# US 2024/0201792 A1 · Full hand kinematics reconstruction using electrical impedance tomography wearable > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2024/0201792 A1 - Document ID: US20240201792A1 - Status: Published application - Kind: A1 - Application number: 18/537,224 - Filed: 2023-12-12 - Published: 2024-06-20 - Earliest claimed: 2022-12-14 - Assignees: Google Inc. - Applicants: Google LLC - Inventors: Stiven Guillaume Francois Morvan; Dongeek Shin; Andrea Colaco; Sambuddha Basu; Sean Kyungmok Bae; Junyi Zhu - Web page: https://patentgrep.com/patent/US20240201792A1 ## CPC - G06F 3/017: {Gesture based interaction, e.g. based on a set of recognized hand gestures (interaction based on gestures traced on a digitiser G06F3/04883)} - A61B 5/0536: Impedance imaging, e.g. by tomography - A61B 5/263: characterised by the electrode materials - G06F 3/014: {Hand-worn input/output arrangements, e.g. data gloves} - G06V 10/12: Details of acquisition arrangements; Constructional details thereof - G06V 10/764: using classification, e.g. of video objects - G06V 40/28: {Recognition of hand or arm movements, e.g. recognition of deaf sign language (static hand signs G06V40/113)} - G06F 3/011: {Arrangements for interaction with the human body, e.g. for user immersion in virtual reality (blind teaching G09B21/00)} - G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection} - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D) - G06V: IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING ## Abstract Techniques include determining hand gestures formed by a user based on an electrical impedance tomograph of the wrist. For example, a user may be outfitted with a flexible wristband that fits snugly around the wrist and contains a plurality of electrodes, e.g., 32 electrodes. When a current is applied to a first subset of the electrodes, e.g., two of 32 electrodes, the electric field induced through at least one cross-section of the wrist will in turn induce a voltage across adjacent pairs of a second subset of the electrodes (e.g., the other 30 of 32 electrodes). From this current and induced voltage, one may use techniques of electrical impedance tomography (EIT) to determine the electrical impedance throughout the at least one cross-section of the wrist, e.g., in an electrical impedance tomograph. One may use a neural network to map the electrical impedance tomograph to a hand gesture. ## Claims ### Claim 1 A method, comprising: receiving an electrical impedance tomograph representing a map of electrical impedance through at least one cross-section of a wrist of a user; determining a gesture formed by a hand of the user based on the electrical impedance tomograph; and triggering execution of a command related to an object being displayed in an augmented reality (AR) system based on the gesture. ### Claim 2 (depends on 1) The method as in claim 1, wherein determining the gesture includes: inputting the electrical impedance tomograph into a machine learning classification engine, the machine learning classification engine being configured to map the electrical impedance tomograph to the gesture. ### Claim 3 (depends on 2) The method as in claim 2, wherein, in the machine learning classification engine, the hand is represented by a plurality of keypoints and the gesture is represented by an arrangement of the plurality of keypoints in space. ### Claim 4 (depends on 1) The method as in claim 1, wherein the electrical impedance tomograph is generated using a plurality of electrodes disposed on a band and in contact with the wrist of the user, the band being configured to fit snugly around the wrist of the user. ### Claim 5 (depends on 4) The method as in claim 4, where at least one of the plurality of electrodes includes brass. ### Claim 6 (depends on 4) The method as in claim 4, wherein receiving the electrical impedance tomograph includes: applying an electrical current to a first subset of the plurality of electrodes; determining electrical voltages between adjacent pairs of a second subset of the plurality of electrodes; and generating the electrical impedance tomograph based on the electrical current at the first subset of the plurality of electrodes and the electrical voltages between adjacent pairs of the second subset of the plurality of electrodes. ### Claim 7 (depends on 1) The method as in claim 1, wherein the electrical impedance tomograph represents an average electrical impedance through a plurality of cross-sections of the wrist of the user. ### Claim 8 An augmented reality (AR) system, including: gesture detection circuitry coupled to a memory, the gesture detection circuitry being configured to: receive an electrical impedance tomograph representing a map of electrical impedance through at least one cross-section of a wrist of a user; and determine a gesture formed by a hand of the user based on the electrical impedance tomograph; and wherein the AR system is configured to trigger execution of a command related to an object being displayed in the AR system based on the gesture. ### Claim 9 (depends on 8) The AR system as in claim 8, wherein the gesture detection circuitry configured to determine the gesture is further configured to: input the electrical impedance tomograph into a machine learning classification engine, the machine learning classification engine being configured to map the electrical impedance tomograph to the gesture. ### Claim 10 (depends on 9) The AR system as in claim 9, wherein, in the machine learning classification engine, the hand is represented by a plurality of keypoints and the gesture is represented by an arrangement of the plurality of keypoints in space. ### Claim 11 (depends on 8) The AR system as in claim 8, wherein the electrical impedance tomograph is generated using a plurality of electrodes disposed on a band and in contact with the wrist of the user, the band being configured to fit snugly around the wrist of the user. ### Claim 12 (depends on 11) The AR system as in claim 11, where at least one of the plurality of electrodes includes brass. ### Claim 13 (depends on 11) The AR system as in claim 11, wherein receiving the electrical impedance tomograph includes: applying an electrical current to a first subset of the plurality of electrodes; determining electrical voltages between adjacent pairs of a second subset of the plurality of electrodes; and generating the electrical impedance tomograph based on the electrical current at the first subset of the plurality of electrodes and the electrical voltages between adjacent pairs of the second subset of the plurality of electrodes. ### Claim 14 (depends on 8) The AR system as in claim 8, wherein the electrical impedance tomograph represents an average electrical impedance through a plurality of cross-sections of the wrist of the user. ### Claim 15 A computer program product comprising a nontransitory storage medium, the computer program product including code that, when executed by processing circuitry, causes the processing circuitry to perform a method, the method comprising: receiving an electrical impedance tomograph representing a map of electrical impedance through at least one cross-section of a wrist of a user; determining a gesture formed by a hand of the user based on the electrical impedance tomograph; and triggering execution of a command related to an object being displayed in an augmented reality (AR) system based on the gesture. ### Claim 16 (depends on 15) The computer program product as in claim 15, wherein determining the gesture includes: inputting the electrical impedance tomograph into a machine learning classification engine, the machine learning classification engine being configured to map the electrical impedance tomograph to the gesture. ### Claim 17 (depends on 16) The computer program product as in claim 16, wherein, in the machine learning classification engine, the hand is represented by a plurality of keypoints and the gesture is represented by an arrangement of the plurality of keypoints in space. ### Claim 18 (depends on 15) The computer program product as in claim 15, wherein the electrical impedance tomograph is generated using a plurality of electrodes disposed on a band and in contact with the wrist of the user, the band being configured to fit snugly around the wrist of the user. ### Claim 19 (depends on 18) The computer program product as in claim 18, wherein receiving the electrical impedance tomograph includes: applying an electrical current to a first subset of the plurality of electrodes; determining electrical voltages between adjacent pairs of a second subset of the plurality of electrodes; and generating the electrical impedance tomograph based on the electrical current at the first subset of the plurality of electrodes and the electrical voltages between adjacent pairs of the second subset of the plurality of electrodes. ### Claim 20 (depends on 15) The computer program product as in claim 15, wherein the electrical impedance tomograph represents an average electrical impedance through a plurality of cross-sections of the wrist of the user.