# US 12,223,400 B2 · Impact mitigation and pressure mapping using smart fabrics > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,223,400 B2 - Document ID: US12223400B2 - Status: Granted - Kind: B2 - Application number: 17/249,076 - Filed: 2021-02-19 - Published: 2022-08-25 - Granted: 2025-02-11 - Earliest claimed: 2021-02-19 - Assignees: International Business Machines Corporation - Applicants: International Business Machines Corporation - Inventors: Tathagato Bose; Sourav Bhattacharjee; Ranjeeth Pasupathi; Diwesh Pandey - Web page: https://patentgrep.com/patent/US12223400B2 ## CPC - G06N 20/00: Machine learning - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - A41D 1/002: {adapted to accommodate electronic equipment (patients' garments for medical monitoring A41D13/1281; luminous ornaments A41D27/085)} - G06N 3/09: Supervised learning - G06N 5/04: Inference or reasoning models - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 40/67: for remote operation - A41D 13/02: Overalls {, e.g. bodysuits or bib overalls} (sweating suits A61H36/00) - A61B 5/11: Measuring movement of the entire body or parts thereof, e.g. head or hand tremor or mobility of a limb {(A61B5/1038 takes precedence; motion detection to correct for motion artifacts in physiological signals A61B5/721)} - A61B 5/6804: {Garments; Clothes} - A61B 5/7267: {involving training the classification device} - G06N 3/08: Learning methods - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - A41D: OUTERWEAR; PROTECTIVE GARMENTS; ACCESSORIES - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) ## Abstract A method, computer system, and a computer program product for impact response is provided. The present invention may include triggering a stimulus if a threshold is reached or exceeded based on a positional relationship between a body suit and a single-track vehicle, wherein the stimulus activates one or more pressure sensors of the body suit. The present invention may include generating a pressure map of the body suit utilizing the one or more pressure sensors. The present invention may include generating an injury map using a machine learning model, wherein the injury map is based on the pressure map and one or more input parameters, and wherein the input parameters are specific to a user of the body suit. ## Claims ### Claim 1 A method for impact response, the method comprising: triggering a stimulus if a threshold is reached or exceeded based on a positional relationship between a user of a body suit and a single-track vehicle, wherein the stimulus activates one or more pressure sensors integrated into a smart fabric of the body suit designed to measure an impact to the body suit; generating a pressure map of the body suit utilizing the one or more pressure sensors, wherein the pressure map is comprised of a plurality of pixels corresponding to a plurality of intersection points of the smart fabric of the body suit, wherein each of the plurality of pixels is assigned a value illustrating one or more pressure regions corresponding to the impact on the body suit; and generating an injury map using the pressure map and a machine learning model, wherein the machine learning model utilizes one or more input parameters specific to the user of the body suit to determine a neighborhood pressure expansion, wherein the neighborhood pressure expansion is utilized to expand the one or more pressure regions of the pressure map to one or more surrounding pixels based on the one or more input parameters specific to the user. ### Claim 2 (depends on 1) The method of claim 1, wherein the positional relationship between the user of the body suit and the single-track vehicle is determined based on a composite center of gravity, the composite center of gravity being a center of gravity relationship between the user of the body suit and the single-track vehicle. ### Claim 3 (depends on 1) The method of claim 1, wherein the machine learning model utilizes the one or more input parameters to determine a pressure mapping correlation, and wherein the pressure mapping correlation is a determined correlation between two or more body parts of the user based on the one or more input parameters, wherein the one or more input parameters include at least a medical history of the user of the body suit. ### Claim 4 (depends on 1) The method of claim 1, further comprising: displaying, using the body suit, a criticality level of an injury utilizing one or more colors. ### Claim 5 (depends on 1) The method of claim 1, further comprising: translating the injury map into one or more injury captions, wherein the one or more injury captions describe the injury map in words, and wherein the one or more injury captions are improved over time based on injuries resulting from the impact on the body suit being utilized as feedback for the machine learning model. ### Claim 6 (depends on 1) The method of claim 1, further comprising: retraining the machine learning model using feedback received based on the accuracy of the injury map, wherein the machine learning model is a deep neural network which is trained based on the one or more input parameters specific to the user to determine the neighborhood pressure expansion, wherein the machine learning model is retrained to improve future injury maps and learn detailed injury descriptions. ### Claim 7 (depends on 1) The method of claim 1, further comprising: retrieving, in response to a scanning of a portion of the body suit, the one or more input parameters specific to the user and additional information related to the impact on the body suit. ### Claim 8 (depends on 7) The method of claim 7, wherein the one or more input parameters and the additional information are displayed to a third party, wherein the additional information includes at least a velocity of the user of the body suit at a time of the impact and the time of impact. ### Claim 9 (depends on 1) The method of claim 1, wherein generating the pressure map further comprises: determining the impact to the body suit using a voltage difference based on an electrical resistance variation utilizing a micro-controller to measure the electrical resistance as a function of time. ### Claim 10 A computer system for impact response, comprising: one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein execution of the program instructions causes the computer system to perform a method comprising: triggering a stimulus if a threshold is reached or exceeded based on a positional relationship between a user of a body suit and a single-track vehicle, wherein the stimulus activates one or more pressure sensors integrated into a smart fabric of the body suit designed to measure an impact to the body suit; generating a pressure map of the body suit utilizing the one or more pressure sensors, wherein the pressure map is comprised of a plurality of pixels corresponding to a plurality of intersection points of the smart fabric of the body suit, wherein each of the plurality of pixels is assigned a value illustrating one or more pressure regions corresponding to the impact on the body suit; and generating an injury map using the pressure map and a machine learning model, wherein the machine learning model utilizes one or more input parameters specific to the user of the body suit to determine a neighborhood pressure expansion, wherein the neighborhood pressure expansion is utilized to expand the one or more pressure regions of the pressure map to one or more surrounding pixels based on the one or more input parameters specific to the user. ### Claim 11 (depends on 10) The computer system of claim 10, wherein the method further comprises determining the positional relationship between the user of the body suit and the single-track vehicle, wherein the positional relationship between the user of the body suit and the single-track vehicle is determined based on a composite center of gravity, the composite center of gravity being a center of gravity relationship between the user of the body suit and the single-track vehicle. ### Claim 12 (depends on 10) The computer system of claim 10, wherein the machine learning model utilizes the one or more input parameters to determine a pressure mapping correlation, and wherein the pressure mapping correlation is a determined correlation between two or more body parts of the user based on the one or more input parameters, wherein the one or more input parameters include at least a medical history of the user of the body suit. ### Claim 13 (depends on 10) The computer system of claim 10, wherein the method further comprises: displaying, using the body suit, a criticality level of an injury utilizing one or more colors. ### Claim 14 (depends on 10) The computer system of claim 10, wherein the method further comprises: translating the injury map into one or more injury captions, wherein the one or more injury captions describe the injury map in words, and wherein the one or more injury captions. ### Claim 15 A computer program product for impact response, comprising: one or more non-transitory computer-readable storage media and program instructions stored on at least one of the one or more tangible storage media, the program instructions executable by a processor to cause the processor to perform a method comprising: triggering a stimulus if a threshold is reached or exceeded based on a positional relationship between a user of a body suit and a single-track vehicle, wherein the stimulus activates one or more pressure sensors integrated into a smart fabric of the body suit designed to measure an impact to the body suit; generating a pressure map of the body suit utilizing the one or more pressure sensors, wherein the pressure map is comprised of a plurality of pixels corresponding to a plurality of intersection points of the smart fabric of the body suit, wherein each of the plurality of pixels is assigned a value illustrating one or more pressure regions corresponding to the impact on the body suit; and generating an injury map using the pressure map and a machine learning model, wherein the machine learning model utilizes one or more input parameters specific to the user of the body suit to determine a neighborhood pressure expansion, wherein the neighborhood pressure expansion is utilized to expand the one or more pressure regions of the pressure map to one or more surrounding pixels based on the one or more input parameters specific to the user. ### Claim 16 (depends on 15) The computer program product of claim 15, wherein the method further comprises determining the positional relationship between the user of the body suit and the single-track vehicle, wherein the positional relationship between the user of the body suit and the single-track vehicle is determined based on a composite center of gravity, the composite center of gravity being a center of gravity relationship between the user of the body suit and the single-track vehicle. ### Claim 17 (depends on 15) The computer program product of claim 15, wherein the machine learning model utilizes the one or more input parameters to determine a pressure mapping correlation, and wherein the pressure mapping correlation is a determined correlation between two or more body parts of the user based on the one or more input parameters, wherein the one or more input parameters include at least a medical history of the user of the body suit. ### Claim 18 (depends on 15) The computer program product of claim 15, wherein the method further comprises: displaying, using the body suit, a criticality level of an injury utilizing one or more colors. ### Claim 19 (depends on 15) The computer program product of claim 15, wherein the method further comprises: translating the injury map into one or more injury captions, wherein the one or more injury captions describe the injury map in words, and wherein the one or more injury captions are improved over time based on injuries resulting from the impact on the body suit being utilized as feedback for the machine learning model.