Motion predictions of overlapping kinematic chains of a skeleton model used to control a computer system
First Claim
1. A system, comprising:
- a plurality of sensor modules, each respective sensor module in the plurality of sensor modules having an inertial measurement unit and attached to a portion of a user to generate motion data identifying a sequence of orientations of the portion of the user, the plurality of sensor modules including a first subset and a second subset that share a common sensor module between the first subset and the second subset;
a computing device coupled to the plurality of sensor modules and configured to;
provide orientation measurements generated by the first subset as input to a first artificial neural network;
obtain, as output from the first artificial neural network, at least one first orientation measurement of a common part of the user on which the common sensor module is attached;
provide orientation measurements generated by the second subset as input to a second artificial neural network;
obtain, as output from the second artificial neural network, at least one second orientation measurement of the common part; and
generate a predicted orientation measurement of the common part from combining the at least one first orientation measurement of the common part and the at least one second orientation measurement of the common part.
2 Assignments
0 Petitions
Accused Products
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 sensor modules include a first subset and a second subset that share a common sensor module. The computing device provides orientation measurements generated by the first subset as input to a first artificial neural network to obtain at least one first orientation measurement of the common module, provides orientation measurements generated by the second subset as input to a second artificial neural network to obtain at least one second orientation measurement of the common module, and generates, a predicted orientation measurement of the common module by combining the at least one first orientation measurement of the common module and the at least one second orientation measurement of the common module.
-
Citations
20 Claims
-
1. A system, comprising:
-
a plurality of sensor modules, each respective sensor module in the plurality of sensor modules having an inertial measurement unit and attached to a portion of a user to generate motion data identifying a sequence of orientations of the portion of the user, the plurality of sensor modules including a first subset and a second subset that share a common sensor module between the first subset and the second subset; a computing device coupled to the plurality of sensor modules and configured to; provide orientation measurements generated by the first subset as input to a first artificial neural network; obtain, as output from the first artificial neural network, at least one first orientation measurement of a common part of the user on which the common sensor module is attached; provide orientation measurements generated by the second subset as input to a second artificial neural network; obtain, as output from the second artificial neural network, at least one second orientation measurement of the common part; and generate a predicted orientation measurement of the common part from combining the at least one first orientation measurement of the common part and the at least one second orientation measurement of the common part. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11)
-
-
12. A method, comprising:
-
receiving, from a plurality of inertial measurement units attached to portions of a user connected by joints, motion data identifying sequences of orientations of the portions of the user, the plurality of inertial measurement units including a first subset and a second subset that share a common inertial measurement unit between the first subset and the second subset; providing orientation measurements generated by the first subset as input to a first artificial neural network; obtaining, as output from the first artificial neural network, at least one first orientation measurement of the common part of the user on which the common inertial measurement unit is attached; providing orientation measurements generated by the second subset as input to a second artificial neural network; obtaining, as output from the second artificial neural network, at least one second orientation measurement of the common part; and generating, a predicted orientation measurement of the common part from combining the at least one first orientation measurement of the common part and the at least one second orientation measurement of the common part. - View Dependent Claims (13, 14, 15)
-
-
16. A method, comprising:
-
attaching a plurality of sensor modules to a person, including a first subset of the sensor modules to track orientations of a first kinematic chain of the person and a second subset of the sensor modules to track orientations of a second kinematic chain of the person, wherein the first kinematic chain and the second kinematic chain have a common part of the person; measuring, using the sensor modules, a plurality of sequences of orientations of the sensor modules during the person performing a plurality of sequence of motions; measuring, independent of measurements of the sensor modules and using a separate tracking system, orientations of the sensor modules during the person performing a plurality of sequence of motions; training a first artificial neural network using a supervised machine learning technique to predict orientation measurements of the first kinematic chain from the separate tracking system using orientation measurements from the first subset; training a second artificial neural network using the supervised machine learning technique to predict orientation measurements of the second kinematic chain from the separate tracking system using orientation measurements from the second subset; and training a third artificial neural network using the supervised machine learning technique to predict orientation measurements of the common part of the person from first predicted orientation measurements of the common part of the person generated from the first artificial neural network and second predicted orientation measurements of the common part of the person generated from the second artificial neural network. - View Dependent Claims (17, 18, 19, 20)
-
Specification