View source for Google llc (20240338084). GESTURE DETECTION VIA IMAGE CAPTURE OF SUBDERMAL TISSUE FROM A WRIST-POINTING CAMERA SYSTEM simplified abstract

=GESTURE DETECTION VIA IMAGE CAPTURE OF SUBDERMAL TISSUE FROM A WRIST-POINTING CAMERA SYSTEM=


==Organization Name==

[[:Category:google llc|google llc]]


[[Category:google llc]]

==Inventor(s)==

[[:Category:Dongeek Shin of San Jose CA (US)|Dongeek Shin of San Jose CA (US)]][[Category:Dongeek Shin of San Jose CA (US)]]

[[:Category:Andrea Colaco of Los Altos CA (US)|Andrea Colaco of Los Altos CA (US)]][[Category:Andrea Colaco of Los Altos CA (US)]]

[[:Category:Stiven Guillaume Francois Morvan of New York NY (US)|Stiven Guillaume Francois Morvan of New York NY (US)]][[Category:Stiven Guillaume Francois Morvan of New York NY (US)]]

[[:Category:Adam James Banfield of Troy NY (US)|Adam James Banfield of Troy NY (US)]][[Category:Adam James Banfield of Troy NY (US)]]

[[:Category:Shahram Izadi of Tiburon CA (US)|Shahram Izadi of Tiburon CA (US)]][[Category:Shahram Izadi of Tiburon CA (US)]]

==GESTURE DETECTION VIA IMAGE CAPTURE OF SUBDERMAL TISSUE FROM A WRIST-POINTING CAMERA SYSTEM - A simplified explanation of the abstract==


This abstract first appeared for US patent application 20240338084 titled 'GESTURE DETECTION VIA IMAGE CAPTURE OF SUBDERMAL TISSUE FROM A WRIST-POINTING CAMERA SYSTEM

'''Simplified Explanation:'''
The patent application describes a system that uses near-infrared imaging to detect hand gestures based on changes in biological flow metrics in the user's wrist.

* An image capture device on a wristband emits infrared radiation into the user's wrist and captures two-dimensional images of the dermal layer.
* Gesture detection circuitry analyzes the sequence of images to determine values of a biological flow metric, such as perfusion index, between frames.
* A trained model generates the metric from the image sequence, mapping these values to specific hand/finger movements that represent different gestures.

'''Key Features and Innovation:'''
* Use of near-infrared imaging to detect hand gestures based on biological flow metrics.
* Mapping of biological flow metric values to specific hand/finger movements for gesture recognition.

'''Potential Applications:'''
* Human-computer interaction systems.
* Virtual reality and augmented reality applications.
* Healthcare monitoring and rehabilitation devices.

'''Problems Solved:'''
* Accurate and non-invasive hand gesture recognition.
* Real-time monitoring of biological flow metrics for various applications.

'''Benefits:'''
* Improved user experience in human-computer interaction.
* Enhanced accuracy and efficiency in gesture recognition.
* Non-invasive and continuous monitoring of biological flow metrics.

'''Commercial Applications:'''
Potential commercial applications include:
* Gesture-controlled devices in gaming and entertainment.
* Healthcare monitoring systems for patient rehabilitation.
* Virtual reality and augmented reality interfaces for immersive experiences.

'''Questions about Hand Gesture Recognition:'''
1. How does the use of near-infrared imaging improve hand gesture recognition compared to other methods?
2. What are the potential limitations of using biological flow metrics for gesture detection?

'''Frequently Updated Research:'''
Ongoing research in this field may focus on improving the accuracy and speed of gesture recognition algorithms, as well as expanding the range of detectable gestures and applications.


==Original Abstract Submitted==

techniques of operating an ar system include determining hand gestures formed by a user based on a sequence of two-dimensional images through skin of the user's wrist acquired from a near-infrared camera. specifically, an image capture device disposed on a band worn around a user's wrist includes a source of electromagnetic radiation, e.g., light-emitting diodes in the infrared (ir) wavelength band that emit the radiation into the user's wrist and an ir detector which produces the sequence of two-dimensional images of a region within a dermal layer in the user's wrist. from this sequence, gesture detection circuitry determines values of a biological flow metric, e.g., a change in perfusion index (pi) between frames of the sequence, based on a trained model that generates the metric from the sequence. finally, the gesture detection circuitry maps the values of the biological flow metric to specific hand/finger movements that determine a gesture.



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