# US 12,515,312 B2 · Mobility based on machine-learned movement determination

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- Number: US 12,515,312 B2
- Document ID: US12515312B2
- Status: Granted
- Kind: B2
- Application number: 18/653,767
- Filed: 2024-05-02
- Published: 2024-08-22
- Granted: 2026-01-06
- Earliest claimed: 2020-12-06
- Assignees: Cionic, Inc.
- Applicants: Cionic, Inc.
- Inventors: Jeremiah Robison; Michael Dean Achelis; Lina Avancini Colucci; Sidney Rafael Primas; Andrew James Weitz
- Web page: https://patentgrep.com/patent/US12515312B2

## CPC

- A61H 3/00: Appliances for aiding patients or disabled persons to walk about (apparatus for helping babies to walk A47D13/04 {; applying electrical currents by contact electrodes for stimulating motor muscles, e.g. walking assistance A61N1/36003})
- B25J 9/0006: {Exoskeletons, i.e. resembling a human figure}
- A61B 5/1038: {Measuring plantar pressure during gait}
- A61B 5/1114: {Tracking parts of the body}
- A61B 5/112: {Gait analysis}
- A61B 5/1126: {using a particular sensing technique}
- A61B 5/271: Arrangements of electrodes with cords, cables or leads, e.g. single leads or patient cord assemblies
- A61B 5/296: for electromyography [EMG]
- A61B 5/397: Analysis of electromyograms
- A61B 5/6811: {External prosthesis}
- A61B 5/6812: {Orthopaedic devices}
- A61B 5/6828: {Leg}
- A61B 5/6829: {Foot or ankle}
- A61B 5/7267: {involving training the classification device}
- A61N 1/0452: {Specially adapted for transcutaneous muscle stimulation [TMS]}
- A61N 1/0484: {Garment electrodes worn by the patient}
- A61N 1/36003: {of motor muscles, e.g. for walking assistance}
- A61N 1/36031: {using physiological parameters for adjustment}
- G06F 3/015: {Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection}
- G06F 3/016: {Input arrangements with force or tactile feedback as computer generated output to the user}
- 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)}
- G06F 3/0346: with detection of the device orientation or free movement in a three-dimensional [3D] space, e.g. 3D mice, 6-DOF [six degrees of freedom] pointers using gyroscopes, accelerometers or tilt-sensors
- G06N 3/006: based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
- G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks
- G06N 20/00: Machine learning
- A61B 2562/0219: Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
- A61H 2003/007: {secured to the patient, e.g. with belts}
- A61H 2201/10: with further special therapeutic means, e.g. electrotherapy, magneto therapy or radiation therapy, chromo therapy, infrared or ultraviolet therapy
- A61H 2201/1238: with hydraulic or pneumatic drive
- A61H 2201/164: Feet or leg, e.g. pedal
- A61H 2201/165: Wearable interfaces
- A61H 2201/5007: computer controlled
- A61H 2201/5058: Sensors or detectors
- A61H 2201/5071: Pressure sensors
- A61H 2230/605: used as a control parameter for the apparatus
- A61H: PHYSICAL THERAPY APPARATUS, e.g. DEVICES FOR LOCATING OR STIMULATING REFLEX POINTS IN THE BODY; ARTIFICIAL RESPIRATION; MASSAGE; BATHING DEVICES FOR SPECIAL THERAPEUTIC OR HYGIENIC PURPOSES OR SPECIFIC PARTS OF THE BODY (electrotherapy, magnetotherapy, radiation therapy or ultrasound therapy A61N)
- B25J: MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES ({manipulators specially adapted for use in surgery A61B34/70; manipulators used in cleaning hollow articles B08B9/04}; manipulators associated with rolling mills B21B39/20; manipulators associated with forging machines B21J13/10; {manipulators associated with picking-up and placing mechanisms B23P19/007}; means for holding wheels or parts thereof B60B30/00; {vehicles with ground-engaging propulsion means, e.g. walking members B62D57/02, B62D57/032; devices for picking-up and depositing articles or materials between conveyors B65G47/90, B65G47/91; manipulators with gripping or holding means for transferring packages B65H67/065}; cranes B66C; {manipulators used in the protection or supervision of pipe-line installations F17D5/00; walking equipment adapted for nuclear steam-generators F22B37/006}; manipulators specially adapted for, or associated with, nuclear reactors G21C; {apparatus used for handling wafers during manufacture or treatment of semiconductor H10P72/50})
- A61B: DIAGNOSIS; SURGERY; IDENTIFICATION (usefulness limited to only animals A61D)
- A61N: ELECTROTHERAPY; MAGNETOTHERAPY; RADIATION THERAPY; ULTRASOUND THERAPY (measurement of bioelectric currents A61B; surgical instruments, devices or methods for transferring non-mechanical forms of energy to or from the body A61B18/00; anaesthetic apparatus in general A61M; incandescent lamps H01K; infrared radiators for heating H05B)
- G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N)
- G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS

## Abstract

A mobility augmentation system monitors a user's motor intent data and augments the user's mobility based on the monitored motor intent data. A machine-learned model is trained to identify an intended movement based on the monitored motor intent data. The machine-learned model may be trained based on generalized or specific motor intent data (e.g., user-specific motor intent data). A machine-learned model initially trained on generalized motor intent data may be re-trained on user-specific motor intent data such that the machine-learned model is optimized to the movements of the user. The system uses the machine-learned model to identify a difference between the user's monitored movement and target movement signals. Based on the identified difference, the system determines actuation signals to augment the user's movement. The actuation signals determined can be an adjustment to a currently applied actuation such that the system optimizes the actuation strategy during application.

## Claims

### Claim 1

A method comprising:
applying a plurality of actuation signals produced using a machine-learned movement model and based on an intended movement of a user to one or more augmentation devices worn by the user, the application of the plurality of actuation signals causing an actuated movement of the user, the actuated movement and the intended movement corresponding to a same movement;
computing a difference between feedback signals measuring the actuated movement and a plurality of target movement signals representative of the intended movement;
updating the plurality of actuation signals based on the computed signal difference; and
applying the updated actuation signals to the one or more augmentation devices worn by the user, the application of the updated actuation signals configured to minimize the signal difference and generate a personalized actuation strategy for the intended movement and the user.

### Claim 2 (depends on 1)

The method of claim 1, further comprising determining the intended movement of the user based on motor intent data received from the user, wherein the motor intent data comprises one or more of electromyography (EMG) data or inertial measurement unit (IMU) data.

### Claim 3 (depends on 1)

The method of claim 1, further comprising:
monitoring a plurality of movement result signals representative of augmented movement of the user, the application of the plurality of actuation signals to the one or more augmentation devices contributing to the augmented movement;
identifying, using the machine-learned model, a subsequent difference between the plurality of movement result signals and the plurality of target movement signals representative of intended movement of the user;
modifying the plurality of actuation signals based on the subsequent difference; and
applying the plurality of actuation signals to the one or more augmentation devices worn by the user.

### Claim 4 (depends on 1)

The method of claim 1, further comprising:
collecting neurotypical motor intent data of one or more users from a database, the neurotypical motor intent data corresponding to an activity performed by the one or more users; and
creating a movement template representative of a sequence of muscle activity events corresponding to the activity based on the collected neurotypical motor intent data.

### Claim 5 (depends on 4)

The method of claim 4, wherein computing a difference between feedback signals measuring the actuated movement and the plurality of target movement signals representative of the intended movement comprises:
determining a movement prediction indicating that the plurality of feedback signals corresponds to the activity using the machine-learned model;
selecting the movement template based on the movement prediction, wherein the sequence of muscle activity events of the movement template is associated with the plurality of target movement signals; and
computing the difference between the feedback signals and the plurality of target movement signals using the selected movement template.

### Claim 6 (depends on 5)

The method of claim 5, further comprising determining a plurality of actuation signals based on the computed difference by identifying a trigger associated with an actuation strategy based on one or more muscle activity events in the sequence of muscle activity events of the movement template, wherein the actuation strategy comprises an actuation signal of the plurality of actuation signals, an augmentation device of the one or more augmentation devices to which the actuation signal is applied, and a time to apply the actuation signal.

### Claim 7 (depends on 5)

The method of claim 5, wherein the plurality of target movement signals comprises one or more of kinematic signals, foot plantar pressure signals, or kinetic signals.

### Claim 8 (depends on 1)

The method of claim 1, further comprising:
collecting neurotypical motor intent data from a coaching user, the neurotypical motor intent data corresponding to an activity performed by the coaching user and monitored using a plurality of sensors worn by the coaching user at one or more locations on the coaching user; and
determining the plurality of target movement signals representative of the intended movement based on the collected neurotypical motor intent data, the plurality of target movement signals characterizing the activity.

### Claim 9 (depends on 1)

The method of claim 1, wherein the plurality of actuation signals are applied to a muscle group of the user.

### Claim 10 (depends on 1)

The method of claim 1, further comprising:
collecting neurotypical motor intent data from the user using a first augmentation device of the one or more augmentation devices worn by the user at a first location on the user, the neurotypical motor intent data corresponding to an activity performed by the user; and
determining a plurality of target movement signals representative of the intended movement based on the collected neurotypical motor intent data, the plurality of target movement signals characterizing the activity;
wherein applying the plurality of actuation signals comprises applying the plurality of actuation signals to a second augmentation device of the one or more augmentation devices at a second location on the user, the first and second locations mirrored across a sagittal plane of the body of the user.

### Claim 11 (depends on 1)

The method of claim 1, further comprising:
determining intent labels based on one or more of foot plantar pressure signals, kinematic signals, and kinetic signals of one or more users from a database;
collecting motor intent data of the one or more users from a database;
preprocessing the collected motor intent data; and
training the machine-learned model using the determined intent labels and the preprocessed motor intent data.

### Claim 12 (depends on 11)

The method of claim 11, wherein preprocessing the collected motor intent data comprises, for each user of the one or more users:
aligning the motor intent data of the respective user in a temporal domain;
processing the motor intent data using one or more signal processing techniques selected from filtering, averaging, peak-finding, and down-sampling; and
determining kinematic data associated with the processed motor intent data using a biomechanical model.

### Claim 13 (depends on 1)

The method of claim 1, wherein the plurality of actuation signals comprises one or more of electrical actuation signals, haptic actuation signals, visible signals output from a display of a user device, audio signals, or mechanical actuation signals.

### Claim 14 (depends on 13)

The method of claim 13, wherein the electrical actuation signals are characterized by a first frequency, a first pulse duration, and a first amplitude for a first period of time and a second frequency, a second pulse duration, and a second amplitude for a second period of time following the first period of time.

### Claim 15 (depends on 1)

The method of claim 1, wherein applying the plurality of actuation signals comprises actuating an exoskeleton using the plurality of actuation signals.

### Claim 16 (depends on 1)

The method of claim 1, wherein the one or more augmentation devices comprise an exoskeleton, a plurality of modular electrode straps, or a foot pressure bed.

### Claim 17 (depends on 1)

The method of claim 1, wherein applying the plurality of actuation signals to the one or more augmentation devices comprises determining a plurality of times to apply the respective plurality of actuation signals based on the intended movement.

### Claim 18 (depends on 17)

The method of claim 17, further comprising:
monitoring a plurality of movement result signals representative of augmented movement of the user, the application of the plurality of actuation signals to the one or more augmentation devices contributing to the augmented movement;
identifying, using the machine-learned model, a subsequent difference between the plurality of movement result signals and the plurality of target movement signals representative of the intended movement of the user;
modifying the plurality of times based on the subsequent difference; and
applying the actuation signals to the one or more augmentation devices worn by the user at the modified plurality of times.

### Claim 19

A mobility improvement system comprising a non-transitory computer-readable storage medium storing instructions for execution and a hardware processor configured to execute the instructions, the instructions, when executed, cause the hardware processor to perform steps comprising:
applying a plurality of actuation signals produced using a machine-learned movement model and based on an intended movement of a user to one or more augmentation devices worn by the user, the application of the plurality of actuation signals causing an actuated movement of the user, the actuated movement and the intended movement corresponding to a same movement;
computing a difference between feedback signals measuring the actuated movement and a plurality of target movement signal representative of the intended movement;
updating the plurality of actuation signals based on the computed signal difference; and
applying the updated actuation signals to the one or more augmentation devices worn by the user, the application of the updated actuation signals configured to minimize the signal difference and generate a personalized actuation strategy for the intended movement and the user.

### Claim 20

A method comprising:
determining a plurality of target movement signals based on a movement goal;
monitoring a plurality of movement signals representative of movement of a user, the movement signals determined using sensor data from sensors located at first and second locations on a user;
identifying, based on the movement signals, kinematic patterns associated with the first location on the user;
determining a plurality of actuation signals based on the identified kinematic patterns; and
applying the plurality of actuation signals to a second augmentation device worn by the user at the second location on the user, wherein the plurality of actuation signals are not applied to a first augmentation device worn by the user at the first location on the user.