Calibration of multiple sensor modules related to an orientation of a user of the sensor modules

    公开(公告)号:US10809797B1

    公开(公告)日:2020-10-20

    申请号:US16534674

    申请日:2019-08-07

    Abstract: A system including a computing device receiving a first indication, the first indication indicating that a plurality of sensor modules are positioned in placeholders in a container device, wherein the placeholders are configured to hold the plurality of sensor modules at pre-determined positions and orientations in the container device. In response to the first indication, the computing device calibrates orientation measurements of the plurality or sensor modules relative to a first common reference system based on the pre-determined positions and orientations in the container device. The computing device then receives a second indication, the second indication indicating that the sensor modules are positioned on parts of a user; and in response to the second indication, the computing device calibrates the plurality of sensor modules relative to user's reference system aligned with the orientation of the user based on a specific orientation relation between the arm sensor module and the orientation of the user, wherein the arm sensor module is one of the plurality of sensor modules.

    Tracking User Movements to Control a Skeleton Model in a Computer System

    公开(公告)号:US20190339766A1

    公开(公告)日:2019-11-07

    申请号:US15973137

    申请日:2018-05-07

    Abstract: A system having sensor modules and a computing device. Each sensor module has an inertial measurement unit attached to a portion of a user to generate motion data identifying a sequence of orientations of the portion. The computing device provides the sequences of orientations measured by the sensor modules as input to an artificial neural network, obtains as output from the artificial neural network a predicted orientation measurement of a part of the user, and controls an application by setting an orientation of a rigid part of a skeleton model of the user according to the predicted orientation measurement. The artificial neural network can be trained to predict orientations measured using an optical tracking system based on orientations measured using inertial measurement units and/or to prediction orientation measurements of some rigid parts in a kinematic chain based on orientation measurements of other rigid parts in the kinematic chain.

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