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    MACHINE-LEARNING BASED GESTURE RECOGNITION WITH FRAMEWORK FOR ADDING USER-CUSTOMIZED GESTURES
    1.
    发明申请
    MACHINE-LEARNING BASED GESTURE RECOGNITION WITH FRAMEWORK FOR ADDING USER-CUSTOMIZED GESTURES 有权

    公开(公告)号：US20220391697A1

    公开(公告)日：2022-12-08

    申请号：US17740291

    申请日：2022-05-09

    申请人： Apple Inc.

    发明人： Hojjat Seyed Mousavi ,  Behrooz Shahsavari ,  Nima Ferdosi ,  Charles Maalouf ,  Xuhai Xu

    IPC分类号： G06N3/08 ,  G06F1/16 ,  G06F3/01 ,  G06F3/0346

    摘要： Embodiments are disclosed for a machine learning (ML) gesture recognition with a framework for adding user-customized gestures. In an embodiment, a method comprises: receiving sensor data indicative of a gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn on a limb of the user; generating a current encoding of features extracted from the sensor data using a machine learning model with the features as input; generating similarity metrics between the current encoding and each encoding in a set of previously generated encodings for gestures; generating similarity scores based on the similarity metrics; predicting the gesture made by the user based on the similarity scores; and performing an action on the wearable device or other device based on the predicted gesture.

    HOLD GESTURE RECOGNITION USING MACHINE LEARNING
    9.
    发明公开
    HOLD GESTURE RECOGNITION USING MACHINE LEARNING 审中-公开

    公开(公告)号：US20240103633A1

    公开(公告)日：2024-03-28

    申请号：US18370837

    申请日：2023-09-20

    申请人： Apple Inc.

    发明人： Bongsoo Suh ,  Behrooz Shahsavari ,  Charles Maalouf ,  Hojjat Seyed Mousavi ,  Laurence Lindsey ,  Shivam Kumar Gupta

    IPC分类号： G06F3/01 ,  G06N3/0464

    CPC分类号： G06F3/017 ,  G06N3/0464

    摘要： Embodiments are disclosed for hold gesture recognition using machine learning (ML). In an embodiment, a method comprises: receiving sensor signals indicative of a hand gesture made by a user, the sensor data obtained from at least one sensor of a wearable device worn by the user; generating a first embedding of first features extracted from the sensor signals; predicting a first part of a hold gesture based on a first ML gesture classifier and the first embedding; generating a second embedding of second features extracted from the sensor signals; predicting a second part of the hold gesture based on a second ML gesture classifier and the second embedding; predicting a hold gesture based at least in part on outputs of the first and second ML gesture classifiers and a prediction policy; and performing an action on the wearable device or other device based on the predicted hold gesture.