Determining Respiration Rates Based On Audio Streams and User Conditions

    公开(公告)号:US20250134408A1

    公开(公告)日:2025-05-01

    申请号:US18901467

    申请日:2024-09-30

    Applicant: Apple Inc.

    Abstract: A system can receive an input indicating a user condition. The system can also receive an internal audio stream from an in-ear microphone and an external audio stream from an external microphone of a head worn system. The system can determine a respiration rate of a user based on the internal audio stream, the external audio stream, and the input indicating the user condition. In some implementations, the respiration rate may be determined from a respiration signal in the internal audio stream and/or the external audio stream. The respiration signal may measure breathing of the user. In some implementations, the system can invoke a machine learning model to determine the respiration signal from the internal audio stream and/or the external audio stream based on the user condition.

    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.

    Eye detection methods and devices

    公开(公告)号:US12282596B2

    公开(公告)日:2025-04-22

    申请号:US18403599

    申请日:2024-01-03

    Applicant: Apple Inc.

    Abstract: A head-mounted device having a plurality of electrodes configured to detect optical events such as the movement of one or more eyes or coarse eye gestures is disclosed. In some examples, the one or more electrodes can be coupled to dielectric elastomer materials whose shape can be changed to vary contact between a user of the head-mounted device and the one or more electrodes to ensure sufficient contact and electrode signal quality. In some examples, the one or more electrodes can be coupled to pressure sensors and control circuitry to monitor and adjust the applied pressure. In some examples, the optical events can be used as triggers for operating the device, including transitioning between operational power modes. In some examples, the triggers can invoke higher resolution sensing capabilities of the head-mounted device. In some examples, the electrodes can be used as an on-head detector to wake-up and/or unlock the device.

    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.

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