Facebook Technologies, LLC

Meta Platforms Technologies, LLC

Ctrl-labs Corporation

Alexandre Barachant

Patrick Kaifosh

Daniel Z Wetmore

Tudor Giurgica-Tiron

Adam Al-natsheh

Steven Kober

Adam L Berenzweig

Ning Guo

Jonathan Reid

Zhuo Wang

Jason Reisman

Christopher Anderson

Jeffrey Scott Seely

Christopher Osborn

Nitzan Bartov

Robert Cochran

Naor Brown

Joshua Duyan

Ran Rubin

Nadine Hussami

Michael Isaac Mandel

Methods and systems used in calibrating the position and/or orientation of a wearable device configured to be worn on a wrist or forearm of a user, the method comprises sensing a plurality of neuromuscular signals from the user using a plurality of sensors arranged on the wearable device, and providing the plurality of neuromuscular signals and/or signals derived from the plurality of neuromuscular signals as inputs to one or more trained autocalibration models, determining based, at least in part, on the output of the one or more trained autocalibration models, a current position and/or orientation of the wearable device on the user, and generating a control signal based, at least in part, on the current position and/or orientation of the wearable device on the user and the plurality of neuromuscular signals.

Methods and apparatus for autocalibration of a wearable electrode sensor system

The disclosed computer-implemented method may include presenting, via a user interface, a sensory cue, and receiving, from neuromuscular sensors of a wearable device, various neuromuscular signals generated by a user wearing the wearable device, where the user generates the neuromuscular signals in response to the sensory cue being presented to the user via the user interface. The method may also include interpreting the received neuromuscular signals as input commands with respect to the sensory cue provided by the user interface, such that the input commands initiate performance of specified tasks within the user interface. The method may also include performing the specified tasks within the user interface according to the interpreted input commands. Various other methods, systems, and computer-readable media are also disclosed.

Systems, methods, and interfaces for performing inputs based on neuromuscular control

Example systems may include a head-mounted device configured to present an artificial reality view to a user, a control device including a plurality of electromyography (EMG) sensors, and at least one physical processor programmed to receive EMG data based on signals detected by the EMG sensors, detect EMG signals corresponding to user gestures within the EMG data, classify the EMG signals to identify gesture types, and provide control signals based on the gesture types, wherein the control signal triggers the head-mounted device to modify the artificial reality view. Various other methods, systems, and computer-readable media are also disclosed.

Methods and apparatus for gesture detection and classification

The disclosed method may include receiving neuromuscular activity data over a first time series from a first sensor on a wearable device donned by a user receiving ground truth data over a second time series from a second sensor that indicates a body part state of a body part of the user, generating one or more training datasets by time-shifting at least a portion of the neuromuscular activity data over the first time series relative to the second time series, to associate the neuromuscular activity data with at least a portion of the ground truth data, and training one or more inferential models based on the one or more training datasets. Various other related methods and systems are also disclosed.

Methods and apparatuses for low latency body state prediction based on neuromuscular data

Methods and apparatus for substantially real-time detection of spike events in neuromuscular data. The method comprises receiving a plurality of neuromuscular signals from a plurality of neuromuscular sensors arranged on one or more wearable devices worn by a user, detecting, based on the plurality of neuromuscular signals or information derived from the plurality of neuromuscular signals, at least one spike event corresponding to firing of an action potential in at least one motor unit, determining, based on the plurality of neuromuscular signals or the information derived from the plurality of neuromuscular signals, a biological source of the detected at least one spike event, and generating at least one output based, at least in part, on the detected at least one spike event and/or the determined biological source of the detected at least one spike event.

Real-time spike detection and identification

Methods and apparatus for calibrating performance of one or more statistical models used to generate a musculoskeletal representation. The method comprises controlling presentation of instructions via a user interface to instruct the user to perform the at least one gesture and updating at least one parameter of the one or more statistical models based, at least in part on a plurality of neuromuscular signals recorded by a plurality of neuromuscular sensors during performance of the at least one gesture by the user.

Calibration techniques for handstate representation modeling using neuromuscular signals

Techniques for shielding wearable surface electromyography (sEMG) devices are described. According to some aspects, an sEMG device may comprise amplification circuitry comprising at least a first differential amplifier and at least two sEMG electrodes electrically connected to the amplification circuitry. The device may further comprise at least one auxiliary conductor not electrically connected to the amplification circuitry, wherein the at least one auxiliary conductor is configured to be electrically coupled to a wearer of the wearable device, and an electromagnetic shield surrounding the wearable device at least in part and electrically connected to the at least one auxiliary conductor.

Shielding techniques for noise reduction in surface electromyography signal measurement and related systems and methods

Methods and apparatus for authenticating a user based on neuromuscular signals. The method comprises recording, using a plurality of neuromuscular sensors arranged on one or more wearable devices, a plurality of neuromuscular signals from a user; deriving a neuromuscular signature for the user from the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals, wherein the neuromuscular signature is indicative of at least one personal characteristic of the user detected in the plurality of neuromuscular signals; and authenticating the user based on the derived neuromuscular signature.

User identification and authentication with neuromuscular signatures

Methods and apparatus for anonymizing neuromuscular signals used to generate a musculoskeletal representation. The method comprises recording, using a plurality of neuromuscular sensors arranged on one or more wearable devices, a plurality of neuromuscular signals from a user, providing as input to a trained statistical model, the plurality of neuromuscular signals and/or information based on the plurality of neuromuscular signals; and generating, the musculoskeletal representation based, at least in part, on an output of the trained statistical model, wherein the musculoskeletal representation is an anonymized musculoskeletal representation from which at least one personal characteristic of the user has been removed.

Techniques for anonymizing neuromuscular signal data

Methods and apparatus for training a classification model and using the trained classification model to recognize gestures performed by a user. An apparatus comprises a processor that is programmed to: receive, via a plurality of neuromuscular sensors, a first plurality of neuromuscular signals from a user as the user performs a first single act of a gesture; train a classification model based on the first plurality of neuromuscular signals, the training including: deriving value(s) from the first plurality of neuromuscular signals, the value(s) indicative of distinctive features of the gesture including at least one feature that linearly varies with a force applied during performance of the gesture; and generating a first categorical representation of the gesture in the classification model based on the value(s); and determine that the user performed a second single act of the gesture, based on the trained classification model and a second plurality of neuromuscular signals.

Methods and apparatus for unsupervised one-shot machine learning for classification of human gestures and estimation of applied forces

Methods and apparatus for generating a control signal based on sub-muscular activation. Information for a first sub-muscular control channel of a plurality of sub-muscular control channels is derived from the plurality of neuromuscular signals. Each of the plurality of sub-muscular control channels is configured to process information associated with activation of one or more sub-muscular structures. A control signal is generated based on the derived information for the first sub-muscular control channel and the control signal is provided to a control interface to control an operation of a device.

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Brooklyn, NY, United States of America

Alexandre Barachant