Capacitive Gesture Recognition Using Slant Parameters and Temporal Evolution
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Solution Overview
Problem
Current gesture recognition technologies in self-sensing capacitive display devices are limited in accurately distinguishing between hand and finger gestures, particularly in touch-less modes, and struggle to effectively recognize the direction of hovering gestures without physical contact.
Innovation Solution
The implementation of a method using circular buffers to analyze X and Y axis signals from a capacitive sensing panel, which computes slant parameters to differentiate between hand and finger gestures, and determines the direction of hovering movements by analyzing the temporal evolution of capacitance signals, enabling recognition of gestures up to 3 centimeters from the panel surface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gesture recognition methods are used, then the device can detect basic touch gestures, but it cannot accurately distinguish between hand and finger gestures in touch-less mode
Solution Approach 1:
The patent segments the gesture recognition process into distinct analytical components: extracting X and Y coordinates from capacitance signals, computing slant parameters separately for each axis, analyzing temporal evolution patterns, and distinguishing between hand and finger gestures through systematic comparison of these segmented features. This segmentation enables accurate gesture differentiation while maintaining manageable processing complexity.
2Length of stationary object
If the sensing range is extended to detect gestures up to 3 centimeters from the panel, then touch-less gesture recognition is enabled, but the precision in determining gesture direction is reduced
Solution Approach 1:
The patent adds the temporal dimension to the spatial X-Y coordinate data by analyzing the temporal evolution of capacitance signals. This creates a four-dimensional analysis space (X, Y, time, slant parameter) that enables precise determination of gesture direction even when the hand is positioned 3 centimeters from the panel surface, thereby maintaining measurement precision while extending sensing range.
3Ease of operation
If simple capacitance detection is used, then the device structure remains simple, but it cannot recognize the direction of hovering gestures
Solution Approach 1:
The patent performs preliminary actions by pre-computing slant parameters from the raw capacitance signals and storing them in circular buffers before full gesture analysis is required. This preliminary processing of coordinate extraction and slant calculation enables the system to quickly determine gesture direction without requiring complex real-time analysis, thus achieving ease of operation while managing device complexity through staged processing.
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 allows for precise recognition of hand and finger gestures, including their direction, in a touch-less mode, enhancing the capability for intuitive and hygienic interaction with devices by accurately detecting hovering gestures without physical contact.
Implementation Method 1
self-sensing capacitive display devices
Data Source
AI summary
In an embodiment, 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 locations 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 a set of X-node sensing signals and for a set of 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 X-node and Y-node sensing signals while remaining stationary for the other of the sets of X-node and Y-node sensing signals. Finger shapes are distinguished over “ghosts” generated by palm or first features by transforming the node-intensity representation for the sensing signals into a node-distance representation, based on the distances of the detection intensities for a number of nodes under a peak for a mean point between the valleys adjacent to the peak.