Gesture Recognition Using Circular Buffer Temporal Analysis
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Solution Overview
Problem
Current gesture recognition technologies in self-sensing capacitive display devices are limited in their ability to accurately distinguish between hand and finger gestures, and to recognize gestures performed in proximity without physical contact, particularly in touch-less modes, which restricts their application and sensitivity.
Innovation Solution
The implementation of a method that utilizes circular buffers to analyze X and Y axis signals from a capacitive sensing panel, computing slant parameters and distinguishing between hand and finger gestures by analyzing the temporal evolution of capacitance signals, allowing for the detection of hovering gestures and their direction, even at distances up to 3 centimeters from the panel surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gesture recognition methods are used in self-sensing capacitive display devices, then the basic touch functionality is maintained, but the ability to accurately distinguish between hand and finger gestures and to recognize gestures performed in proximity without physical contact is limited
Solution Approach 1:
The patent segments the gesture recognition process into distinct analysis stages: signal acquisition from capacitive sensing panel, temporal evolution analysis using circular buffers, slant parameter computation, and gesture classification. This segmentation enables precise differentiation between hand and finger gestures by analyzing specific temporal characteristics of capacitance signals at different time points
Solution Approach 2:
The patent implements preliminary action by storing capacitance signal values in circular buffers before analysis. The system pre-processes and stores signal data at multiple time points (t1, t2, t3) and computes slant parameters in advance, enabling accurate gesture classification when the gesture occurs without requiring complex real-time processing during the actual gesture recognition moment
2Ease of operation
If the sensing range is extended to detect gestures up to 3 centimeters from the panel surface, then touch-less interaction capability is improved, but the complexity of signal analysis and gesture differentiation increases
Solution Approach 1:
The patent applies self-service by using the capacitive sensing panel's inherent ability to detect capacitance changes without requiring additional sensors or complex hardware modifications. The system leverages the panel's existing self-sensing capability and processes the signals through software-based temporal analysis and slant parameter computation, avoiding the need for extra hardware components
Solution Approach 2:
The system performs preliminary signal processing by storing capacitance values in circular buffers and pre-computing temporal characteristics before gesture classification. This preliminary action simplifies the real-time analysis complexity by preparing signal data in advance, making touch-less gesture detection feasible without overwhelming computational requirements during actual operation
3Measurement precision
If temporal analysis of coordinates is performed for each frame in an image sequence, then hovering gesture detection accuracy is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-storing capacitance signal values in circular buffers for multiple time points before analysis is needed. The system prepares temporal signal data in advance, so when hovering gesture detection is required, the analysis can proceed efficiently using pre-organized data rather than collecting and processing raw signals in real-time
Solution Approach 2:
The system performs partial analysis by focusing computational resources on specific temporal characteristics (slant parameters) rather than analyzing all possible signal features. By concentrating on the most discriminative temporal aspects of the capacitance signals, the system achieves high detection accuracy without the computational overhead of exhaustive frame-by-frame coordinate analysis
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables enhanced gesture recognition, allowing for intuitive touch-less interactions, improved sensitivity, and the ability to differentiate between hand and finger gestures, expanding the capabilities of self-sensing capacitive display devices in various applications.
Implementation Method 1
self-sensing capacitive display devices
Data Source
AI summary
Hand gestures, such as hand or finger hovering, in the proximity space of a sensing panel are detected from X-node and Y-node sensing signals indicative of the presence of a hand feature at corresponding row and column locations of a sensing panel. Hovering is detected by detecting the locations of maxima for a plurality of frames over a time window for sets of X-node and Y-node sensing signals by recognizing a hovering gesture if the locations of the maxima detected vary over the plurality of frames for one of the sets of sensing signals and not for the other of set. Finger shapes are distinguished over “ghosts” generated by palm or fist features by transforming the node-intensity representation for the sensing signals into a node-distance representation based on distances of detection intensities for a number of nodes under a peak for a mean point between valleys adjacent to the peak.