Radar Gesture Recognition Using Point Cloud Filtering
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Solution Overview
Problem
Current gesture recognition technologies using radar-based frequency modulated continuous wave (FMCW) modulation struggle with high unintentional triggering probabilities due to difficulties in distinguishing point cloud data sets generated by hand actions from those generated by human movement, leading to suboptimal man-machine interaction experiences.
Innovation Solution
A method involving filtering and clustering of point cloud data sets to identify valid hand gestures by setting horizontal distance and duration thresholds, followed by feature value analysis using neural networks and similarity calculations to enhance recognition accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If radar signals are used to generate point cloud data for gesture recognition, then gesture recognition capability is enabled, but the probability of unintentional triggering increases due to inability to distinguish hand actions from other movements
Solution Approach 1:
The patent applies local quality by focusing analysis on specific regions of the point cloud data corresponding to hand locations. By identifying hand regions through clustering and analyzing only the motion characteristics within these localized areas, the system can distinguish intentional hand gestures from general body movements, thereby reducing unintentional triggering while maintaining gesture recognition capability.
Solution Approach 2:
The patent utilizes dynamics by analyzing the temporal evolution and motion patterns of point cloud clusters over time. By examining how clusters move, change shape, and evolve across multiple frames, the system can differentiate between deliberate gesture actions and accidental movements, resolving the contradiction between recognition capability and false triggering.
2Measurement precision
If filtering based on maximum horizontal distance between cluster centers is applied, then stationary vs moving state differentiation is improved, but device complexity increases
Solution Approach 1:
The patent applies partial action by implementing a tiered filtering approach. Instead of applying complex filters to all point cloud data uniformly, the system first applies a simple maximum horizontal distance filter to quickly identify stationary periods. Only when this initial filter indicates potential gesture activity does the system engage more sophisticated analysis, thereby achieving accurate stationary state detection without consistently high processing complexity.
3Measurement precision
If clustering processing is performed on point cloud data subsets, then gesture differentiation accuracy is improved, but processing time increases
Solution Approach 1:
The patent applies segmentation by dividing the point cloud data into multiple subsets corresponding to different time frames or spatial regions. By performing clustering processing on these segmented subsets rather than on the entire dataset at once, the system achieves accurate gesture differentiation through localized analysis while reducing overall processing time through parallel or sequential processing of smaller segments.
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
Reduces unintentional gesture recognition triggers and improves user interaction experience by accurately filtering out irrelevant data, ensuring that only valid hand gestures are recognized.
Implementation Method 1
a radar based on a frequency modulated continuous wave (frequency modulated continuous wave, FMCW) modulation mode should be used to sense the gesture action. After a radar sensor sends radar signals to a user, the radar signals act on a human body to generate reflected signals.
Data Source
AI summary
A gesture recognition method and a related apparatus are provided, to obtain a first point cloud data set by filtering an original point cloud data set collected by a radar apparatus. The first point cloud data set includes a plurality of frames of first point cloud subsets, the first point cloud subset includes a first cluster center, the first cluster center is a cluster center of a plurality of pieces of point cloud data in the first point cloud subset, a maximum horizontal distance between any two first cluster centers meets a first preset condition, and duration of the first point cloud data set meets the first preset condition. Point cloud data whose motion track does not match gesture motion can be effectively filtered out. Gesture recognition is performed by using the first point cloud data set obtained by filtering.