# US 2018/0025664 A1 · Computerized methods and systems for motor skill training > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2018/0025664 A1 - Document ID: US20180025664A1 - Status: Published application - Kind: A1 - Application number: 15/658,548 - Filed: 2017-07-25 - Published: 2018-01-25 - Earliest claimed: 2016-07-25 - Inventors: Anna CLARKE; Per - Olof GUTMAN - Web page: https://patentgrep.com/patent/US20180025664A1 ## CPC - G09B 19/003: {Repetitive work cycles; Sequence of movements} - A63B 69/00: Training appliances or apparatus for special sports (training of parachutists B64D23/00) - A63B 69/06: for rowing or sculling - G06F 17/18: for evaluating statistical data {, e.g. average values, frequency distributions, probability functions, regression analysis (forecasting specially adapted for a specific administrative, business or logistic context G06Q10/04)} - G06F 30/20: Design optimisation, verification or simulation (optimisation, verification or simulation of circuit designs G06F30/30) - G06N 20/00: Machine learning - G09B 5/02: with visual presentation of the material to be studied, e.g. using film strip - G09B 15/00: Teaching music (metronomes G04F5/02) - G09B 19/0038: {Sports} - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G09B: EDUCATIONAL OR DEMONSTRATION APPLIANCES; APPLIANCES FOR TEACHING, OR COMMUNICATING WITH, THE BLIND, DEAF OR MUTE; MODELS; PLANETARIA; GLOBES; MAPS; DIAGRAMS (devices for psychotechnics or for testing reaction times A61B5/16; games, sports, amusements A63; projectors, projector screens G03B) - A63B: APPARATUS FOR PHYSICAL TRAINING, GYMNASTICS, SWIMMING, CLIMBING, OR FENCING; BALL GAMES; TRAINING EQUIPMENT (apparatus for passive exercising, massage A61H) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS ## Abstract A method of motor skill training using visual computer generated feedback, comprising: receiving from a plurality of sensors measurements of a current position and motion of a trainee's body; constructing an estimated model of the body in the current position and motion based on the measurements and on specific characteristics of the body; calculating a predicted model of the body in an estimated future state based on the estimated model; and presenting the predicted model to the trainee on a visual output device. ## Claims ### Claim 1 A method of motor skill training using visual computer generated feedback, comprising: receiving from a plurality of sensors measurements of a current position and motion of a trainee's body; constructing an estimated model of said body in said current position and motion based on said measurements and on specific characteristics of said body; calculating a predicted model of said body in an estimated future state based on said estimated model; and presenting said predicted model to said trainee on a visual output device. ### Claim 2 (depends on 1) The method of claim 1, further comprising: constructing a goal model of said body in a pre-defined desired position and orientation; and presenting said goal model on said visual output device, to guide said trainee in performing movements so that said predicted model is similar to said goal model. ### Claim 3 (depends on 1) The method of claim 1, further comprising: calculating a motion error between a pre-defined movement task and movement from said estimated model; calculating desired forces and moments from said motion error using control algorithms; calculating a difference between said desired forces and moments and currently estimated forces and moments acting on said trainee's body; constructing a goal model of said body in a desired position from said difference using learning algorithms; and presenting said goal model on said visual output device, to guide said trainee in performing movements so that said predicted model is similar to said goal model. ### Claim 4 (depends on 3) The method of claim 3, wherein said constructing is done by a state machine of schemes to select an action for creating said goal model. ### Claim 5 (depends on 3) The method of claim 3, wherein said learning algorithms includes: classifying said difference according to at least one of magnitude, direction and type; selecting one of a plurality of actions based on said classification; creating said goal model from said selected action; and evaluating efficiency of said selected action. ### Claim 6 (depends on 5) The method of claim 5, wherein said evaluating is done according to a new calculated difference between said desired forces and moments and current forces and moments acting on said trainee's body. ### Claim 7 (depends on 5) The method of claim 5, wherein said evaluating is done according to a new calculated error between a measured movement and said pre-defined movement task. ### Claim 8 (depends on 5) The method of claim 5, wherein said selecting is also based on a Q-learning look-up table and said evaluating includes updating said look-up table. ### Claim 9 (depends on 5) The method of claim 5, wherein said selecting is also based on a guidance strategy for selecting unevaluated actions. ### Claim 10 (depends on 5) The method of claim 5, wherein said selecting is also based on previously evaluated efficiency of said plurality of actions. ### Claim 11 (depends on 5) The method of claim 5, wherein said learning algorithms further includes: performing Monte-Carlo simulations of a plurality of suggested action optimizations; selecting one of said plurality of action optimizations based on a cost function calculated on said simulations; and updating said selected action according to said selected action optimization. ### Claim 12 (depends on 5) The method of claim 5, wherein said learning algorithms further includes: performing Monte-Carlo simulations of a plurality of randomly generated actions; selecting at least one of said randomly generated actions based on a cost function calculated on said simulations; and adding said selected at least one action to said plurality of actions. ### Claim 13 (depends on 3) The method of claim 3, further comprising: presenting on said visual output device recommended movement instructions based on said predicted model, said goal model and said specific characteristics. ### Claim 14 (depends on 3) The method of claim 3, further comprising: evaluating success of said trainee in performing said pre-defined movement task; and activating a second pre-defined movement task based on said success. ### Claim 15 (depends on 3) The method of claim 3, further comprising: detecting failure of said trainee in performing said pre-defined movement task; and activating a default pre-defined movement task. ### Claim 16 (depends on 1) The method of claim 1, further comprising: presenting on said visual output device background elements relative to said predicted model. ### Claim 17 (depends on 1) The method of claim 1, wherein said trainee is a skydiving trainee. ### Claim 18 (depends on 1) The method of claim 1, wherein said visual output device is an augmented reality head device. ### Claim 19 (depends on 1) A computer readable medium comprising computer executable instructions adapted to perform the method of claim 1. ### Claim 20 A system of motor skill training using visual computer generated feedback, comprising: a plurality of sensors for acquiring measurements of a current position and motion of a trainee's body; at least one computerized processor for: constructing an estimated model of said body in said current position and motion based on said measurements and on specific characteristics of said body; and calculating a predicted model of said body in an estimated future state based on said estimated model; and a visual output device for presenting said predicted model to said trainee. ### Claim 21 A software program product for automatically cross-referencing error information between code developers, comprising: a non-transitory computer readable storage medium; first program instructions for receiving from a plurality of sensors measurements of a current position and motion of a trainee's body; second program instructions for constructing an estimated model of said body in said current position and motion based on said measurements and on specific characteristics of said body; third program instructions for calculating a predicted model of said body in an estimated future state based on said estimated model; and fourth program instructions for presenting said predicted model to said trainee on a visual output device; wherein said first, second, third and fourth program instructions are executed by at least one computerized processor from said non-transitory computer readable storage medium.