Gesture Recognition via Sinusoidal Pattern Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for recognizing motion gestures lack accuracy and efficiency, particularly in distinguishing between intentional and unintentional movements, which affects the reliability of human-computer interfaces.
Innovation Solution
A system that tracks the position of a moving object over time along a defined shape within a motion history map, graphing its position as a proportion of a single dimension to identify waving, swiping, or oscillating gestures by recognizing patterns resembling sinusoids, and maps these gestures to control inputs, eliminating the need for physical buttons and reducing blurring effects on touch screens.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional motion gesture recognition methods are used, then the system can detect basic movements, but the accuracy in distinguishing intentional gestures from unintentional movements is insufficient
Solution Approach 1:
The patent segments the motion detection process into multiple components: motion history map generation, sinusoidal pattern extraction, parameter calculation (amplitude, frequency, phase), and threshold-based classification. This segmentation allows each component to be optimized independently, improving overall gesture recognition accuracy while maintaining reliability.
Solution Approach 2:
The patent transforms 2D motion data into a temporal dimension by analyzing motion history maps across multiple frames. By graphing position versus time and detecting sinusoidal patterns, the system adds a temporal dimension to gesture recognition, enabling distinction between intentional rhythmic gestures and unintentional movements.
2Ease of operation
If physical buttons are used for user interaction, then the interface is simple to implement, but touch screen blurring occurs and physical interaction is required
Solution Approach 1:
The patent replaces mechanical touch input with optical motion detection. Instead of requiring physical contact with the touchscreen (which causes blurring), the system uses a camera to detect hand gestures in 3D space and interprets them as control commands, eliminating the blurring issue while maintaining ease of operation.
Solution Approach 2:
The patent creates a virtual copy of button functionality through gesture recognition. Instead of physical buttons, the system recognizes specific hand motion patterns (such as waving gestures) and maps them to equivalent control functions, providing the same user experience without the harmful effects of physical touch on the screen.
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
Enhances the accuracy and efficacy of human-computer interfaces by allowing users to invoke functions through defined gestures, improving functionality selection and eliminating the need for physical interaction, thus reducing errors and blurring issues.
Implementation Method 1
Cameras have been used to capture images of objects
Implementation Method 2
optical flow has been used to detect motion of an object by analyzing multiple images of the object taken at different times
Data Source
Figure 1A
Figure 1B
Figure 2
AI summary
The enhanced detection of a waving engagement gesture, in which a shape is defined within motion data, the motion data is sampled at points that are aligned with the defined shape, and, based on the sampled motion data, positions of a moving object along the defined shape are determined over time. It is determined whether the moving object is performing a gesture based on a pattern exhibited by the determined positions, and an application is controlled if determining that the moving object is performing the gesture.