View source for 18174602. METHODS AND APPARATUSES FOR LATENCY REDUCTION IN GESTURE RECOGNITION USING MMWAVE RADAR simplified abstract (SAMSUNG ELECTRONICS CO., LTD.)

=METHODS AND APPARATUSES FOR LATENCY REDUCTION IN GESTURE RECOGNITION USING MMWAVE RADAR=


==Organization Name==

[[:Category:SAMSUNG ELECTRONICS CO., LTD.|SAMSUNG ELECTRONICS CO., LTD.]]


[[Category:SAMSUNG ELECTRONICS CO., LTD.]]

==Inventor(s)==

[[:Category:Saifeng Ni of Santa Clara CA (US)|Saifeng Ni of Santa Clara CA (US)]][[Category:Saifeng Ni of Santa Clara CA (US)]]

[[:Category:Vutha Va of Plano TX (US)|Vutha Va of Plano TX (US)]][[Category:Vutha Va of Plano TX (US)]]

[[:Category:Priyabrata Parida of Garland TX (US)|Priyabrata Parida of Garland TX (US)]][[Category:Priyabrata Parida of Garland TX (US)]]

[[:Category:Anum Ali of Plano TX (US)|Anum Ali of Plano TX (US)]][[Category:Anum Ali of Plano TX (US)]]

[[:Category:Boon Loong Ng of Plano TX (US)|Boon Loong Ng of Plano TX (US)]][[Category:Boon Loong Ng of Plano TX (US)]]

==METHODS AND APPARATUSES FOR LATENCY REDUCTION IN GESTURE RECOGNITION USING MMWAVE RADAR - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18174602 titled 'METHODS AND APPARATUSES FOR LATENCY REDUCTION IN GESTURE RECOGNITION USING MMWAVE RADAR

==Simplified Explanation==

The method involves processing radar data to detect gestures using a sliding input data window.
* Radar frames in the data window contain selected features and time-velocity or time-angle data.
* Binary predictions are received for each radar frame to indicate if a gesture has ended.
* An early stop (ES) checker is triggered if a gesture end is predicted to determine if ES conditions are met.
* ES conditions include noise frames and valid activity conditions being satisfied.
* A gesture classifier is triggered to predict the type of gesture if ES conditions are met.

== Potential Applications ==
* Gesture recognition systems
* Human-computer interaction technology
* Security systems

== Problems Solved ==
* Accurate detection of gestures in radar data
* Efficient processing of radar frames
* Reduction of false positives in gesture recognition

== Benefits ==
* Improved gesture recognition accuracy
* Real-time gesture detection
* Enhanced user experience in interactive systems


==Original Abstract Submitted==

A method includes obtaining a stream of radar data into a sliding input data window composed of recent radar frames from the stream. Each radar frame within the data window includes features selected from a predefined feature set and at least one of time-velocity data or time angle data. The method includes, for each radar frame within the data window, receiving a binary prediction indicating whether the radar frame includes a gesture end. The method includes in response to the binary prediction indicating that the radar frame includes the gesture end, triggering an early stop (ES) checker to determine whether an ES condition is satisfied. Determining whether the ES condition is satisfied comprises determining whether a noise frames condition and a valid activity condition are satisfied. The method includes in response to a determination that the ES condition is satisfied, triggering a gesture classifier to predict a gesture type.



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