a wide area network (“WAN”), an internetwork (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer to-peer networks).

[00175] Implementations of the subject matter and the operations described in this specification can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification can be implemented as one or more computer programs, i.e., one or more modules of computer program instructions, encoded on computer storage medium for execution by, or to control the operation of, data processing apparatus. Alternatively or in addition, the program instructions can be encoded on an artificially generated propagated signal, e.g., a machinegenerated electrical, optical, or electromagnetic signal that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. A computer storage medium can be, or be included in, a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them. Moreover, while a computer storage medium is not a propagated signal, a computer storage medium can be a source or destination of computer program instructions encoded in an artificially generated propagated signal. The computer storage medium can also be, or be included in, one or more separate physical components or media (e.g., multiple CDs, disks, or other storage devices).

[00176] The operations described in this specification can be implemented as operations performed by a “data processing apparatus” on data stored on one or more computer-readable storage devices or received from other sources.

[00177] The term “data processing apparatus” encompasses all kinds of apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, a system on a chip, or multiple ones, or combinations, of the foregoing The apparatus can include special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). The apparatus can also include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, a cross-platform runtime environment, a virtual machine, or a combination of one or more of them. The apparatus and execution environment can realize various different computing model infrastructures, such as web services, distributed computing and grid computing infrastructures.

[00178] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, declarative or procedural languages, and it can be deployed in any form, including as a standalone program or as a module, component, subroutine, object, or other unit suitable for use in a computing environment. A computer program may, but need not, correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated fdes (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by a communication network.

[00179] The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform actions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[00180] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing actions in accordance with instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Moreover, a computer can be embedded in another device, e.g., a mobile telephone, a personal digital assistant (PDA), a mobile audio or video player, a game console, a Global Positioning System (GPS) receiver, or a portable storage device (e.g., a universal serial bus (USB) flash drive), to name just a few. Devices suitable for storing computer program instructions and data include all forms of non-volatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices; magnetic disks, e.g., internal hard disks or removable disks; magneto optical disks; and CD ROM and DVD-ROM disks. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

Conclusion

[00181] The various operations of exemplary methods described herein may be performed, at least partially, by an algorithm. The algorithm may be comprised in program codes or instructions stored in a memory (e.g., a non-transitory computer-readable storage medium described above). Such algorithm may comprise a machine learning algorithm. In some embodiments, a machine learning algorithm may not explicitly program computers to perform a function, but can learn from training data to make a predictions model that performs the function. [00182] The various operations of exemplary methods described herein may be performed, at least partially, by one or more processors that are temporarily configured (e.g., by software) or permanently configured to perform the relevant operations. Whether temporarily or permanently configured, such processors may constitute processor-implemented engines that operate to perform one or more operations or functions described herein.

[00183] Similarly, the methods described herein may be at least partially processor- implemented, with a particular processor or processors being an example of hardware. For example, at least some of the operations of a method may be performed by one or more processors or processor-implemented engines. Moreover, the one or more processors may also operate to support performance of the relevant operations in a “cloud computing” environment or as a “software as a service” (SaaS). For example, at least some of the operations may be performed by a group of computers (as examples of machines including processors), with these operations being accessible via a network (e.g., the Internet) and via one or more appropriate interfaces (e.g., an Application Program Interface (API)).

[00184] The performance of certain of the operations may be distributed among the processors, not only residing within a single machine, but deployed across a number of machines. In some exemplary embodiments, the processors or processor-implemented engines may be located in a single geographic location (e.g., within a home environment, an office environment, or a server farm). In other exemplary embodiments, the processors or processor-implemented engines may be distributed across a number of geographic locations.

[00185] Although an overview of the subject matter has been described with reference to specific exemplary embodiments, various modifications and changes may be made to these embodiments without departing from the broader scope of embodiments of the present disclosure. Such embodiments of the subject matter may be referred to herein, individually or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single disclosure or concept if more than one is, in fact, disclosed.

[00186] The embodiments illustrated herein are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed. Other embodiments may be used and derived therefrom, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. The Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

[00187] Any process descriptions, elements, or blocks in the flow diagrams described herein and/or depicted in the attached figures should be understood as potentially representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. Alternate implementations are included within the scope of the embodiments described herein in which elements or functions may be deleted, executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those skilled in the art.

[00188] As used herein, the term “or” may be construed in either an inclusive or exclusive sense. Moreover, plural instances may be provided for resources, operations, or structures described herein as a single instance. Additionally, boundaries between various resources, operations, engines, and data stores are somewhat arbitrary, and particular operations are illustrated in a context of specific illustrative configurations. Other allocations of functionality are envisioned and may fall within a scope of various embodiments of the present disclosure. In general, structures and functionality presented as separate resources in the exemplary configurations may be implemented as a combined structure or resource. Similarly, structures and functionality presented as a single resource may be implemented as separate resources. These and other variations, modifications, additions, and improvements fall within a scope of embodiments of the present disclosure as represented by the appended claims. The specification and drawings are, accordingly, to be regarded in an illustrative rather than a restrictive sense.

[00189] Conditional language, such as, among others, “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain embodiments include, while other embodiments do not include, certain features, elements, and/or steps. Thus, such conditional language is not generally intended to imply that features, elements, and/or steps are in any way required for one or more embodiments or that one or more embodiments necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and/or steps are included or are to be performed in any particular embodiment. [00190] While the present disclosure has been described with reference to one or more particular embodiments and implementations, those skilled in the art will recognize that many changes may be made thereto without departing from the spirit and scope of the present disclosure. Each of these embodiments and implementations and obvious variations thereof is contemplated as falling within the spirit and scope of the present disclosure, which is set forth in the claims that follow.

Claims

CLAIMS WHAT IS CLAIMED IS:

1. A pressure sensing system, comprising: a PCB layer; a plurality of pressure sensing units disposed on a first side of the PCB layer, each pressure sensing unit of the plurality of pressure sensing units including a first electrically conductive trace and a second electrically conductive trace; a plurality of column traces disposed on the first side of the PCB layer, each column trace of the plurality of column traces connecting two corresponding first electrically conductive traces of two generally vertically adjacent pressure sensing units; and a plurality of row traces disposed on a second side of the PCB layer that is opposite to the first side of the PCB layer, each row trace of the plurality of row traces connecting two corresponding second electrically conductive traces of two generally horizontally adjacent pressure sensing units.

2. The pressure sensing system of claim 1, wherein the plurality of pressure sensing units is arranged in an array of generally vertical columns and generally horizontal rows.

3. The pressure sensing system of claim 1 or claim 2, wherein the first electrically conductive trace of each pressure sensing unit is connected to at least one generally vertically adjacent pressure sensing unit via a corresponding column trace of the plurality of column traces.

4. The pressure sensing system of claim 3, wherein the first electrically conductive trace of each pressure sensing unit is connected to two generally vertically adjacent pressure sensing units via two corresponding column traces.

5. The pressure sensing system of any one of claims 1 to 4, wherein the second electrically conductive trace of each pressure sensing unit is connected to at least one generally horizontally adjacent pressure sensing unit via a corresponding row trace of the plurality of column traces.

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6. The pressure sensing system of claim 5, further comprising a plurality of vias disposed on the first side of the PCB layer, each via of the plurality of vias connecting a corresponding second electrically conductive trace of each pressure sensing unit to a corresponding row trace through the PCB layer.

7. The pressure sensing system of claim 6, wherein each via of the plurality of vias is enclosed by the corresponding second electrically conductive trace of each pressure sensing unit.

8. The pressure sensing system of any one of claims 1 to 7, wherein the first electrically conductive trace of each pressure sensing unit is shaped as a polygon.

9. The pressure sensing system of any one of claims 1 to 8, wherein the second electrically conductive trace of each pressure sensing unit is shaped as a polygon.

10. The pressure sensing system of any one of claims 1 to 7, wherein the first electrically conductive trace of each pressure sensing unit is shaped as a hexagon.

11. The pressure sensing system of any one of claims 1 to 7, wherein the first electrically conductive trace of each pressure sensing unit is shaped as a circle.

12. The pressure sensing system of any one of claims 1 to 11, wherein the second electrically conductive trace of each pressure sensing unit has a diameter smaller than that of the first electrically conductive trace.

13. The pressure sensing system of any one of claims 1 to 12, wherein the second electrically conductive trace of each pressure sensing unit is enclosed by the first electrically conductive trace.

14. The pressure sensing system of any one of claims 1 to 13, further including a pressure sensing layer positioned above the plurality of pressure sensing units.

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15. The pressure sensing system of claim 14, wherein the pressure sensing layer is configured to change its electrical resistance in response to pressure being applied thereto.

16. The pressure sensing system of any one of claims 1 to 15, wherein the plurality of pressure sensing units is disposed on a first layer on the first side of the PCB layer.

17. The pressure sensing system of claim 16, wherein the first layer is coupled directly to the PCB layer.

18. The pressure sensing system of claim 16 or claim 17, wherein the plurality of column traces is disposed on the first layer on the first side of the PCB layer.

19. The pressure sensing system of any one of claims 16 to 18, wherein the plurality of row traces is disposed on a second layer on the second side of the PCB layer.

20. The pressure sensing system of any one of claims 1 to 19, wherein the plurality of pressure sensing units and the plurality of column traces are coplanar.

21. The pressure sensing system of any one of claims 1 to 20, wherein the plurality of column traces and the plurality of row traces are electrically conductive.

22. The pressure sensing system of any one of claims 1 to 21, wherein the first electrically conductive trace of each pressure sensing unit is not connected to any of the second electrically conductive traces.

23. The pressure sensing system of any one of claims 1 to 22, wherein a thickness of the first electrically conductive trace or the second electrically conductive trace of each pressure sensing unit is between 0.1 mm and 0.5 mm.

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24. The pressure sensing system of claim 23, wherein the thickness of the first electrically conductive trace or the second electrically conductive trace of each pressure sensing unit is about 0.2 mm.

25. The pressure sensing system of any one of claims 1 to 24, wherein a pressure sensing feature of each pressure sensing unit is defined by (i) a line length L measured from a perimeter of a middle circle disposed at midpoint between the first electrically conductive trace and the second electrically conductive trace, (ii) a line thickness e measured from a distance between the first electrically conductive trace and the second electrically conductive trace, (iii) an external diameter d measured from a diameter of an external circle such that the pressure sensing unit is enclosed in the external circle, (iv) an internal diameter di measured from a diameter of an internal circle such that the pressure sensing unit is inscribed in the internal circle, or (v) any combination thereof.

26. The pressure sensing system of claim 25, wherein the pressure sensing feature of each pressure sensing unit is manipulated by changing an e/L ratio of the pressure sensing unit.

27. The pressure sensing system of claim 25 or claim 26, wherein the line length L is about 12 mm, and the line thickness e is about 1 mm.

28. The pressure sensing system of claim 25 or claim 26, wherein the line length L is about 2 mm, and the line thickness e is about 0.2 mm.

29. The pressure sensing system of claim 25 or claim 26, wherein the line length L is about 30 mm, and the line thickness e is about 0.5 mm.

30. The pressure sensing system of claim 25 or claim 26, wherein the line length L is about 9 mm, and the line thickness e is about 0.25 mm.

31. The pressure sensing system of claim 25 or claim 26, wherein the line length L is about 12 mm, and the line thickness e is about 1 mm.

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32. The pressure sensing system of any one of claims 1 to 31, wherein the pressure sensing system includes between one to four pressure sensing units per square centimeter.

33. A smart mat system comprising: a tech device including: a PCB layer; and an array of pressure sensing units disposed on a first side of the PCB layer, each pressure sensing unit including: a first electrically conductive trace disposed on the first side of the PCB layer, the first electrically conductive trace being connected to a generally vertically adjacent pressure sensing unit via a column trace that is disposed on the first side of the PCB layer; and a second electrically conductive trace disposed on the first side of the PCB layer, the second electrically conductive trace being connected to a generally horizontally adjacent pressure sensing unit via a row trace that is disposed on a second side of the PCB layer; and a mat cover configured to be placed directly above and covering the tech device.

34. The smart mat system of claim 33, wherein at least a portion of the mat cover is made of fabric, rubber, conductive metallic thread, or any combination thereof.

35. The smart mat system of claim 33 or claim 34, further comprising a bioelectrical impedance system configured to generate bioelectrical impedance data associated with a user of the smart mat system, the bioelectrical impedance system including a plurality of electrodes configured to conductively contact a user and form a first closed circuit with the user.

36. The smart mat system of any one of claims 33 to 35, wherein at least a portion of the mat cover includes two electrically conductive fabric portions spaced from each other.

37. The smart mat system of claim 36, wherein the two electrically conductive fabric portions are spaced from each other at least 3 inches.

38. The smart mat system of any one of claims 33 to 37, wherein the mat cover includes one or more conductive thread electrodes.

39. The smart mat system of claim 38, wherein the one or more conductive thread electrodes includes exposed conductive electrodes providing direct physical contact with the user of the smart mat system.

40. The smart mat system of any one of claims 35 to 39, wherein the plurality of electrodes includes a first pair of electrodes that forms the first closed circuit with the user.

41. The smart mat system of claim 40, wherein the first pair of electrodes is configured to contact a first foot of the user.

42. The smart mat system of claim 40 or claim 41 , wherein the first pair of electrodes is coupled to a bioelectrical impedance module of the bioelectrical impedance system, and wherein the first pair of electrodes is configured to measure a current of the first closed circuit and generate current data.

43. The smart mat system of any one of claims 40 to 42, wherein the plurality of electrodes further includes a second pair of electrodes configured to conductively contact the user and form a second closed circuit with the user.

44. The smart mat system of claim 43, wherein the second pair of electrodes is configured to contact a second foot of the user.

45. The smart mat system of claim 43 or claim 44, wherein the second pair of electrodes is configured to measure a voltage of the second closed circuit and generate voltage data.

46. The smart mat system of any one of claims 33 to 45, wherein the mat cover is washable.

47. The smart mat system of any one of claims 33 to 46, wherein the array of pressure sensing unit is configured to generate pressure data associated with a user of the smart mat system.

48. The smart mat system of claim 47, wherein the smart mat system is configured to generate and/or normalize weight data based at least in part on the generated pressure data.

49. The smart mat system of claim 47 or claim 48, wherein the array of pressure sensors is configured to generate the pressure data in response to the user engaging the smart mat system.

50. The smart mat system of claim 49, wherein the user engaging the smart mat system includes the user standing, stepping, squatting, exercising, or stretching on the mat cover.

51. The smart mat system of any one of claims 33 to 50, wherein one or more components of the smart mat system form a bath mat, a yoga mat, a doormat, an anti-fatigue mat, a chair cushion, a body pillow, a shoe insole, a portion of a carpet, one or more pieces of tile, one or more pieces of hardwood flooring, part of a mattress, or part of a shower.

52. A smart mat system, comprising: a PCB layer; an array of pressure sensing units disposed on a first side of the PCB layer, each pressure sensing unit including: a first electrically conductive trace disposed on the first side of the PCB layer, the first electrically conductive trace being connected to a generally vertically adjacent pressure sensing unit via a column trace that is disposed on the first side of the PCB layer; and a second electrically conductive trace disposed on the first side of the PCB layer, the second electrically conductive trace being connected to a generally horizontally adjacent pressure sensing unit via a row trace that is disposed on a second side of the PCB layer;

54 a memory storing machine-readable instructions; and a control system coupled to the memory and arranged to provide control signals to one or more processors configured to execute the machine-readable instructions to: receive, from the array of pressure sensors, pressure data; based at least in part on the pressure data, determine that a user is engaging the smart mat system; and in response to the determining that the user is engaging the smart mat system, determine a physiological parameter of the user.

53. The smart mat system of claim 52, wherein the physiological parameter of the user is a normalized weight of the user, and wherein the control system is further configured to: determine a weight location of the user based at least in part on the received pressure data; and estimate the normalized weight based at least in part on the weight location.

54. The smart mat system of claim 52 or claim 53, wherein the physiological parameter of the user is a posture, balance, or stability of the user, and wherein the control system is further configured to: determine a center of pressure of the user based at least in part on the received pressure data; determine a geometric center of the user based at least in part on the received pressure data; and determine the posture, the balance, or the stability of the user based at least in part on comparing the determined center of pressure and the determined geometric center.

55. The smart mat system of claim 54, wherein the posture, the balance, or the stability of the user is determined based on comparing the determined center of pressure and the determined geometric center over a period of time.

56. The smart mat system of claim 54 or claim 55, wherein the control system is further configured to determine a center of mass of the user based at least in part on the received pressure data, and wherein the posture, the balance, or the stability of the user is further determined based at least in part on the determined center of mass.

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57. A pressure sensing system, comprising: a plurality of pressure sensing units disposed on a first layer, each pressure sensing unit of the plurality of pressure sensing units including a first electrically conductive trace and a second electrically conductive trace; a plurality of column traces disposed on a first side of the first layer, each column trace of the plurality of column traces connecting two corresponding first electrically conductive traces of two generally vertically adjacent pressure sensing units; and a plurality of row traces disposed on a second side of the first layer that is opposite to the first side of the fabric layer, each row trace of the plurality of row traces connecting two corresponding second electrically conductive traces of two generally horizontally adjacent pressure sensing units; a memory storing machine-readable instructions; and a control system coupled to the memory and arranged to provide control signals to one or more processors configured to execute the machine-readable instructions to: receive, from the plurality of pressure sensing units, pressure data; based at least in part on the pressure data, determine that a user is engaging the pressure sensing system; and in response to the determining that the user is engaging the smart mat system, determine a physiological parameter of the user.

58. The pressure sensing system of claim 57, wherein the first layer is fabric.

59. The pressure sensing system of claim 57, wherein the first layer is a PCB layer.

60. The pressure sensing system of any one of claims 57 to 59, wherein the physiological parameter of the user is a weight of the user, a left foot shoe size of the user, a right foot shoe size of the user, a center of gravity for the user, a vertical foot length for the right foot of the user, a vertical foot length for the left foot of the user, an arch type associated with the user, a distance between the feet of the user, a shape of the feet of the user, or any combination thereof.

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61. The pressure sensing system of any one of claims 57 to 60, wherein the physiological parameter of the user is used in recognizing the user as one of a number of stored registered users.

62. The pressure sensing system of claim 61 , wherein stored reference vectors associated with the stored registered users are compared against the physiological parameter associated with the user when recognizing the user as one of the number of stored registered users.

63. The pressure sensing system of claim 62, wherein distances between the stored reference vectors associated with the stored registered users and the physiological parameter is compared against a similarity threshold when recognizing the user as one of the number of stored registered users.

64. The pressure sensing system of claim 63, wherein the similarity threshold is 1.

65. The pressure sensing system of any one of claims 62 to 64, wherein a subset of the stored registered users is eliminated from consideration based on a consideration threshold.

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