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    Methods and systems for multimodal hand state prediction
    2.
    发明授权
    Methods and systems for multimodal hand state prediction 有权

    公开(公告)号：US11782522B1

    公开(公告)日：2023-10-10

    申请号：US17704970

    申请日：2022-03-25

    申请人： Roya Cody ,  Da Yuan Huang ,  Xiang Cao ,  Wei Li

    发明人： Roya Cody ,  Da Yuan Huang ,  Xiang Cao ,  Wei Li

    IPC分类号： G06F3/0346 ,  G06F3/0354 ,  G06F3/044 ,  G06F3/01

    CPC分类号： G06F3/0346 ,  G06F3/017 ,  G06F3/03545 ,  G06F3/044

    摘要： In various examples, the present disclosure describes methods and systems for generating hand state predictions. A hand state prediction system includes a machine learning-based model, such as a neural network model, that is trained to convert inertial motion measurements and surface contact data into predictions of a corresponding hand gesture or gripping posture. For each window of data sampled, motion data and contact data are obtained, processed and fused to generate a fused prediction. The hand state prediction system can operate in continuous mode to automatically detect a start and an end of a hand action, or a user can designate a start and an end of a hand action. A hand state prediction is generated by a multimodal classifier by processing the fused prediction. Instructions represented by a hand action can be acted upon through a command action performed by a computing device or a computer application.

    METHODS AND SYSTEMS FOR MULTIMODAL HAND STATE PREDICTION
    3.
    发明公开
    METHODS AND SYSTEMS FOR MULTIMODAL HAND STATE PREDICTION 审中-公开

    公开(公告)号：US20230305644A1

    公开(公告)日：2023-09-28

    申请号：US17704970

    申请日：2022-03-25

    申请人： Roya CODY ,  Da Yuan HUANG ,  Xiang CAO ,  Wei LI

    发明人： Roya CODY ,  Da Yuan HUANG ,  Xiang CAO ,  Wei LI

    IPC分类号： G06F3/0354 ,  G06F3/0346 ,  G06F3/044

    CPC分类号： G06F3/03545 ,  G06F3/0346 ,  G06F3/044

    摘要： In various examples, the present disclosure describes methods and systems for generating hand state predictions. A hand state prediction system includes a machine learning-based model, such as a neural network model, that is trained to convert inertial motion measurements and surface contact data into predictions of a corresponding hand gesture or gripping posture. For each window of data sampled, motion data and contact data are obtained, processed and fused to generate a fused prediction. The hand state prediction system can operate in continuous mode to automatically detect a start and an end of a hand action, or a user can designate a start and an end of a hand action. A hand state prediction is generated by a multimodal classifier by processing the fused prediction. Instructions represented by a hand action can be acted upon through a command action performed by a computing device or a computer application.