Apple inc. (20240103605). CONTINUOUS HAND POSE TRACKING WITH WRIST-WORN ANTENNA IMPEDANCE CHARACTERISTIC SENSING simplified abstract
Contents
- 1 CONTINUOUS HAND POSE TRACKING WITH WRIST-WORN ANTENNA IMPEDANCE CHARACTERISTIC SENSING
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 CONTINUOUS HAND POSE TRACKING WITH WRIST-WORN ANTENNA IMPEDANCE CHARACTERISTIC SENSING - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
CONTINUOUS HAND POSE TRACKING WITH WRIST-WORN ANTENNA IMPEDANCE CHARACTERISTIC SENSING
Organization Name
Inventor(s)
Istvan J. Szini of San Jose CA (US)
Chris Harrison of Pittsburgh PA (US)
CONTINUOUS HAND POSE TRACKING WITH WRIST-WORN ANTENNA IMPEDANCE CHARACTERISTIC SENSING - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240103605 titled 'CONTINUOUS HAND POSE TRACKING WITH WRIST-WORN ANTENNA IMPEDANCE CHARACTERISTIC SENSING
Simplified Explanation
The patent application describes a continuous hand pose tracking system using wrist-worn antennas to track hand poses in real-time through machine learning algorithms.
- The system employs at least one wrist-worn antenna to capture real-time dielectric loading variations caused by different hand poses.
- Sensor data is processed by a machine learning backend to generate a fully-posed 3D hand model that can be continuously tracked.
- Two degrees of freedom (2DOF) wrist angle and micro-gestures can be tracked using this system.
- The technology can be expanded to include multiple antennas operating at different self-resonances.
Potential Applications
The technology can be used in virtual reality and augmented reality applications for more accurate hand tracking, in sign language recognition systems, and in human-computer interaction interfaces.
Problems Solved
This technology solves the problem of accurately tracking hand poses in real-time without the need for complex camera setups or bulky sensors attached to the hand.
Benefits
The system offers a more natural and intuitive way for users to interact with virtual environments, leading to improved user experiences in gaming, training simulations, and other interactive applications.
Potential Commercial Applications
This technology can be applied in gaming consoles, VR headsets, smart devices, and medical rehabilitation systems to enhance user interaction and feedback.
Possible Prior Art
Prior art may include similar systems using wrist-worn sensors for gesture recognition or hand tracking, but the specific use of wrist-worn antennas for continuous hand pose tracking may be a novel approach.
Unanswered Questions
How does the system handle occlusions or interference in tracking hand poses?
The patent application does not provide details on how the system deals with occlusions or interference that may affect the accuracy of hand pose tracking.
What is the power consumption of the system and how does it impact the battery life of the device?
The patent application does not mention the power consumption of the system or its impact on the battery life of the wrist-worn device.
Original Abstract Submitted
embodiments are disclosed for a continuous hand pose tracking system employing at least one wrist-worn antenna, from which real-time dielectric loading resulting from different hand poses). the sensor data is interpreted by a machine learning backend, which outputs a fully-posed three-dimensional (3d) hand that can be continuously tracked. in some embodiments, two degrees of freedom (2dof) wrist angle and micro-gestures are tracked. the hand pose tracking system can be extended to include two or more and/or different types of antennas operating at different self-resonances. in an embodiment, a method comprises: determining, with at least one processor of a wrist-worn device, a complex impedance characteristic variation based on a dynamic finite electric ground plane of at least one antenna coupled to the device; and predicting, with the at least one processor, a hand pose of a user wearing the device on a their wrist based on the determined complex impedance characteristic variation.
