# US 2017/0281009 A1 · Digital image correlation for measuring skin strain and deformation > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2017/0281009 A1 - Document ID: US20170281009A1 - Status: Published application - Kind: A1 - Application number: 15/514,297 - Filed: 2015-10-05 - Published: 2017-10-05 - Earliest claimed: 2011-10-17 - Assignees: Massachusetts Institute of Technology - Applicants: Massachusetts Institute of Technology - Inventors: Edward W. Obropta, JR.; Dava J. Newman - Web page: https://patentgrep.com/patent/US20170281009A1 ## CPC - A61B 5/442: {Evaluating skin mechanical properties, e.g. elasticity, hardness, texture, wrinkle assessment} - A41H 1/00: Measuring aids or methods - A61B 5/0077: {Devices for viewing the surface of the body, e.g. camera, magnifying lens} - A61B 5/1121: {Determining geometric values, e.g. centre of rotation or angular range of movement} - A61B 5/1127: {using markers} - A61B 5/7225: {Details of analogue processing, e.g. isolation amplifier, gain or sensitivity adjustment, filtering, baseline or drift compensation (input circuits for detecting, measuring, or recording bioelectric or biomagnetic signals A61B5/30; specific diagnostic methods using bioelectric or biomagnetic signals A61B5/316)} - A61B 5/7278: {Artificial waveform generation or derivation, e.g. synthesizing signals from measured signals} - A61B 5/0064: {Body surface scanning} - A61B 5/6828: {Leg} - A61B 2562/0261: Strain gauges - A61B 2576/02: specially adapted for a particular organ or body part - G16H 30/40: for processing medical images, e.g. editing - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - A41H: APPLIANCES OR METHODS FOR MAKING CLOTHES, e.g. FOR DRESS-MAKING OR FOR TAILORING, NOT OTHERWISE PROVIDED FOR - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Described embodiments provide a system and method for measuring surface deformation and strain using digital image correlation of a surface of a test object. A data acquisition system acquires images of the surface. The surface has a unique surface pattern to facilitate image acquisition. The images are grouped into one or more image sets. Three dimensional image correlation is performed on each of the image sets to determine deformation and strain data. The deformation and strain data from the image sets are stitched into one dataset. Principal strains and lines of non-extension (LoNEs) directions are determined. One or more LoNEs streamlines and lines of maximum and minimum extensions are determined. Visualizations for the strain magnitudes, LoNE streamlines, maximum and minimum extension streamlines are generated in three dimensions. ## Claims ### Claim 1 A method for measuring surface deformation and strain using digital image correlation of a surface of a test object, comprising: acquiring images of the surface, the surface having a unique surface pattern; grouping the images into one or more image sets; performing three dimensional image correlation on each of the image sets to determine deformation and strain data; stitching the deformation and strain data from the image sets into one dataset; determining principal strains and lines of non-extension (LoNEs) directions; determining one or more LoNEs streamlines; determining lines of maximum and minimum extensions; and providing visualizations for the strain magnitudes, LoNE streamlines, maximum and minimum extension streamlines in three dimensions. ### Claim 2 (depends on 1) The method of claim 1, further comprising: based on one or more of the determined principal strains, the LoNEs directions, the one or more LoNEs streamlines and the lines of maximum and minimum extensions, generating a pattern for one or more customized coverings for the test object. ### Claim 3 (depends on 2) The method of claim 2, wherein the surface is human skin and the test object is a human, and wherein the one or more custom coverings comprise at least one of: custom garments, custom orthotics, custom prosthetics and custom wearable electronic devices. ### Claim 4 (depends on 2) The method of claim 2, further comprising: based on the generated pattern, generating the one or more customized coverings. ### Claim 5 (depends on 1) The method of claim 1, further comprising: generating the unique surface pattern, the unique surface pattern having a pattern with a granularity determined to provide image tracking resolution of approximately 1 mm2; and applying the generated unique surface patter to the surface. ### Claim 6 (depends on 5) The method of claim 5, further comprising: generating an application tool to apply the unique surface pattern to the surface. ### Claim 7 (depends on 1) The method of claim 1, wherein stitching the deformation and strain data from the image sets into one dataset comprises: converting the image sets from into a plurality of meshes; and stitching the meshes together at points of overlap between each mesh. ### Claim 8 (depends on 7) The method of claim 7, further comprising: at a point of overlap between the meshes, merging data of the image sets by re-meshing the data at the overlap point. ### Claim 9 (depends on 7) The method of claim 7, further comprising: at a point of overlap between the meshes, averaging data of the image sets at the overlap point. ### Claim 10 (depends on 7) The method of claim 7, further comprising: at a point of overlap between the meshes: determining a measurement quality index, q, and keeping data from the image sets that reach a predetermined threshold of q, and discarding data from the image sets that do not reach the predetermined threshold. ### Claim 11 (depends on 10) The method of claim 10, wherein q is defined as an inverse of a standard deviation of a matching error of the image sets given by q=1/σ. ### Claim 12 (depends on 7) The method of claim 7, wherein the image sets are in a curvilinear grid data format, and wherein the meshes are triangular meshes. ### Claim 13 (depends on 1) The method of claim 1, wherein determining principal strains and lines of non-extension (LoNEs) directions comprises: identifying and determining motion of, based on the unique surface pattern, pixel groups from an image of an initial position of the surface and from an image of a deformed position of the surface; determining one or more strains of each pixel group; projecting the determined strains onto a two-dimensional (2D) plane tangent to the surface; rotating the projected strains onto axes defined with respect to the surface; based on the rotated strains, generating a longitudinal strain, a circumferential strain, a shear strain and principal strains for each pixel group; if the principal strains associated with each pixel group have opposite signs, determining an angle of a line of non-extension for the pixel group based on the principal strains; if the principal strains associated with each pixel group have opposite signs, determining an angle of minimum extension or minimum compression for the pixel group; projecting the determined angle of a line of non-extension or angles of minimum extension or minimum compression for each pixel group onto the body surface; and connecting angles of lines of non-extension into streamlines. ### Claim 14 (depends on 13) The method of claim 13 wherein determining one or more strains surrounding each marker point comprises calculating at least one of: Lagrangian strains and Euler-Almansi strains. ### Claim 15 (depends on 13) The method of claim 13, further comprising: generating the 2D tangential plane by averaging normal vectors to the planes between neighboring pairs of strain vectors associated with the corresponding pixel group, wherein a neighboring pair of strain vectors comprises strain vectors associated with pixel groups that are adjacent to one another. ### Claim 16 (depends on 1) The method of claim 1, wherein determining one or more LoNEs streamlines and determining lines of maximum and minimum extensions comprises: selecting one or more seed points within the image sets; linearly interpolating a vector filed of LoNEs directions of the image sets by transforming to a local tangential coordinate system; determining streamlines where a current position plus a velocity at that point multiplied by a time step equals a new position; and translating the determined streamlines from a 2D coordinate system to a 3D coordinate system. ### Claim 17 (depends on 1) The method of claim 1, wherein the images are acquired by one or more stereoscopic camera pairs. ### Claim 18 (depends on 1) The method of claim 1, wherein the images are acquired by at least one of: one or more optical cameras, computed tomography (CT) and magnetic resonance imaging (MRI). ### Claim 19 (depends on 1) The method of claim 1, further comprising: acquiring deformation data from one or more sensors in contact with the surface. ### Claim 20 A system for measuring surface deformation and strain using digital image correlation of a surface of a test object, the system comprising: a data collection system configured to acquire images of the surface, the surface having a unique surface pattern; and a lines of non-extension (LoNEs) processor configured to: group the images into one or more image sets; perform three dimensional image correlation on each of the image sets to determine deformation and strain data; stitch the deformation and strain data from the image sets into one dataset; determine principal strains and LoNEs directions; determine one or more LoNEs streamlines; determine lines of maximum and minimum extensions; provide visualizations for the strain magnitudes, LoNE streamlines, maximum and minimum extension streamlines in three dimensions; and generate, based on one or more of the determined principal strains, the LoNEs directions, the one or more LoNEs streamlines and the lines of maximum and minimum extensions, a pattern for one or more customized coverings for the test object. ### Claim 21 (depends on 20) The system of claim 20, wherein the surface is human skin and the test object is a human, and wherein the one or more custom coverings comprise at least one of: custom garments, custom orthotics, custom prosthetics and custom wearable electronic devices. ### Claim 22 (depends on 20) The system of claim 20, wherein the data collection system comprises one or more stereoscopic camera pairs. ### Claim 23 (depends on 20) The system of claim 20, wherein the data collection system comprises at least one of: one or more optical cameras, computed tomography (CT) imager, and a magnetic resonance imager (MRI). ### Claim 24 (depends on 20) The system of claim 20, wherein the data collection system comprises one or more sensors in contact with the surface. ### Claim 25 A non-transitory machine-readable storage medium, having encoded thereon program code, wherein, when the program code is executed by a machine, the machine implements a method for measuring surface deformation and strain using digital image correlation of a surface of a test object, the method comprising: acquiring images of the surface, the surface having a unique surface pattern; grouping the images into one or more image sets; performing three dimensional image correlation on each of the image sets to determine deformation and strain data; stitching the deformation and strain data from the image sets into one dataset; determining principal strains and lines of non-extension (LoNEs) directions; determining one or more LoNEs streamlines; determining lines of maximum and minimum extensions; providing visualizations for the strain magnitudes, LoNE streamlines, maximum and minimum extension streamlines in three dimensions; and based on one or more of the determined principal strains, the LoNEs directions, the one or more LoNEs streamlines and the lines of maximum and minimum extensions, generating a pattern for one or more customized coverings for the test object. ### Claim 26 (depends on 25) The non-transitory machine-readable storage medium of claim 25, wherein the surface is human skin and the test object is a human, and wherein the one or more custom coverings comprise at least one of: custom garments, custom orthotics, custom prosthetics and custom wearable electronic devices.