Identification of watch bands
    1.
    发明授权

    公开(公告)号:US12204292B1

    公开(公告)日:2025-01-21

    申请号:US17200654

    申请日:2021-03-12

    Applicant: Apple Inc.

    Abstract: A watch can include a watch body and a band for securing the watch to the user. The watch body can detect an identification of the band or combination of band portions, which can serve as an input to initiate actions performed by the watch body. For example, a type, model, color, size, or other characteristic of a band can be determined and used to select a corresponding action performed by the watch body. Identification of the band can be performed by components of the watch body that also serve other purposes. The watch body can respond to the identification of a particular band by performing particular functions, such as changing an aspect of a user interface or altering settings of the watch body.

    Touch sensor pattern for edge input detection

    公开(公告)号:US10671222B2

    公开(公告)日:2020-06-02

    申请号:US14870905

    申请日:2015-09-30

    Applicant: Apple Inc.

    Abstract: An apparatus is disclosed. In some examples, the apparatus comprises a cover substrate having a front surface, a first edge and a first cavity adjacent to the first edge. In some examples, the apparatus comprises a plurality of touch sensor electrodes disposed opposite the front surface of the cover substrate. In some examples, the apparatus comprises at least one touch sensor edge electrode disposed within the first cavity on a surface that is angled relative to the front surface of the cover substrate. In some examples, at least one touch sensor edge electrode is disposed on an outward facing curved surface of the first cavity. In some examples, the plurality of touch sensor electrodes are formed from a first conductive material and the at least one touch sensor edge electrode is formed from a second conductive material. In some examples, the first conductive material is transparent, and the second conductive material is non-transparent. In some examples, the second conductive material is formed on a black mask layer disposed around a perimeter of a bottom surface of the cover substrate.

    Touch scan modes during device charging

    公开(公告)号:US10203803B2

    公开(公告)日:2019-02-12

    申请号:US14475293

    申请日:2014-09-02

    Applicant: Apple Inc.

    Abstract: Touch scan modes for touch sensitive devices during device charge is disclosed. To prevent adverse effects to the touch sensor panel due to inductive noise while the device is charging, the touch controller can switch to a touch scan mode (i.e., power charging touch mode) that can cancel or reduce noise from the touch scan or touch image. Power charging touch modes can include low noise frequency selection, increased number of touch samples employed to calculate an average, and simultaneous sampling. In some examples, a power charging touch mode can be different from a normal touch scan mode (i.e., a touch scan mode when the device is not charging). With the one or more power charging touch modes, false touch readings, erroneous touch location identification, and/or undetected touches can be eliminated or reduced.

    FULL BODY POSE ESTIMATION THROUGH FEATURE EXTRACTION FROM MULTIPLE WEARABLE DEVICES

    公开(公告)号:US20230101617A1

    公开(公告)日:2023-03-30

    申请号:US17951943

    申请日:2022-09-23

    Applicant: Apple Inc.

    Abstract: Embodiments are disclosed for full body pose estimation using features extracted from multiple wearable devices. In an embodiment, a method comprises: obtaining point of view (POV) video data and inertial sensor data from multiple wearable devices worn at the same time by a user; obtaining depth data capturing the user's full body; extracting two-dimensional (2D) keypoints from the POV video data; reconstructing a full body 2D skeletal model from the 2D keypoints; generating a three-dimensional (3D) mesh model of the user's full body based on the depth data; merging nodes of the 3D mesh model with the inertial sensor data; aligning respective orientations of the 2D skeletal model and the 3D mesh model in a common reference frame; and predicting, using a machine learning model, classification types based on the aligned 2D skeletal model and 3D mesh model.

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