Estimating Tidal Volume Using Mobile Devices

    公开(公告)号:US20240423498A1

    公开(公告)日:2024-12-26

    申请号:US18752405

    申请日:2024-06-24

    Abstract: In one embodiment, a method includes detecting, by a motion sensor of a mobile device worn by a user, multiple motion signals, each representing a motion of the user about one of a number of mobile-device axes defined by an orientation of the mobile device. The method further includes determining, for each of the multiple mobile-device axes, a ballistocardiogram (BCG) signal based on the motion signal corresponding to that mobile-device axis; selecting, based on a strength of the determined BCG signals, one or more particular mobile-device axes and corresponding motion signals for estimating a user's tidal volume; determining, based on the one or more selected motion signals, one or more breathing features; and estimating, by providing the one or more breathing features to a trained machine-learning model, the user's current tidal volume.

    Classifying User Activity Using Multiple Sensors

    公开(公告)号:US20240197246A1

    公开(公告)日:2024-06-20

    申请号:US18118528

    申请日:2023-03-07

    CPC classification number: A61B5/6802 A61B5/1118 A61B5/7264

    Abstract: In one embodiment, a method includes accessing first sensor data from a first sensor worn on a first portion of a user's body and accessing second sensor data from a second sensor worn on a second portion of the user's body. The method includes determining, based on both the first sensor data and the second sensor data, one or more first features related to the user's activity and determining, based on the first features, an initial classification of the user's activity. When the initial classification indicates a class that includes one or more subclasses that are more distinguishable by one of the sensors, then a specific subclassification may be determined based on sensor data from only that one sensor. Otherwise, the classification of the user's activity may be based on the one or more first features that use data from both sensors.

    DEVICE-INVARIANT, FREQUENCY-DOMAIN SIGNAL PROCESSING WITH MACHINE LEARNING

    公开(公告)号:US20220269958A1

    公开(公告)日:2022-08-25

    申请号:US17245951

    申请日:2021-04-30

    Abstract: Device-invariant, frequency-domain signal processing with machine learning includes retrieving with a host device a device-specific alien dataset corresponding to an alien device. The device-specific alien dataset is retrieved from a remote data storage device communicatively coupled with the host device. A plurality of frequency-domain features are extracted from the device-specific alien dataset and a machine learning model is trained using the plurality of frequency-domain features. The host device extracts frequency-domain features from signals generated by sensors operatively coupled with the host device. Real-time frequency bin adaptation of the frequency-domain features extracted by the host device is performed. Based on the frequency-domain features extracted by the host device, as adapted, an inference is performed using the machine learning model.

    NON-INVASIVE CONTINUOUS HEART RHYTHM MONITORING BASED ON WEARABLE SENSORS

    公开(公告)号:US20220183608A1

    公开(公告)日:2022-06-16

    申请号:US17124438

    申请日:2020-12-16

    Abstract: Continuously monitoring heart rhythm can include grouping, using computer hardware, a plurality of inter-beat intervals (IBI) data for a user into a plurality of epochs, wherein each epoch includes a subset of the IBI data corresponding to a predetermined time span. For each epoch, a selected feature set selected from a plurality of feature sets is extracted based on a determination of temporal consistency of the epoch. A plurality of epoch classifications may be generated for the epochs using a selected feature processor, wherein each epoch classification indicates whether arrhythmia is detected for the epoch from which the epoch classification is generated. The selected feature processor is selected from a plurality of different feature processors on a per-epoch basis based on the selected feature set extracted from the epoch. An indication of arrhythmia may be output, via an output device of the computer hardware, based on the epoch classifications.

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