MACHINE LEARNING CONFIGURATIONS MODELED USING CONTEXTUAL CATEGORICAL LABELS FOR BIOSIGNALS

    公开(公告)号:US20240012480A1

    公开(公告)日:2024-01-11

    申请号:US18355659

    申请日:2023-07-20

    Applicant: Apple Inc.

    CPC classification number: G06F3/015 G06N3/04 G06N20/00

    Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.

    MACHINE LEARNING CONFIGURATIONS MODELED USING CONTEXTUAL CATEGORICAL LABELS FOR BIOSIGNALS

    公开(公告)号:US20210286429A1

    公开(公告)日:2021-09-16

    申请号:US17174875

    申请日:2021-02-12

    Applicant: Apple Inc.

    Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.

    ECG SIGNAL RECONSTRUCTION FROM EEG SIGNAL

    公开(公告)号:US20250090075A1

    公开(公告)日:2025-03-20

    申请号:US18884061

    申请日:2024-09-12

    Applicant: Apple Inc.

    Abstract: Embodiments are disclosed for ECG signal reconstruction from an EEG signal. In some embodiments, a method comprises: determining, with at least one processor, heartbeat timestamps from a heartrate signal; obtaining an electroencephalography (EEG) signal from an EEG sensor; aligning, with the at least one processor, the EEG signal with the heartbeat timestamps; collaborative filtering, with the at least one processor, the beat-aligned EEG signal; and determining, with the at least one processor, an approximation of an electrocardiography (ECG) signal based on the filtered beat-aligned EEG signal.

    Machine learning configurations modeled using contextual categorical labels for biosignals

    公开(公告)号:US11747902B2

    公开(公告)日:2023-09-05

    申请号:US17174875

    申请日:2021-02-12

    Applicant: Apple Inc.

    CPC classification number: G06F3/015 G06N3/04 G06N20/00

    Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.

    Machine learning configurations modeled using contextual categorical labels for biosignals

    公开(公告)号:US12135837B2

    公开(公告)日:2024-11-05

    申请号:US18355659

    申请日:2023-07-20

    Applicant: Apple Inc.

    Abstract: Techniques are disclosed for defining a training data set to include biosignals and categorical labels representative of a context. For example, a categorical label may indicate whether a user was performing a difficult or easy mental task while the biosignal was being recorded. A set of first layers in a neural network can be trained using a portion of the training data set associated with a first set of users and at least one second layer can be trained using a portion of the training data set associated with a particular other user. The neural network can then be used to process other biosignals from the particular other user to generate predicted categorical context labels.

    Electrodes for gesture recognition

    公开(公告)号:US12056285B2

    公开(公告)日:2024-08-06

    申请号:US17823870

    申请日:2022-08-31

    Applicant: Apple Inc.

    CPC classification number: G06F3/017 A61B5/279 A61B5/681

    Abstract: Electrodes that can be formed in a flexible band of a wrist-worn device to detect hand gestures are disclosed. Multiple rows of electrodes can be configured to detect electromyography (EMG) signals produced by activity of muscles and tendons. The band can include removable electrical connections (e.g., pogo pins) to enable the electrode signals to be routed to processing circuitry in the housing of the wrist-worn device. Measurements between signals from the active electrodes and one or more reference electrodes can be obtained to capture EMG signals at a number of locations on the band. The measurement method and mode of operation (lower power coarse detection or higher power fine detection) can determine the location and number of electrodes to be measured. These EMG signals can be processed to identify hand movements and recognize gestures associated with those hand movements.

    ADAPTIVE WORKOUT PLAN CREATION AND PERSONALIZED FITNESS COACHING BASED ON BIOSIGNALS

    公开(公告)号:US20240058650A1

    公开(公告)日:2024-02-22

    申请号:US18451798

    申请日:2023-08-17

    Applicant: Apple Inc.

    Abstract: Methods, systems and/or computer-implemented instructions are configured to perform or support actions that include: determining a time contribution for each of a set of workout effort zones for a user, wherein each of the set of workout effort zones corresponds to a range of values for a biosignal; determining a timeseries of workout target effort zones for the user based on the target time contributions for the set of workout effort zones; receiving, during a workout time period, real-time biosignal data from a sensor in a wearable electronic device being worn by the user; generating, during the workout time period, an audio, visual, or haptic stimulus based on the real-time biosignal data and a target effort zone in the time series of workout target effort zones; and outputting, during the workout time period, the audio, visual, or haptic stimulus.

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