System and method for generating three-dimensional surface topology using wearable instrumentation

Abstract

Systems, devices, and methods for generating a surface topology using a wearable instrumentation comprising a fabric element (112, 162) configured to fit at least part of a user's body; a plurality of sensing elements (114, 164) configured to be embedded and distributed throughout the fabric element and collect physical data from the at least part of the user's body; and a computation device (120, 170) configured to generate a digital reconstruction of the at least part of the user's body based on the physical data.

Description

[0001] PATENT COOPERATION TREATY APPLICATION

[0002] TITLE: SYSTEM AND METHOD FOR GENERATING THREE-DIMENSIONAL

[0003] SURFACE TOPOLOGY USING WEARABLE INSTRUMENTATION

[0004] CROSS-REFERENCE TO RELATED APPLICATION

[0005] This application claims the priority benefit of U.S. Provisional Patent Application Serial Number 63 / 648,369 filed May 16, 2024, all of which are incorporated herein by reference in their entireties.

[0006] TECHNICAL FIELD

[0007] Embodiments relate generally to systems and methods for generating a surface topology, and more particularly to systems and methods for generating a surface topology of the human body.

[0008] BACKGROUND

[0009] A topology, or surface topology, involves properties of a geometric object or shape that can be invariant under any deformations, such as stretching, twisting, and bending, while maintaining points of the geometric object to stay close to each other. The three-dimensional models of surface topology of a certain geometric object have been used in various fields, such as biology, robotics, engineering, computer science, and others. Recently, three-dimensional models of surface topology of the human body have been studied for diverse purposes.

[0010] SUMMARY

[0011] An embodiment of a system disclosed herein comprises wearable instrumentation to generate a three-dimensional surface topology of different parts of the human body. The wearable instrumentation may include a fabric element and sensing elements that is embedded in the fabric element and configured to measure displacements. The system may be applied to various parts of the human body, which include, but are not limited to, the feet, knees, hips, elbows, hands, and shoulders. A system embodiment may include: a fabric element configured to fit at least part of a user’s body; a plurality of sensing elements configured to be embedded and distributed throughout the fabric element and collect physical data from the at least part of the user’s body; and a computation device configured to generate a digital reconstruction of the at least part of the user’s body based on the physical data.

[0012] In another embodiment, the fabric element may be configured to be in contact with skin of the at least part of the user’s body and flexible to deform with movement of the at least part of the user’s body.

[0013] In another embodiment, the digital reconstruction may include a three- dimensional surface topology corresponding to a three-dimensional surface geometry of the at least part of the user’s body.

[0014] In another embodiment, each of the sensing elements may include a line shape configured to measure a linear distance.

[0015] In another embodiment, when the fabric element is flexible to deform with movement of the at least part of the user’s body, the line shapes of the sensing elements may be configured to stretch and flex along with the fabric element.

[0016] In another embodiment, the plurality of the sensing elements may be configured to be arranged to form an instrumentation mesh including a plurality of polygons.

[0017] In another embodiment, the instrumentation mesh may include a plurality of nodes and a plurality of line segments, each of the line segments may be configured to connect between adjacent nodes of the plurality of nodes, and when the fabric element deforms with movement of the at least part of the user’s body, the lengths of some of the line segments may be configured to change.

[0018] In another embodiment, the plurality of the sensing elements may be configured to collect the physical data by measuring resistances of the line segments that are proportional to the lengths of the line segments, respectively.

[0019] In another embodiment, the computation device may be configured to generate the digital reconstruction including a surface mesh that approximates the surface of the at least part of the user’s body based on the lengths.

[0020] In another embodiment, the plurality of the sensing elements may be configured to collect the physical data by measuring the resistances of the line segments at a plurality of states including: a rest state when the fabric element is not affected by an applied force, and a deformed state when an applied force formed by movement of the at least part of the user’s body causes a deformation on the fabric element.

[0021] In another embodiment, the plurality of sensing elements may be configured to be formed by at least one of: ink printing, ink deposition, sewing, lamination, or adhesive bonding.

[0022] In another embodiment, some of the plurality of sensing elements may be configured to be formed more densely in one or more portions of the fabric element than in other portions.

[0023] In another embodiment, the system may further comprise at least one secondary sensing module, each including a temperature sensor and an inertial measurement unit (IMU).

[0024] In another embodiment, the system may further comprise electronics configured to be attached to the fabric element, and the electronics may be configured to receive the collected physical data from the plurality of the sensing elements and store and process the received physical data in real-time.

[0025] In another embodiment, the sensing elements may include at least one of strain gauges, piezoelectric sensors, capacitive sensors, and conductive filaments stitched in a mesh pattern.

[0026] A system embodiment may include: a fabric element configured to fit at least part of a user’s body and comprising a top layer and a bottom layer stacked on one another; a plurality of sensing elements including a plurality of top sensing elements embedded in the top layer and a plurality of bottom sensing elements embedded in the bottom layer, wherein the plurality of sensing elements are configured to collect physical data from the at least part of the user’s body; and a computation device configured to generate a digital reconstruction of the at least part of the user’s body based on the physical data.

[0027] In another embodiment, the plurality of the top sensing elements may be configured to be arranged to form a top instrumentation mesh, and the plurality of the bottom sensing elements may be configured to be arranged to form a bottom instrumentation mesh.

[0028] In another embodiment, the top instrumentation mesh and the bottom instrumentation mesh may be configured to be arranged to correspond with one another. In another embodiment, the plurality of the top sensing elements and the bottom sensing elements may be configured to collect the physical data including curvature of the fabric element by measuring the top lengths of line segments of the top instrumentation mesh and the bottom lengths of line segments of the top instrumentation mesh and calculating difference between the top lengths and the bottom lengths.

[0029] A method embodiment may include: collecting, by a wearable instrumentation, physical data from at least part of the user’ s body, wherein the wearable instrumentation includes a fabric element configured to fit the at least part of a user’s body and a plurality of sensing elements configured to be distributed throughout the fabric element; modeling, by a computation device in communication with the wearable instrumentation, algorithm configured to represent the sensing elements as an instrumentation mesh based on the collected physical data; determining, by the computation device, a digital topological model of the at least part of the user’s body; and determining, by the computation device, a physiological model for the at least part of the user’s body by extracting physiological data from the digital topological model.

[0030] BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The components in the figures are not necessarily to scale, emphasis instead being placed upon illustrating the principals of the invention. Like reference numerals designate corresponding parts throughout the different views. Embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:

[0032] FIG. 1 depicts a diagram of a system for generating a surface topology using a wearable instrumentation, according to an embodiment of the disclosure;

[0033] FIG. 2A depicts a wearable instrumentation of a system for generating a surface topology, according to an embodiment of the disclosure;

[0034] FIG. 2B depicts alternative shapes of sensing elements and other line-shaped elements of a wearable instrumentation, according to an embodiment of the disclosure;

[0035] FIG. 2C depicts a wearable instrumentation worn on the right knee to explain a method for optimizing the spatial placement of nodes and node devices for the present system, according to an embodiment of the disclosure; FIGS. 2D to 2F depict various examples of wearable instrumentations in systems for generating a surface topology, according to other embodiments of the disclosure;

[0036] FIG. 3A depicts a system for generating a surface topology using a wearable instrumentation applied to the knee, according to an embodiment of the disclosure;

[0037] FIG. 3B depicts another system for generating a surface topology using a wearable instrumentation applied to the knee, according to another embodiment of the disclosure;

[0038] FIG. 3C depicts yet another system for generating a surface topology using a wearable instrumentation applied to the knee, according to another embodiment of the disclosure;

[0039] FIG. 4 depicts an instrumentation mesh formed by a plurality of sensing elements on a wearable instrumentation, according to an embodiment of the disclosure;

[0040] FIG. 5A depicts two dimensional representation of unequal cells of an instrumentation mesh when a wearable instrumentation is positioned on a flat surface, according to an embodiment of the disclosure;

[0041] FIG. 5B depicts three dimensional representation of unequal cells an instrumentation mesh when a wearable instrumentation is worn on the knee, according to an embodiment of the disclosure;

[0042] FIG. 6A depicts a single cell of an instrumentation mesh at a rest state when the cell is not affected by an applied force, according to an embodiment of the disclosure;

[0043] FIG. 6B depicts a single cell of an instrumentation mesh at a deformed state when an applied force causes a measurable deformation to the cell, according to an embodiment of the disclosure;

[0044] FIG. 7 depict drawings for explaining a directional stretching in fabric element of a wearable instrumentation, according to an embodiment of the disclosure;

[0045] FIG. 8 depict a sensing element on a stretchable fabric element, according to an embodiment of the disclosure;

[0046] FIG. 9A depicts a multi-layer stack-up structure of a fabric element, according to an embodiment of the disclosure;

[0047] FIG. 9B depicts a multi-layer stack-up structure of a fabric element, according to another embodiment of the disclosure; FIG. 10 depicts a cross-sectional view of a multi-layer stack-up structure of a fabric element, according to an embodiment of the disclosure;

[0048] FIG. 11 depicts a sensing element on a fabric element at a rest state when the fabric element is not stretched and at a stretched state when the fabric element is stretched, according to an embodiment of the disclosure;

[0049] FIG. 12 depicts a diagram of electronics of a wearable instrumentation, according to an embodiment of the disclosure;

[0050] FIG. 13 A depicts a flowchart of a method for generating a surface topology using a wearable instrumentation and signal analysis, according to an embodiment of the disclosure;

[0051] FIG. 13B depicts a flowchart of a method for generating a surface topology using a wearable instrumentation and signal analysis, according to another embodiment of the disclosure;

[0052] FIGS. 14A and 14B depict a flowchart of a method for constructing a digital topological model and biological / physiological metrics using machine learning, according to an embodiment of the disclosure;

[0053] FIGS. 15A and 15B depict a flowchart of another method for constructing a digital topological model and biological / physiological metrics using machine learning, according to another embodiment of the disclosure;

[0054] FIG. 16 shows a high-level block diagram and process of a computing system for implementing an embodiment of the system and process;

[0055] FIG. 17 shows a block diagram and process of an exemplary system in which an embodiment may be implemented;

[0056] FIG. 18 depicts a cloud computing environment for implementing an embodiment of the system and process disclosed herein; and

[0057] FIG. 19 illustrates an example top-level functional block diagram of a computing device embodiment.

[0058] DETAILED DESCRIPTION

[0059] The present disclosure provides a system of a wearable instrumentation for generating a three-dimensional surface topology of different parts of the human body. The wearable instrumentation may include a fabric element and sensing elements that are configured to be embedded in the fabric element and measure displacements.

[0060] FIG. 1 depicts a diagram of a system for generating a surface topology using a wearable instrumentation, according to an embodiment of the disclosure. With reference to FIG. 1, a system 100 for generating a surface topology may comprise a wearable instrumentation 110 including a fabric element 112, a plurality of sensing elements 114, electronics 116, and electrical connection 117, and a computing device 120 in communication with the wearable instrumentation 110. The fabric element 112 may include at least one of layer that is configured to fit the part of body of a user. The plurality of sensing elements 114 may be embedded in the fabric element 112 and distributed throughout the fabric element 112. The plurality of sensing elements 114 distributed throughout the fabric element 112 may detect, sense, and / or collect physical data from the body and displacements. In this case, the physical data may include node data for nodes connecting line segments of the sensing elements 114, length data for lengths of each line segment of sensing elements 114, and curvature data for curvatures of each line segment, which may be collected from the embedded sensing elements 114. In some embodiments, the sensing elements 114 may be formed by ink printed on the fabric element 112 but are not limited thereto.

[0061] The electronics 116 may also be embedded in the fabric element 112 and configured to store and process the received physical data in real-time. In some embodiments, the electronics 116 may not be embedded on the fabric element 112 directly but may be affixed to the fabric element 112. In some embodiments, first electronics 116A of the electronics 116 may include an acquisition module configured to acquire the physical data from sensing elements 114 and a secondary sensing module (e.g., accelerator) configured to sense additional data from the body. The first electronics 116A may include a plurality of devices distributed throughout the body and may be located at some of the nodes connecting line segments of the sensing elements 114. Second electronics 116B of the electronics 116 may include modules configured to perform processing, storage, transmission of the physical data received from the sensing elements 114 and / or the first electronics 116A, a battery module configured to supply power to the elements of the wearable instrumentation 110, and a secondary sensing module configured to sense additional data from the body (e.g., temperature sensor, Inertial Measurement Unit (IMU) with accelerator, gyroscope, magnetometer, etc.). The secondary sensing modules may be used to calibrate the analysis of the distances between nodes. The electrical connection 117 may be configured to connect between the sensing elements 114 and the electronics 116. In some embodiments, the electrical connection 117 may be formed by ink printed on the fabric element 112 but are not limited thereto.

[0062] The computation device 120 may be positioned outside of the wearable instrumentation 110 and configured to operate a digital reconstruction module 122 and generate a three-dimensional digital reconstruction of the part of body.

[0063] FIG. 2A depicts a wearable instrumentation of a system for generating a surface topology, according to an embodiment of the disclosure. The left side of FIG. 2A illustrates the wearable instrumentation in a non-worn state, and the right side of FIG. 2A illustrates a zoomed-in view of the top-left quadrant of the wearable instrumentation shown in the left side. With reference to FIG. 2A, the wearable instrumentation 130 may include a fabric element 132, a plurality of line-shaped sensing elements 134, electronics 136A, 136B, and electrical connection 137 connecting between the plurality of line-shaped sensing elements 134 and the electronics 136A, 136B and between the electronics 136A, 136B. In some embodiments, the line-shaped sensing element 134 may be a flattened version of line-shaped sensing element but is not limited thereto. The first electronics 136A may be configured to acquire the physical data from sensing elements 134 and perform secondary sensing. The second electronics 136B may be configured to perform processing, storage, transmission of the physical data, supply power, and perform secondary sensing. A plurality of nodes 135 may be formed between a plurality of line-shaped sensing elements 134 and between the line-shaped sensing elements 134 and the electrical connection 137. The first electronics 136A may be located at one or more of the nodes 135. The wearable instrumentation 130 may be viewed as a circuit diagram printed with special inks onto the fabric element 132. In FIG. 2A, the wearable instrumentation 130 may include black traces 134A and silver traces 134B printed onto the fabric element 132. Each of the line segments of the black traces 134A may be a line-shaped sensing element 134, and its changing length may be measured continuously. The black traces 134A may act as resistors and be an application of carbon black ink printed or deposited onto the fabric element 132 and then subsequently being heat cured. The silver traces 134B may act as wires and be an application of silver flake ink printed onto the fabric element 132, with an off-the-shelf conductive thread sewn along it. The functionality of the shape of the black traces 134A is to measure a linear distance and its changing length regardless of other factors, and thus the black traces 134A as the sensing element 134 are continuous and / or contiguous to function.

[0064] In some embodiments, the sensing elements 134 may be either sewn, laminated to, or otherwise adhesively bonded to the underlying fabric element 132. In some embodiments, the sensing elements 134 may be formed using multiple methods, such as special inks, sewing, lamination, or adhesive bonding. Each method may not replace the others, but rather be used in conjunction with them.

[0065] FIG. 2B depicts alternative shapes of sensing elements and other line-shaped elements of a wearable instrumentation, according to an embodiment of the disclosure. With reference to FIGS. 2A and 2B, at least one of the black traces 134A, silver traces 134B, and / or thread stitches may be of different shapes, such as zigzag line 138 or sine wave 139, instead of a straight line shown in FIG. 2 A.

[0066] In addition to the wearable instrumentation 130 shown in FIG. 2 A, the present system may include various placements of line-shaped sensing elements, first electronics (node device), second electronics, and electrical connections in a fabric element. FIG. 2C depicts a wearable instrumentation worn on the right knee to explain a method for optimizing the spatial placement of nodes and node devices for the present system, according to an embodiment of the disclosure. FIGS. 2D to 2F depict various examples of wearable instrumentations of systems for generating a surface topology, according to other embodiments of the disclosure. FIGS. 2D to 2F show examples of the ways nodes and nodes devices may be implemented, but the present disclosure is not limited thereto. For example, although FIG. 2D illustrates a node device 1502 operating as an 8-pole electrical switch, this is an illustrative example, and the number of pole and / or the configuration of the node device in the present system may be selected freely, depending on the application. That is, the node device 1502 may operate as two 4-pole switches, a 16-pole switch, or any other number or configuration of pole, as needed.

[0067] The method for optimizing the spatial placement of nodes and node devices may include steps for performing requirements and optimization. The first step may be configured to determine the body part to be instrumented, considering its surface geometry (e.g. considering the knee, which is a hinge joint and that has larger circumference at the top near the thigh, compared to a smaller circumference at the bottom near the calf, and a minimum circumference around the patella). The second step may be configured to overlay a mesh or grid pattern onto this surface geometry, with the nodes at the intersections of the mesh. The third step may be configured to consider and determine that line-shaped sensing elements will connect adjacent nodes along the same axis [e.g. nodes at coordinates (x,y) and (x+l,y)] as well as a plurality of adjacent nodes across the diagonal [e.g. nodes at (x,y) and (x+l,y+l)], when positioning the mesh and nodes.

[0068] Then, the fourth step may be a first optimization step. The fourth step may be configured to align line-shaped sensing elements such that changes in their measured distances will be able to describe major physiological characteristics of the body part in question. For example, with reference to FIG. 2C, knowing the need to record patellar circumference, four nodes 141, 142, 143, 144 may be determined or placed in a wearable instrumentation 140 such that 1) one 141 is on the front of the knee, at the center of the patella, 2) one 142 is on the back of the knee centered on the joint, 3) one 143 is in the same plane as those formed by the above 1) and 2) where the medial collateral ligament (MCL) meets the femur, and 4) the other 144 is in the same plane as those formed by the above 1) and 2) where the lateral collateral ligament (LCL) meets the femur. In this way, the sum of these four distances may provide a proxy for patellar circumference, and would simplify calculations in future steps.

[0069] Then, the fifth step may be configured to place node devices such that they can be contiguous conductive / re si stive connection to nearby nodes without intersection. In this case, as node devices may be rigid, node devices may be placed such that they do not interfere with comfortable movement for the user. The sixth step may be configured to measure the distance over a line-shaped sensing element. There must be a known voltage running over a dynamically changing resistive element. The sixth step may be configured to account for running power such that each line-shaped sensing element has an available known voltage at its terminus opposite the node device.

[0070] Then, the seventh step may be a second optimization step. The seventh step may be configured to determine a balance point or optimal trade-off along the spectrum related to the number of nodes and the user's movement. Specifically, there is a tradeoff for node devices: the greater the number of nodes a single node device can connect to, the more restricted the user's movement will be. This is due to the fact that as the number of conductive traces leading out of the node device increases, the width of each conductive trace decreases, which in turn leads to an increased chance of normal user movement creating a short-circuit across parallel conductive traces. When the desired nodes have been positioned, the eighth step for a third optimization may be configured to place node devices such that the fewest connections can be used, and that each lineshaped sensing element can be electrically isolated.

[0071] In summary, the final geometry of nodes and node devices may be determined by the three-dimensional complexity of the joint in motion. Using this approach, the present systems may be implemented with various wearable instrumentations.

[0072] With reference to FIG. 2D, a wearable instrumentation 1500 may show the placement of a node device, a first electronics, 1502 centrally placed among five nodes Ndl, Nd2, Nd3, Nd4, Nd5 along with the conductive trace patterns 1512 (shown in silver) and resistive trace patterns 1514 (shown in black) that are required to comprise a portion of the broader mesh pattern. The node device 1501, which is configured to acquire sensed physical data, may be connected to the second electronics 1520, which is configured to process, store, or transmit the data. The five nodes Ndl to Nd5 may be formed around the perimeter of the mesh pattern, and the node device 1502 may sit at the center of the mesh pattern and measure the distances Dsl, Ds2, Ds3, Ds4, Ds5, Ds6, Ds7, Ds8 of the resistive trace patterns 1514 which are between itself and the five nodes Ndl to Nd5 and between some of nodes Ndl to Nd5 as follows: distance(Dsl) = node device 1502 to Ndl distance(Ds2) = Ndl to Nd2 distance(Ds3) = node device 1502 to Nd2 distance(Ds4) = Nd2 to Nd4 distance(Ds5) = node device 1502 to Nd3 distance(Ds6) = node device 1502 to Nd4 distance(Ds7) = Nd4 to Nd5 distance(Ds8) = node device 1502 to Nd5

[0073] Specifically, in the above configuration, the node device 1502 may operate as an 8-pole electrical switch. Starting with the terminal SI marked by the white circle and working clockwise, terminals SI through S8 may be determined. In some embodiments, although this switch may represent a total of 2A8 = 256 possible states, the system may use part of the states (e.g., 9 of the states), corresponding to the states where at most one terminal is activated. This may be represented internally as an 8-bit bitmask where at most one bit is set to 1. For example, 00000000 indicates no terminals are activated, 00000001 indicates terminal SI is activated, 00000010 indicates terminal S2 is activated, and so on to 10000000 indicating that terminal S8 is activated. Depending on which terminal is activated, a reading may be taken to determine the marked distances as follows:

[0074] 51 on = distance(Dsl+Ds2)

[0075] 52 on = distance(Ds2)

[0076] 53 on = distance(Ds3+Ds4) 54 on = distance(Ds4)

[0077] 55 on = distance(Ds5)

[0078] 56 on = distance(Ds6)

[0079] 57 on = distance(Ds7)

[0080] 58 on = distance(Ds7+Ds8)

[0081] An onboard clock may allow cycling through terminal activation in rapid succession, and thus continuous readings may be obtained for these distances. In the few cases where there is no direct measurement for a distance, for example distance(Dsl), simple subtraction may be used to determine it. That is, distance(Dsl) = distance(Dsl+Ds2) - distance(Ds2).

[0082] Further revisions of the node device itself may allow for functional expansion to support a greater number of nodes and measure a greater number of distances between them. With reference to FIG. 2E, an expansion of the node device design may be shown. The node device 1602 may be placed among eight nodes Nd6, Nd7, Nd8, Nd9, NdlO, Ndl 1, Ndl2, Ndl3 and configured to measure 16 distances.

[0083] FIG. 2F shows how this configuration can be scaled up to include multiple node devices. With reference to FIG. 2F, the wearable instrumentation 1700 may include four node devices 1702, 1704, 1706, 1708. An important note is that since there is a shared onboard clock, terminal activations may be cycled in parallel for each node device. That is, if the wearable instrumentation 1700 have four node devices 1702, 1704, 1706, 1708, measurements from terminal SI for all four node devices 1702, 1704, 1706, 1708 in parallel may be taken and sent to the second electronics, or puck, 1720 for onboard storage, and then measurements from terminal S2 for all four node devices 1702, 1704, 1706, 1708 in parallel may be taken and sent to the second electronics, or puck, 1720 for onboard storage, and so on.

[0084] FIG. 3A depicts a system for generating a surface topology using a wearable instrumentation applied to the knee, according to an embodiment of the disclosure. FIG. 3 A is to show the overview of an application of a system 150 for generating a surface topology of the knee as a representative example, but the wearable instrumentation 160 of the present disclosure may be applied to any other parts of the human body other than the knee. With reference to FIG. 3 A, the system 150 may comprise: the wearable instrumentation 160, including a fabric element 162, a plurality of individual sensing elements 164, electronics 166, and electrical connections, and a computation device 170 in communication with the wearable instrumentation 160. The wearable instrumentation 160 may be worn on the knee of a user to generate a topological model of the knee joint. In FIG. 3 A, electrical connections configured to connect between the sensing elements 164 and the electronics 166 may be not shown.

[0085] The fabric element 162 may be configured to fit the part of body of a user, such as the knee, and be in contact with skin or tight-fitting clothing worn on the part of body. Specifically, the fabric element 162 may be in the appropriate form factor to measure a specific part of the body by being tight-fitting and flexible enough to deform with corresponding movement in the body part. The form factors of the fabric element 162 may be a shape, configuration, size, physical properties, such as elasticity, stretchiness, flexibility, and others. To measure the surface geometry of the body part, such as knee, a flexible knee sleeve form factor may be appropriate. The fabric element 162 may be embedded with the plurality of individual sensing elements and the electronics inside. In this example, the embedded sensing elements 164 on the knee sleeve may capture physical data to allow for reconstruction of a three-dimensional surface topology 123 of the knee as shown in FIG. 3 A. In some embodiments, the fabric element 162 may be wearable instrumentation. The fit of the fabric element 162 may be detachably attached to a user, connected to a user, placed on a user, secured to a user, or the like.

[0086] The plurality of individual sensing elements 164 may be embedded in the fabric element 162 and configured to collect physical data from the body. The plurality of sensing elements 164 may be distributed throughout the entire fabric element 162. In some embodiments, the plurality of sensing elements 164 may be irregularly distributed on the fabric element 162. In other words, some sensing elements 164 may be formed more densely in some portions of the fabric element 162 than other portions. For example, the portions of the fabric element 162 where the sensing elements 164 are formed more densely may be portions corresponding to the knee joint, while the portions of the fabric element 162 where the sensing elements 164 are formed less densely may be portions corresponding to the thigh. The high density of the individual sensing elements in the fabric element 162 may enable the capture of more physical data of the body. Accordingly, the higher the density of the individual sensing elements 162, the higher the surface resolution of three-dimensional surface topology 123 that may be modeled. In some embodiments, each of the individual sensing elements 164 may have a shape configured to measure a linear distance and its changing length, such as a straight line shape, a zig-zag line shape, and / or a sine wave, and the plurality of the sensing elements 164 may be arranged to form a surface mesh including a plurality of polygons that approximates the surface of the part of the body but is not limited thereto.

[0087] The electronics 166 may also be embedded in the fabric element 162 and configured to collect or receive the collected physical data from the plurality of the sensing elements 164. Then, the electronics 166 may be configured to store and process the received physical data in real-time. In some embodiments, the electronics 166 may be a computing device including a processor and a memory.

[0088] The computation device 170 may be configured to operate a digital reconstruction module 172 based on software algorithms that allow for a digital reconstruction of the part of body based on the physical data. The digital reconstruction module 172 may be configured to create a three-dimensional surface topology 123, or topological model, of the part of body. In the embodiment shown in FIG. 3 A, a three- dimensional surface topology 123 of the knee may be generated. The topological model may discretize the surface of the part of body into a plurality of nodes and a plurality of line segments connecting the nodes to allow for the generation of a surface mesh that approximates the surface of the part of body. In this case, the higher the density of the individual sensing elements 164 on the fabric element 162, the higher the surface resolution of the three-dimensional surface topology 123 that can be modeled. FIG. 3B depicts a system for generating a surface topology using a wearable instrumentation applied to the knee, according to another embodiment of the disclosure. With reference to FIGS. 3 A and 3B, the system 151 may be similar to the system 150 shown in FIG. 3 A. However, the wearable instrumentation 161 of the system 151 may further comprise distributed secondary sensing modules 180 and a centralized secondary sensing module 190. Specifically, the system 151 may comprise: the wearable instrumentation 161, including a fabric element 162, a plurality of individual sensing elements 164, first electronics 166 A, second electronics 166B, and electrical connections, and a computation device 170 in communication with the wearable instrumentation 160.

[0089] The plurality of first electronics, or node devices, 166 A may be electrical switches and be affixed roughly uniformly throughout the wearable instrumentation (e.g., four to eight in total). Each of the first electronics 116A may include an acquisition module configured to acquire the physical data from sensing elements 164 and a secondary sensing module 180 (e.g., accelerator) configured to sense additional data from the body. The acquisition modules and the secondary sensing elements 180 may be handled by the first electronics 166 A. The first electronics 166 A are shown as small square circuit boards in FIG. 3B.

[0090] With reference to FIGS. 2 A and 3B together, each of the first electronics, or node devices, 166 A (136A in FIG. 2 A) may be located at a selected node that connects the adjacent line-shaped sensing elements 164. Functionally, each of the first electronics, or node devices, 166A (136A in FIG. 2A) may measure the distance from itself to nearby nodes that it branches to or connects with. In other words, there is a continuous line-shaped sensing element 164 (e.g., black or silver ink trace) between the first electronics, or node device, 166A (136A in FIG. 2A) and the node in question. The first electronics, or node devices, 166A (136A in FIG. 2A) may also measure the distance between two other distinct nodes when it branches to or connects with both.

[0091] Spatially, the first electronics, or node device, 166 A (136A in FIG. 2 A) may be located at any selected node between the adjacent line-shaped sensing elements 164. In some embodiments, if the first electronics, or node device, 166A (136A in FIG. 2A) is placed at the center of a square formed by electrical connection (137, FIG. 2 A), the nodes 135 which the first electronics, or node device, 166 A (136A in FIG. 2 A) connects to may be either arranged in a U-shape along either the top or bottom perimeter of the square, as shown in the right side of FIG. 2A, or along the full perimeter of the square of which the first electronics, or node device, 166A (136A in FIG. 2A) is at the center. One benefit of this arrangement may be that when two adjacently placed first electronics, or node device, 166A (136A in FIG. 2A) both measure the length of a shared side, the resulting redundant data may be used in calibration, increased precision, and troubleshooting. For example, each of two line-shaped sensing elements 164 (134 in FIG. 2A) that are parallel and near one another may be measured by a different first electronics, or node device, 166A (136A in FIG. 2A). In some embodiments, a single first electronics, or node device, 166 A (136A in FIG. 2 A) may be configured to measure distances to eight nearby nodes, but is not limited thereto, a single first electronics, or node device, 166 A (136A in FIG. 2 A) may be configured to measure distances to 16 or more nearby nodes.

[0092] The first electronics, or node device, 166A may also be placed in locations that maximize user comfort. For example, the first electronics, or node device, 166 A may be placed along the long muscle on the upper side of the thigh, where it does not impede movement or rest directly on bones or fine tendons that could cause discomfort or injury.

[0093] The second electronics, or a puck, 166B, may include processing, storage, transmission, and battery modules and a centralized secondary sensing module 190, and these modules may be handled by the second electronics 166B. The secondary sensing module 190 may include various sensors, such as a temperature sensor and an IMU, to sense additional data from the body. The IMU may function as an accelerometer, gyroscope, and magnetometer. The system 151 may include a few second electronics 166B (e.g., one to two in total). The processing, storage, transmission, and battery modules may be shown as a large rectangular circuit board positioned at the top of the wearable instrumentation 161 on the outer thigh portion. In some embodiments, the secondary sensing modules 180, 190 may not be embedded on the fabric element 162 directly but may be rather components of the circuit boards of the first electronics 166 A and second electronics 166B that are affixed to the fabric element 162. As a tangent, the circuit boards of the first electronics 166A and second electronics 166B may be affixed to the fabric element 162 by adhesive, physical bond such as sewing, or by a shielding capsule.

[0094] FIG. 3C depicts a system for generating a surface topology using a wearable instrumentation applied to the knee, according to another embodiment of the disclosure. With reference to FIGS. 3B and 3C, the system 152 shown in FIG. 3C may be similar to the system 151 shown in FIG. 3B. However, the wearable instrumentation 163 of the system 152 may include a centralized secondary sensing module 190 but not include distributed secondary sensing modules 180 shown in FIG. 3B. That is, in both FIGS. 3B and 3C, the second electronics 166B may include a temperature sensor and an IMU. The only difference may be that each of the first electronics, or node devices, 166 A of the system 151 shown in FIG. 3B may have an accelerometer, while the first electronics, or node devices, 166 A of the system 152 shown in FIG. 3C may be unchanged and an accelerometer may only exist in the second electronics 166B.

[0095] FIGS. 3 A to 3C depict the knee as a representative example, but the system 150 generating a surface topology using a wearable instrumentation of the present disclosure is not limited thereto. The wearable instrumentation 160 of the system 150 may be applied to various parts of the human body, which include, but are not limited to, the feet, knees, hips, elbows, hands, and shoulders. FIG. 3 A is to show the overview of an application of a system for generating a surface topology as a representative example, and the detailed configuration of the system may be described in FIGS. 3B to 15B.

[0096] FIG. 4 depicts an instrumentation mesh formed by a plurality of sensing elements of a wearable instrumentation, according to an embodiment of the disclosure. With reference to FIG. 4, an instrumentation mesh 264 formed in a system 200 may be formed based on a plurality of sensing elements (164, FIG. 3A). The instrumentation mesh 264 may be formed by a possible arrangement of a plurality of sensing elements (164, FIG. 3A) along the surface of the knee but is not limited thereto. In other embodiments, the instrumentation mesh 264 may have a different shape based on the other arrangement of the sensing elements. The instrumentation mesh 264 may be used to measure deformations and the corresponding forces applied. The line segments 272 may correspond to the locations of specific sensing elements (164, FIG. 3 A), and the measured information about lengths of the line segments 272 may be used to reconstruct the geometry of the knee. The connection between line segments 272 may be a node 274, which is shown as a dot in FIG. 4. The specific subset of this instrumentation mesh 264 may be a cell 276, or instrumentation cell, and one cell 276 is indicated with the polygon defined by dashed lines in FIG. 4.

[0097] FIG. 5A depicts two dimensional representation of unequal cells of an instrumentation mesh on when a wearable instrumentation is positioned on a flat surface, according to an embodiment of the disclosure. FIG. 5B depicts three dimensional representation of unequal cells an instrumentation mesh when a wearable instrumentation is worn on the knee, according to an embodiment of the disclosure. With reference to FIGS. 5A and 5B, an instrumentation mesh 364 formed by a plurality of sensing elements (164, FIG. 3 A) in a system 300 may be designed to include unequal cells 376, 377. In other words, a plurality of sensing elements (164, FIG. 3A) may be formed to have different densities throughout a fabric element (162, FIG. 3A) of the wearable instrumentation (160, FIG. 3 A). Since some parts of the body may need higher resolution data in order to collect accurate information, the shape, size, and / or location of cells of the instrumentation mesh 364 may vary across the measured surface as shown by the bold line segments 373. In this case, the patella (knee cap) may exhibit a greater deal of rigid motion as compared to the thigh and thus may benefit from having a higher density of smaller cells 377 in variable geometries to capture more data with the appropriate high resolution.

[0098] FIG. 6A depicts a single cell of an instrumentation mesh at a rest state when the cell is not affected by an applied force, according to an embodiment of the disclosure. FIG. 6B depicts a single cell of an instrumentation mesh at a deformed state when an applied force causes a measurable deformation to the cell, according to an embodiment of the disclosure. FIGS. 6A and 6B are to show characteristics of the cell 476 of the instrumentation mesh formed in a system 400 when the force is applied. With reference to FIGS. 6A and 6B, a simple geometry of the cell 476 formed by four sensing elements (164, FIG. 3A) arranged in a quadrilateral arrangement with one sensing element 472 across one of the diagonals may be shown as one example of the present disclosure. In FIGS. 6A and 6B, Nxis used to designate a specific node of the cell 476, Rxy is used to denote the resistance between node x and node y, and Dxyis used to denote the distances between Nxand Ny. An applied force F may cause a measurable deformation in resistances R12, R13, R14, R23, R34 across the nodes Ni, N2, N3, N4, respectively, and the cell 476 may be changed from the original shape shown in FIG. 6A to the deformed shape shown in FIG. 6B. In some embodiments, the rest state may be a state when the part of a user’s body wearing the wearable instrumentation is at a rest state so that its instrumentation mesh is not affected by an applied force, and the deformed state may be a state when an applied force formed by movement of the at least part of the user’s body causes a deformation on the instrumentation mesh.

[0099] The sensing elements (164, FIG. 3A) in one cell 476 along the perimeter of the quadrilateral may be shared with a neighboring cell if one exists. Each cell 476 surrounded by the sensing elements may measure the deformation of resistive sensing elements caused by a tensile force, such as flexion of a muscle group that expands the skin in that area. The resistances R12, R23, R34, R14, and R13 may be measured directly at a rest state and at their deformed state. Each resistance Rxymay correspond to the distances Dxybetween the nodes Ni, N2, N3, N4 based on a quantifiable calibration; therefore, R12 may be proportional to D12, and measuring R12 may be used to determine D12. The exact calibration may be dependent on the material properties of the resistive element, the mechanical properties of the structure upon which the resistive element is adhered, and signal conditioning electronics.

[0100] FIG. 7 depict drawings for explaining a directional stretching in fabric element of a wearable instrumentation, according to an embodiment of the disclosure. The material properties of the fabric element 562 may play a crucial role in the design of a wearable instrumentation of a system 500. The fabric element 562 of the wearable instrumentation may comprise a single layer or multi-layers using any type of fabrics or fabric composites with embedded sensing elements. The fabrics may include traditional fabrics, rubber fabrics, or fabric composites but are not limited thereto. In some embodiments, the fabric element 562 of the wearable instrumentation may include at least one of: traditional woven fabrics, sheets of rubbers, and sheets of polymers, such as neoprene. In some embodiments, sensing elements (164, FIG. 3A) may also be formed with special inks (e.g. carbon black or silver flake) being printed or deposited onto the fabric element 562 and then subsequently being heat cured. Applied in this way, the inks being printed or deposited onto the fabric element 562 may also stretch and flex along with the fabric element 562 in the same way. In some embodiments, the sensing elements (164, FIG. 3A) may be either sewn, laminated to, or otherwise adhesively bonded to the underlying fabric element 562 so that they stretch and flex along with the fabric element 562.

[0101] In some embodiments, the sensing elements (164, FIG. 3 A) may be formed using multiple methods, such as special inks, sewing, lamination, or adhesive bonding. Each method may not replace the others, but rather be used in conjunction with them.

[0102] With reference to FIG. 7, the fabric element 562 may be stretched in a two-way or in a four-way. Sewn fabrics may have directional stretching based on the weave of the fabrics as shown in FIG. 7. Depending on the material and the weave, the fabric may stretch in two directions, up and down only, as shown in a left image in FIG. 7, or in four directions, up and down and left and right, as shown in a right image in FIG. 7. Material properties for various fabrics may vary significantly. The elasticity profile of the fabric element 562 may be measured and used in manufacturing the fabric element.

[0103] FIG. 8 depicts a sensing element on a stretchable fabric element, according to an embodiment of the disclosure. As a fabric element 662 of a system 600 is deformed or stretched, a resistive sensing element 664 bonded to the fabric element 662 may also stretch. This elongation of the sensing element 664 may also be measured. The underlying mechanical properties of the fabric element 662 may be used to determine the force applied to the fabric element 662 in order to deform it. For example, if the fabric element 662 is a simple spring within elastic limits of the fabric element 662, the stretch may be modeled as F = k x where F is force, k is the spring constant, and x is displacement. Measurement of the change in length of the sensing element 664 may be used to calculate displacement, and the spring constant may be characterized for a specific material, allowing to directly measure the force imparted on the fabric element 662 (e.g. fabric sleeve). These calculations will be further described later on modeling.

[0104] In the above, FIG. 8 primarily describes the functional parameters of a single layer 662. An instrumentation mesh including sensing elements in a single layer 662 may allow for the reconstruction of solid mesh where the line segments are straight lines connecting nodes as shown in FIG. 3A. As a result, the topological model of a single layer 662 may have some limitations in the accuracy that may introduce errors in downstream analysis.

[0105] FIG. 9A depicts a multi-layer stack-up structure of a fabric element, according to an embodiment of the disclosure. FIG. 9B depicts a multi-layer stack-up structure of a fabric element, according to another embodiment of the disclosure. The multi-layer stack-up structure of fabric elements 762, 772 of wearable instrumentations 700, 750 shown in FIGS. 9 A and 9B may be introduced to address the limitations of the topological model of a single layer 662 shown in FIG. 8. With reference to FIG. 9 A, a multi-layer stack-up structure of a fabric element 762 may include two single sensor layers 702, 704 that are distanced apart from each other. Each of the two single sensor layers 702, 704 may indicate a fabric layer including sensing elements 764 inside. In some embodiments, the multi-layer stack-up structure of the fabric element 762 may further include an optional spacer layer 706 between the two single sensor layers 702, 704. The multi-layer stack-up structure of the fabric element 762 may either be accomplished by using a thicker fabric as the top and / or bottom sensor layers 702, 704 or through the inclusion of an optional spacer layer 706 sandwiched between the top and bottom sensor layers 702, 704.

[0106] With reference to FIG. 9B, a multi-layer stack-up structure of the fabric element 772 may include two single sensor layers 752, 754 that are distanced apart from each other. Each of the two single sensor layers 752, 754 may indicate a fabric layer including sensing elements 774, 775 inside. In some embodiments, the multi-layer stack-up structure of the fabric element 772 may further include an optional spacer layer 756 between the two single sensor layers 752, 754. The multi-layer stack-up structure of the fabric element 772 may either be accomplished by using a thicker fabric as the top and / or bottom sensor layers 752, 754 or through the inclusion of an optional spacer layer 756 sandwiched between the top and bottom sensor layers 752, 754. In some embodiments, the fabric element 772 may include at least two layers including the top and / or bottom sensor layers 752, 754 stacked on one another. In this structure, a plurality of top sensing elements 774 may be embedded in the top sensor layer 752 of the at least two layers, and a plurality of bottom sensing elements 775 may be embedded in the bottom sensor layer 754 of the at least two layers. The plurality of the top sensing elements 774 may be configured to be arranged to form a top instrumentation mesh, and the plurality of the bottom sensing elements 775 may be configured to be arranged to form a bottom instrumentation mesh, respectively. The top instrumentation mesh and the bottom instrumentation mesh may be configured to be arranged to correspond with one another.

[0107] FIG. 10 depicts a cross-sectional view of a multi-layer stack-up structure of a fabric element, according to an embodiment of the disclosure. With reference to FIG. 10, the multilayer stack-up structure of a fabric element 862 in a system 800 may allow to create an approximation of the curvature in each line segment. As the multilayer stack-up structure of the fabric element 862 deforms or is bent, the top sensor layer 802 and the bottom sensor layer 804 may both stretch by differing amounts. The difference between the length LtOp of the top sensor layer 802 and the length Lbottom of the bottom sensor layer 804 may be dependent on the curvature of the surfaces of the multi-layer stack-up structure of the fabric element 862, and an approximate degree of curvature for the line segment may be calculated and applied to the topological model. With reference to FIG. 9B, the plurality of the top sensing elements 774 and the bottom sensing elements 775 may be configured to collect the physical data including curvature of the fabric element 772 by measuring the top lengths of line segments of the top instrumentation mesh and the bottom lengths of line segments of the top instrumentation mesh and calculating difference between the top lengths and the bottom lengths.

[0108] FIG. 11 depicts a sensing element on a fabric element at a rest state when the fabric element is not stretched and at a stretched state when the fabric element is stretched, according to an embodiment of the disclosure. With reference to FIG. 11, sensing elements 964 may be responsible for measuring the distances between nodes 974 to allow for digital reconstruction of the body part. The sensing elements 964 may respond to physical deformation by outputting a signal. The sensing elements 964 used in a system 900 may include strain gauges, piezoelectric sensors, capacitive sensors, and conductive filaments stitched in a pattern, such as a mesh pattern as shown in FIG. 4. The resulting signal from the sensing elements 964 may be measured and calibrated to measure distance between two nodes 974. The resistance of conductive filaments stitched on a fabric element 962 may change as the fabric element 962 is stretched. Calibration of the system 900 may allow to take a measurement of resistance between the nodes 974, such as resistances Ri or R2, to solve for the corresponding distances between the nodes 974, such as lengths Li or L2. The measurement from each type of sensing elements 964 used may vary but may still be used to calculate distances between the nodes 974. For example, capacitive stretch sensors vary in capacitance instead of resistance when deformed.

[0109] In some embodiments, in order to calibrate the analysis of the distances of nodes 974, a secondary set of sensing elements may be used. These secondary sensing elements may include, but are not limited to, temperature sensors, inertial measurement units (IMU), accelerometers, gyroscopes, and magnetometers. Ambient conditions may change the working parameters for the sensing elements described above. For example, strain gauges are known to exhibit temperature variation, and the accuracy of the results may be increased by using temperature compensation. Inclusion of the secondary sensing elements may allow for direct measurement of local conditions to allow for the compensation. Additionally, the secondary sensing elements, such as accelerometers, gyroscopes, and magnetometers, may provide data on orientation, frequency of motion, vibrations, among other types of data that may be used in the creation of three dimensional topological models.

[0110] FIG. 12 depicts a diagram of electronics of wearable instrumentation of a system, according to an embodiment of the disclosure. With reference to FIG. 12, a wearable instrumentation (110, FIG. 1; 160, FIG. 3A) of a system may include electronics 1000 to interact with the sensing elements (114, FIG. 1; 164, FIG. 3 A). In some embodiments, the set of electronics 1000 may be detachable from the wearable instrumentation (110, FIG. 1; 160, FIG. 3A). The electronics 1000 may include an acquisition module 1010, a processing module 1020, a storage module 1030, a transmission module 1040, and a battery 1050. Specifically, the acquisition module 1010 may be configured to acquire physical data from the sensing elements embedded in a fabric element. In some embodiments, the acquisition module 1010 may also acquire data from calibration and orientation sensors described in FIG. 11. In some embodiments, the acquisition module 1010 may be data acquisition circuits, such as an analog to digital converter. The processing module 1020 may be configured to process all sensed data. The data may be either processed in real-time or post-processed depending on the use case. In some embodiments, the processing module 1020 may be a processor, such as a microcontroller, computer, and programmable logic controller. The storage module 1030 may be configured to store relevant sensed data locally. In some embodiments, the storage module 1030 may be flash memory but is not limited thereto. The transmission module 1040 may be configured to transmit the sensed data via radios, WiFi, Bluetooth, cellular network, or other wireless transmission protocol for data communication. The transmission module 1040 may include signal processing circuits. The electronics 1000 may operate in low-power mode in order to extend battery life. Trigger events, such as motion or receiving an external signal from another device, may place the system into active mode where the system may perform the operations of any of the modules above.

[0111] FIG. 13 A depicts a flowchart of a method for generating a surface topology using a wearable instrumentation and signal analysis, according to an embodiment of the disclosure. With reference to FIG. 13 A, the method 1100 may start with the step of collecting, by a wearable instrumentation, physical data from at least part of the user’s body (step 1110). In this case, the wearable instrumentation may be any one device described above in reference with FIGS. 1 tol2. That is, the wearable instrumentation may include a fabric element configured to fit the at least part of a user’s body and a plurality of sensing elements configured to be distributed throughout the fabric element. Then, the method 1100 may perform the steps of modeling, by a computation device in communication with the wearable instrumentation, algorithm configured to represent the sensing elements as an instrumentation mesh based on the collected physical data (step 1120); determining, by the computation device, a digital topological model of the at least part of the user’s body (step 1130); and determining, by the computation device, a physiological model for the at least part of the user’ s body by extracting physiological data from the digital topological model (step 1140).

[0112] FIG. 13B depicts a flowchart of a method for generating a surface topology using a wearable instrumentation and signal analysis, according to another embodiment of the disclosure. With reference to FIG. 13B, the data collected from a wearable instrumentation (110, FIG. 1) may be transferred to another computation device (120, FIG. 3A), such as a cell phone and / or computer where the collected data may be processed using computation models to allow for digital reconstruction of the geometry of the body part.

[0113] Specifically, the method 1200 may include the steps of taking and collecting measurements (step 1210), modeling algorithm and analyzing (step 1220), determining a digital topological model (step 1230), and determining a physiological model (step 1240). The initial phase may be the step of taking and collecting measurements (step 1210) from the wearable instrumentation. In the step of taking measurements (step 1210), data from various sources may be collected through the wearable instrumentation, which fits the body part. The measurements data may include various types of data that may be used to reconstruct the three-dimensional surface topological model. The data may include length and curvature data from the embedded sensing elements of the wearable instrumentation, such as length data for lengths of each line segment and curvature data for curvatures of each line segment. In some embodiments, the data may further include supplemental calibration data, orientation data, and additional sensor data from the other sensors. In some embodiments, the step of initial data filtering may also occur in this phase. This data may be collected at each time step and then passed over to the computation device for modeling and analysis.

[0114] Once the measurement data is taken and transmitted to the computation device which may be configured to communicate with the wearable instrumentation, the step of modeling algorithm and analyzing (step 1220) may be performed as the second phase. In the step of modeling algorithms and analysis, the collected data may be initially filtered to remove outliers and erroneous values. Using known connections between the individual sensing elements in the physical wearable instrumentation and their structure and / or arrangement, the algorithm may represent an embedded sensor mesh grid as an instrumentation mesh including a series of nodes and connections. That is, the computation device may determine grid connections from the instrumentation mesh. Then, boundary conditions may be imposed or established based on physical properties and structure of fabric elements. Then, numerical and analytical methods may be used to solve for the positions of each node using this instrumentation mesh information along with calibration data from the sensor measurements and data from the prior timesteps. In some embodiments, orientation data using prior timesteps and additional sensor data may be applied to the model in this phase as well.

[0115] Then, the step of determining or constructing a digital topological model (step 1230) may be performed as the third phase. In the step of digital topologic model construction, the location of each node may be visualized using the data from each time step and a representative surface of digital topological model may be generated. That is, to construct the digital topological model, the computation device may visualize nodes and lengths between nodes at a timestep and interpolate points between nodes and / or connected members using nodes, edge lengths, and curvature. In this case, the prior timesteps may be used to determine variables of motion continuously. The data from the prior timesteps may be aggregated and processed to determine the motion of the digital topological model and individual components of the digital topological model. Forces applied to deform the digital topological model may be solved for by using the physical properties of the fabric element and the data from the sensing elements. Then, applied forces may be calculated using sensor data and fabric properties. In some embodiments, location data may be calculated from the full suite of sensors, based on an initial orientation at the acceleration at each timestep.

[0116] As the last phase, the step of determining and developing a physiological model (step 1240) may be performed. In the step of determining and developing a physiological model (step 1240), biological and / or physiological data may be extracted from the features of the digital topological model, and these biological and / or physiological data extracted from the digital topological model may be compared to baseline or known values from literature, models, aggregated patient data, and / or earlier data from the same patients to determine the physiological model. In some embodiments, the step of data filtering, outlier rejection, and data validation may occur at each step. As shown in FIG. 13B, the data may be stored and collected as time series data over fixed time intervals, and discrete model generation at multiple time steps may allow for motion modeling and analysis. The biological and / or physiological data extracted from the features of the digital topological model may be subdivided into two categories. The primary data may have a discrete, quantitative metric and be derived mathematically. For example, the primary data may include circumference (e.g. measured at the patella, 2" above, and 2" below), a range of motion (maximum flexion and extension of the joint), Q-angle or knee knock, and others. The secondary data may be qualitative and open to interpretation and analysis. For example, the secondary data may include swelling, muscle exertion, hypertrophy, degradation of proper exercise form, compensation from surrounding muscles, and others.

[0117] FIGS. 14A and 14B depict a flowchart of a method for constructing a digital topological model and biological / physiological metrics using machine learning, according to an embodiment of the disclosure. The flowchart shown in FIGS. 14A and 14B are connected through connection points Pl, P2, and P3, respectively. FIGS. 15A and 15B depict a flowchart of another method for constructing a digital topological model and biological / physiological metrics using machine learning, according to another embodiment of the disclosure. The flowchart shown in FIGS. 15A and 15B are connected through connection points P4, P5, and P6, respectively. The step of determining or constructing a digital topological model (step 1230) in FIG. 13B may be performed by at least one of these two approaches: a first method 1300 shown in FIGS. 14A and 14B and a second method 1400 shown in FIGS. 15A and 15B. These two approaches may be similar. The differences may lie between the step 1310 in FIG. 14A and step 1410 in FIG. 15A and between steps 1322, 1324 in FIG. 14B and steps 1422, 1424 in FIG. 15B, which are visually highlighted in the FIGS. 14A to 15B.

[0118] With reference to FIGS. 14A and 14B, in the first method 1300, a test user wears the wearable instrumentation (step 1302), and then two sets of data may be generated. First, sensors from the wearable instrumentation may measure lengths and orientation (step 1304), and they may be represented as measurements in a distance matrix (step 1306). Second, an external three-dimensional scanner may be used to generate a dimensional solid model (step 1308) and represent it as (x,y,z) coordinates of a point cloud (step 1310).

[0119] As this machine learning model is trained (step 1312), it may be used as a predictor, or predictive model. Specifically, when a user wears the wearable instrumentation (step 1314), sensors from the wearable instrumentation may measure lengths and orientation (step 1316), and a distance matrix may be provided (step 1318), the (x,y,z) coordinates of its corresponding point cloud may be predicted using the predictive model (step 1320). Then, the surface model of the knee may be generated from positions (step 1322), and biological / physiological measurements may be extracted from the point cloud (step 1324).

[0120] With reference to FIGS. 15 A and 15B, in the first method 1400, a test user wears the wearable instrumentation (step 1302), and then two sets of data may be generated. First, sensors from the wearable instrumentation may measure lengths and orientation (step 1304), and they may be represented as measurements in a distance matrix (step 1306). Second, an external three-dimensional scanner may be used to generate a dimensional solid model (step 1308) and measure biological / physiological parameters from the scan (step 1410). As this machine learning model is trained (step 1412), it can be used as a predictor, or predictive model. Specifically, when a user wears the wearable instrumentation (step 1314), sensors from the wearable instrumentation may measure lengths and orientation (step 1316), and a distance matrix may be provided (step 1318), the corresponding biological / physiological parameters may be predicted using the predictive model (steps 1420, 1422). Then, the surface model of the knee may be extracted from these biological / physiological parameters (step 1424).

[0121] As described above, according to the present disclosure, the mechanical connection to an outer electrical component can be created via a snap-hook mechanism of the snap structure of the board. This design allows the board to be snapped directly onto other electrical components, thereby eliminating the need for additional connectors and / or additional connection processes, which are conventionally required for electrical connection between a board and other electrical components. Accordingly, the board of the present disclosure can save cost for additional components that are conventionally required for electrical connection and reduce a production time of a board by eliminating an installation time of those additional components in a board.

[0122] FIG. 16 is a high-level block diagram 1500 showing a computing system comprising a computer system useful for implementing an embodiment of the system and process, disclosed herein. Embodiments of the system may be implemented in different computing environments. The computer system includes one or more processors 1502, and can further include an electronic display device 1504 (e.g., for displaying graphics, text, and other data), a main memory 1506 (e.g., random access memory (RAM)), storage device 1508, a removable storage device 1510 (e.g., removable storage drive, a removable memory module, a magnetic tape drive, an optical disk drive, a computer readable medium having stored therein computer software and / or data), user interface device 1511 (e.g., keyboard, touch screen, keypad, pointing device), and a communication interface 1512 (e.g., modem, a network interface (such as an Ethernet card), a communications port, or a PCMCIA slot and card). The communication interface 1512 allows software and data to be transferred between the computer system and external devices. The system further includes a communications infrastructure 1514 (e.g., a communications bus, crossover bar, or network) to which the aforementioned devices / modules are connected as shown.

[0123] Information transferred via communications interface 1514 may be in the form of signals such as electronic, electromagnetic, optical, or other signals capable of being received by communications interface 1514, via a communication link 1516 that carries signals and may be implemented using wire or cable, fiber optics, a phone line, a cellular / mobile phone link, an radio frequency (RF) link, and / or other communication channels. Computer program instructions representing the block diagram and / or flowcharts herein may be loaded onto a computer, programmable data processing apparatus, or processing devices to cause a series of operations performed thereon to produce a computer implemented process.

[0124] Embodiments have been described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems) and computer program products according to embodiments. Each block of such illustrations / diagrams, or combinations thereof, can be implemented by computer program instructions. The computer program instructions when provided to a processor produce a machine, such that the instructions, which execute via the processor, create means for implementing the functions / operations specified in the flowchart and / or block diagram. Each block in the flowchart / block diagrams may represent a hardware and / or software module or logic, implementing embodiments. In alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures, concurrently, etc.

[0125] Computer programs (i.e., computer control logic) are stored in main memory and / or secondary memory. Computer programs may also be received via a communications interface 1512. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor and / or multi- core processor to perform the features of the computer system. Such computer programs represent controllers of the computer system.

[0126] FIG. 17 shows a block diagram of an example system 1600 in which an embodiment may be implemented. The system 1600 includes one or more client devices 1601 such as consumer electronics devices, connected to one or more server computing systems 1630. A server 1630 includes a bus 1602 or other communication mechanism for communicating information, and a processor (CPU) 1604 coupled with the bus 1602 for processing information. The server 1630 also includes a main memory 1606, such as a random access memory (RAM) or other dynamic storage device, coupled to the bus 1602 for storing information and instructions to be executed by the processor 1604. The main memory 1606 also may be used for storing temporary variables or other intermediate information during execution or instructions to be executed by the processor 1604. The server computer system 1630 further includes a read only memory (ROM) 1608 or other static storage device coupled to the bus 1602 for storing static information and instructions for the processor 1604. A storage device 1610, such as a magnetic disk or optical disk, is provided and coupled to the bus 1602 for storing information and instructions. The bus 1602 may contain, for example, thirty -two address lines for addressing video memory or main memory 1606. The bus 1602 can also include, for example, a 32-bit data bus for transferring data between and among the components, such as the CPU 1604, the main memory 1606, video memory and the storage 1610. Alternatively, multiplex data / address lines may be used instead of separate data and address lines.

[0127] The server 1630 may be coupled via the bus 1602 to a display 1612 for displaying information to a computer user. An input device 1614, including alphanumeric and other keys, is coupled to the bus 1602 for communicating information and command selections to the processor 1604. Another type or user input device comprises cursor control 1616, such as a mouse, a trackball, or cursor direction keys for communicating direction information and command selections to the processor 1604 and for controlling cursor movement on the display 1612. According to one embodiment, the functions are performed by the processor 1604 executing one or more sequences of one or more instructions contained in the main memory 1606. Such instructions may be read into the main memory 1606 from another computer-readable medium, such as the storage device 1610. Execution of the sequences of instructions contained in the main memory 1606 causes the processor 1604 to perform the process steps described herein. One or more processors in a multi-processing arrangement may also be employed to execute the sequences of instructions contained in the main memory 1606. In alternative embodiments, hard-wired circuitry may be used in place of or in combination with software instructions to implement the embodiments. Thus, embodiments are not limited to any specific combination of hardware circuitry and software.

[0128] The terms "computer program medium," "computer usable medium," "computer readable medium", and "computer program product," are used to generally refer to media such as main memory, secondary memory, removable storage drive, a hard disk installed in hard disk drive, and signals. These computer program products are means for providing software to the computer system. The computer readable medium allows the computer system to read data, instructions, messages or message packets, and other computer readable information from the computer readable medium. The computer readable medium, for example, may include non-volatile memory, such as a floppy disk, ROM, flash memory, disk drive memory, a CD-ROM, and other permanent storage. It is useful, for example, for transporting information, such as data and computer instructions, between computer systems. Furthermore, the computer readable medium may comprise computer readable information in a transitory state medium such as a network link and / or a network interface, including a wired network or a wireless network that allow a computer to read such computer readable information. Computer programs (also called computer control logic) are stored in main memory and / or secondary memory. Computer programs may also be received via a communications interface. Such computer programs, when executed, enable the computer system to perform the features of the embodiments as discussed herein. In particular, the computer programs, when executed, enable the processor multi-core processor to perform the features of the computer system. Accordingly, such computer programs represent controllers of the computer system.

[0129] Generally, the term "computer-readable medium" as used herein refers to any medium that participated in providing instructions to the processor 1604 for execution. Such a medium may take many forms, including but not limited to, nonvolatile media, volatile media, and transmission media. Non-volatile media includes, for example, optical or magnetic disks, such as the storage device 1610. Volatile media includes dynamic memory, such as the main memory 1606. Transmission media includes coaxial cables, copper wire and fiber optics, including the wires that comprise the bus 1602. Transmission media can also take the form of acoustic or light waves, such as those generated during radio wave and infrared data communications.

[0130] Common forms of computer-readable media include, for example, a floppy disk, a flexible disk, hard disk, magnetic tape, or any other magnetic medium, a CD- ROM, any other optical medium, punch cards, paper tape, any other physical medium with patterns of holes, a RAM, a PROM, an EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave as described hereinafter, or any other medium from which a computer can read.

[0131] Various forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to the processor 1604 for execution. For example, the instructions may initially be carried on a magnetic disk of a remote computer. The remote computer can load the instructions into its dynamic memory and send the instructions over a telephone line using a modem. A modem local to the server 1630 can receive the data on the telephone line and use an infrared transmitter to convert the data to an infrared signal. An infrared detector coupled to the bus 1602 can receive the data carried in the infrared signal and place the data on the bus 1602. The bus 1602 carries the data to the main memory 1606, from which the processor 1604 retrieves and executes the instructions. The instructions received from the main memory 1606 may optionally be stored on the storage device 1610 either before or after execution by the processor 1604.

[0132] The server 1630 also includes a communication interface 1618 coupled to the bus 1602. The communication interface 1618 provides a two-way data communication coupling to a network link 1620 that is connected to the worldwide packet data communication network now commonly referred to as the Internet 1628. The Internet 1628 uses electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 1620 and through the communication interface 1618, which carry the digital data to and from the server 1630, are exemplary forms or carrier waves transporting the information.

[0133] In another embodiment of the server 1630, interface 1618 is connected to a network 1622 via a communication link 1620. For example, the communication interface 1618 may be an integrated services digital network (ISDN) card or a modem to provide a data communication connection to a corresponding type of telephone line, which can comprise part of the network link 1620. As another example, the communication interface 1618 may be a local area network (LAN) card to provide a data communication connection to a compatible LAN. Wireless links may also be implemented. In any such implementation, the communication interface 1618 sends and receives electrical electromagnetic or optical signals that carry digital data streams representing various types of information.

[0134] The network link 1620 typically provides data communication through one or more networks to other data devices. For example, the network link 1620 may provide a connection through the local network 1622 to a host computer 1624 or to data equipment operated by an Internet Service Provider (ISP). The ISP in turn provides data communication services through the Internet 1628. The local network 1622 and the Internet 1628 both use electrical, electromagnetic or optical signals that carry digital data streams. The signals through the various networks and the signals on the network link 1620 and through the communication interface 1618, which carry the digital data to and from the server 1630, are exemplary forms or carrier waves transporting the information.

[0135] The server 1630 can send / receive messages and data, including e-mail, program code, through the network, the network link 1620 and the communication interface 1618. Further, the communication interface 1618 can comprise a USB / Tuner and the network link 1620 may be an antenna or cable for connecting the server 1630 to a cable provider, satellite provider or other terrestrial transmission system for receiving messages, data and program code from another source.

[0136] The example versions of the embodiments described herein may be implemented as logical operations in a distributed processing system such as the system 1600 including the servers 1630. The logical operations of the embodiments may be implemented as a sequence of steps executing in the server 1630, and as interconnected machine modules within the system 1600. The implementation is a matter of choice and can depend on performance of the system 1600 implementing the embodiments. As such, the logical operations constituting said example versions of the embodiments are referred to for e.g., as operations, steps or modules.

[0137] Similar to a server 1630 described above, a client device 1601 can include a processor, memory, storage device, display, input device and communication interface (e.g., e-mail interface) for connecting the client device to the Internet 1628, the ISP, or LAN 1622, for communication with the servers 1630.

[0138] The system 1600 can further include computers (e.g., personal computers, computing nodes) 1605 operating in the same manner as client devices 1601, where a user can utilize one or more computers 1605 to manage data in the server 1630.

[0139] Referring now to FIG. 18, illustrative cloud computing environment 1750 is depicted. As shown, cloud computing environment 1750 comprises one or more cloud computing nodes 1710 with which local computing devices used by cloud consumers, such as, for example, personal digital assistant (PDA), smartphone, smart watch, set- top box, video game system, tablet, mobile computing device, or cellular telephone 1754A, desktop computer 1754B, laptop computer 1754C, and / or automobile computer system 54N may communicate. Nodes 1710 may communicate with one another. They may be grouped (not shown) physically or virtually, in one or more networks, such as Private, Community, Public, or Hybrid clouds as described hereinabove, or a combination thereof. This allows cloud computing environment 1750 to offer infrastructure, platforms and / or software as services for which a cloud consumer does not need to maintain resources on a local computing device. It is understood that the types of computing devices 1754A-N shown in FIG. 18 are intended to be illustrative only and that computing nodes 1710 and cloud computing environment 1750 can communicate with any type of computerized device over any type of network and / or network addressable connection (e.g., using a web browser).

[0140] FIG. 19 illustrates an example of a top-level functional block diagram of a computing device embodiment 1800. The example operating environment is shown as a computing device 1820 comprising a processor 1824, such as a central processing unit (CPU), addressable memory 1827, an external device interface 1826, e.g., an optional universal serial bus port and related processing, and / or an Ethernet port and related processing, and an optional user interface 1829, e.g., an array of status lights and one or more toggle switches, and / or a display, and / or a keyboard and / or a pointermouse system and / or a touch screen. Optionally, the addressable memory may include any type of computer-readable media that can store data accessible by the computing device 1820, such as magnetic hard and floppy disk drives, optical disk drives, magnetic cassettes, tape drives, flash memory cards, digital video disks (DVDs), Bernoulli cartridges, RAMs, ROMs, smart cards, etc. Indeed, any medium for storing or transmitting computer-readable instructions and data may be employed, including a connection port to or node on a network, such as a LAN, WAN, or the Internet. These elements may be in communication with one another via a data bus 1828. In some embodiments, via an operating system 1825 such as one supporting a web browser 1823 and applications 1822, the processor 1824 may be configured to execute steps of a process establishing a communication channel and processing according to the embodiments described above. System embodiments include computing devices such as a server computing device, a buyer computing device, and a seller computing device, each comprising a processor and addressable memory and in electronic communication with each other. The embodiments provide a server computing device that may be configured to: register one or more buyer computing devices and associate each buyer computing device with a buyer profile; register one or more seller computing devices and associate each seller computing device with a seller profile; determine search results of one or more registered buyer computing devices matching one or more buyer criteria via a seller search component. The service computing device may then transmit a message from the registered seller computing device to a registered buyer computing device from the determined search results and provide access to the registered buyer computing device of a property from the one or more properties of the registered seller via a remote access component based on the transmitted message and the associated buyer computing device; and track movement of the registered buyer computing device in the accessed property via a viewer tracking component. Accordingly, the system may facilitate the tracking of buyers by the system and sellers once they are on the property and aid in the seller’s search for finding buyers for their property. The figures described below provide more details about the implementation of the devices and how they may interact with each other using the disclosed technology.

[0141] It is contemplated that various combinations and / or sub-combinations of the specific features and aspects of the above embodiments may be made and still fall within the scope of the invention. Accordingly, it should be understood that various features and aspects of the disclosed embodiments may be combined with or substituted for one another in order to form varying modes of the disclosed invention. Further, it is intended that the scope of the present invention is herein disclosed by way of examples and should not be limited by the particular disclosed embodiments described above.

Claims

AIVItlNUtU CLAIM received by the International Bureau on 16 September 2025 (16.09.2025)CLAIMS:What is claimed is:

1. A system comprising: a fabric element (112, 162) configured to fit at least part of a user’s body; a plurality of sensing elements (114, 164) configured to be embedded and distributed throughout the fabric element and configured to collect physical data from the at least part of the user’s body; a computation device (120, 170) configured to generate a digital reconstruction of the at least part of the user’s body based on the physical data; and electronics (116A, 116B) configured to be attached to the fabric element, wherein the electronics is configured to receive the collected physical data from the plurality of the sensing elements and store and process the received physical data in real-time.

2. The system of claim 1, wherein the fabric element is configured to be in contact with skin of the at least part of the user’s body and flexible to deform with movement of the at least part of the user’s body.

3. The system of claim 1, wherein the digital reconstruction includes a three- dimensional surface topology corresponding to a three-dimensional surface geometry of the at least part of the user’s body.

4. The system of claim 1, wherein each of the sensing elements includes a line shape configured to measure a linear distance.

5. The system of claim 4, wherein when the fabric element is flexible to deform with movement of the at least part of the user’s body, the line shapes of the sensing elements are configured to stretch and flex along with the fabric element.

6. The system of claim 5, wherein the plurality of the sensing elements are configured to be arranged to form an instrumentation mesh (264) including a plurality of polygons.

7. The system of claim 6, wherein the instrumentation mesh includes a plurality of nodes and a plurality of line segments, and each of the line segments is configured to connect between adjacent nodes of the plurality of nodes, and wherein when the fabric element deforms with movement of the at least part of the user’s body, the lengths of some of the line segments are configured to change.

8. The system of claim 7, wherein the plurality of the sensing elements are configured to collect the physical data by measuring resistances of the line segments that are proportional to the lengths of the line segments, respectively.

9. The system of claim 7, wherein the computation device is configured to generate the digital reconstruction including a surface mesh that approximates the surface of the at least part of the user’s body based on the lengths.

10. The system of claim 7, wherein the plurality of the sensing elements are configured to collect the physical data by measuring the resistances of the line segments at a plurality of states including: a rest state when the fabric element is not affected by an applied force, and a deformed state when an applied force formed by movement of the at least part of the user’s body causes a deformation on the fabric element.

11. The system of claim 1, wherein the plurality of sensing elements are configured to be formed by at least one of: ink printing, ink deposition, sewing, lamination, or adhesive bonding.

12. The system of claim 1, wherein some of the plurality of sensing elements are configured to be formed more densely in one or more portions of the fabric element than in other portions.

13. The system of claim 11, further comprising at least one secondary sensing module, each including a temperature sensor and an inertial measurement unit (IMU).

14. The system of claim 1, wherein the sensing elements include at least one of strain gauges, piezoelectric sensors, capacitive sensors, and conductive filaments stitched in a mesh pattern.

15. A system comprising: a fabric element (762) configured to fit at least part of a user’s body, wherein the fabric element (762) comprises a top layer (702) and a bottom layer (704) stacked on one another; a plurality of sensing elements (764) including a plurality of top sensing elements embedded in the top layer and a plurality of bottom sensing elements embedded in the bottom layer, wherein the plurality of sensing elements are configured to collect physical data from the at least part of the user’s body; and a computation device (120, 170) configured to generate a digital reconstruction of the at least part of the user’s body based on the physical data.

16. The system of claim 15, wherein the plurality of the top sensing elements are configured to be arranged to form a top instrumentation mesh, and the plurality of the bottom sensing elements are configured to be arranged to form a bottom instrumentation mesh.

17. The system of claim 16, wherein the top instrumentation mesh and the bottom instrumentation mesh are configured to be arranged to correspond with one another.

18. The system of claim 17, wherein the plurality of the top sensing elements and the bottom sensing elements are configured to collect the physical data including curvature of the fabric element by measuring the top lengths of line segments of the top instrumentation mesh and the bottom lengths of line segments of the top instrumentation mesh and calculating difference between the top lengths and the bottom lengths.

19. A method comprising: collecting, by a wearable instrumentation, physical data from at least part of a user’s body (step 1110), wherein the wearable instrumentation includes a fabric element configured to fit the at least part of a user’s body and a plurality of sensing elements configured to be distributed throughout the fabric element; receiving, by electronics (116A, 116B) configured to be attached to the fabric element, the collected physical data from the plurality of the sensing elements to store and process the received physical data in real-time; modeling, by a computation device in communication with the wearable instrumentation, an algorithm configured to represent the sensing elements as an instrumentation mesh based on the collected physical data (step 1120); determining, by the computation device, a digital topological model of the at least part of the user’s body (step 1130); and determining, by the computation device, a physiological model for the at least part of the user’s body by extracting physiological data from the digital topological model (step 1140).

20. The method of claim 19, wherein the plurality of sensing elements are configured to collect the physical data including curvature of the fabric element.

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