3D Gesture Detection via Inertial Trajectory Analysis
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Solution Overview
Problem
Current gesture detection technologies face instability issues, leading to intermittent or erroneous detection of gestures, which hampers smooth interaction with electronic devices.
Innovation Solution
A three-dimensional gesture detection device and method that utilize a node detection unit, gesture recognition model, and gesture trajectory detection unit to analyze continuous hand images, classify key points, and determine inertial trajectories, ensuring continuous and smooth gesture detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If gesture detection is performed using current available technologies, then gesture control can be implemented, but the detection stability deteriorates leading to intermittent or erroneous detection
Solution Approach 1:
The hand image is segmented into multiple nodes representing key points (fingers, palm, wrist), and each node is independently tracked across frames. This segmentation allows the system to maintain continuous trajectory information even when overall gesture detection is uncertain, improving both stability and precision by analyzing individual body parts separately.
Solution Approach 2:
The system performs preliminary actions by detecting nodes and calculating inertial trajectories in advance before final gesture recognition. The trajectory analysis unit processes node positions and computes smooth trajectories beforehand, preparing continuous motion data that enhances the reliability of subsequent gesture classification and reduces intermittent detection errors.
2Stability of the object's composition
If gesture detection is performed using current available technologies, then gesture control can be implemented, but the detection continuity deteriorates leading to intermittent detection
Solution Approach 1:
The system ensures continuity of useful action by maintaining continuous tracking of node positions across all video frames and computing inertial trajectories that span multiple frames. The trajectory analysis unit continuously processes node data without interruption, generating smooth continuous trajectories that preserve detection continuity and prevent intermittent detection gaps, thereby improving overall detection stability.
3Measurement precision
If gesture detection is performed using current available technologies, then gesture control can be implemented, but the detection accuracy deteriorates leading to erroneous detection
Solution Approach 1:
The system implements feedback by using the detected inertial trajectories to validate and refine gesture recognition results. The trajectory information serves as feedback that corrects erroneous detections, allowing the system to adjust its gesture classification based on the actual continuous motion patterns observed, thereby improving both accuracy and stability simultaneously.
Data Source
AI summary
A three-dimensional gesture detection device and a three-dimensional gesture detection method are provided. The three-dimensional gesture detection device includes a node detection unit, a gesture recognition model and a gesture trajectory detection unit. The node detection unit obtains several nodes according to each of the hand frames of a continuous hand image. The gesture recognition model obtains confidence levels of several gesture categories. The gesture trajectory detection unit includes a weight analyzer, a gesture analyzer, a key point classifier and a trajectory analyzer. The weight analyzer obtains weights of the gesture categories through a user interface. The gesture analyzer performs a weighting calculation on the confidence levels of the gesture categories to analyze a gesture on each of the hand frames. The key point classifier classifies several key points from the nodes. The trajectory analyzer obtains an inertial trajectory of the gesture according to the key points.