Hand Movement Recognition via Velocity Variation Detection
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Solution Overview
Problem
Conventional behavior recognition systems require consideration of environmental changes, leading to inefficient movement recognition due to varying hand trajectories, necessitating the collection of multiple hand trajectories to create standard patterns.
Innovation Solution
A behavior recognition apparatus that includes a hand movement observation unit, velocity variation detection unit, feature selection unit, standard behavior pattern storage unit, and behavior recognition unit, which detects and processes hand movement data to recognize user behavior without considering environmental changes by selecting data based on intentional user movements and calculating similarity with stored patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If hand movement data is uniformly extracted at predetermined intervals, then data collection is simple and continuous, but the extracted data includes environmental variations and hand trajectory variations that complicate movement recognition
Solution Approach 1:
The patent extracts only the essential hand movement data points that correspond to intentional user actions, filtering out data influenced by environmental variations. The velocity variation detection unit identifies specific time points where hand velocity changes significantly, and the feature selection unit extracts only data at these critical moments, thereby removing unnecessary environmental noise while maintaining recognition accuracy.
Solution Approach 2:
The patent changes the parameter of data extraction from uniform time intervals to data points selected based on velocity variation thresholds. By transforming the extraction criterion from temporal uniformity to dynamic velocity-based selection, the system captures only the most relevant movement characteristics while eliminating environmental interference.
2Reliability
If all hand trajectories are collected to create standard patterns considering environmental changes, then movement recognition can account for environmental variations, but the complexity and amount of data required increases significantly
Solution Approach 1:
The patent extracts only the essential movement features that are invariant to environmental changes. By focusing on velocity variations and extracting data at specific critical moments rather than collecting all possible hand trajectories, the system reduces the complexity of standard pattern collection while maintaining reliable recognition across different environments.
Solution Approach 2:
The patent applies different processing qualities to different parts of the hand movement data. Instead of uniformly processing all data, it selectively processes only the data points where velocity variations occur, giving enhanced attention to critical movement moments while ignoring environmentally influenced segments.
3Adaptability or versatility
If environmental changes are considered in movement recognition, then recognition can adapt to different settings, but the recognition process becomes more complex and less efficient
Solution Approach 1:
The patent converts the harmful effect of environmental variations into a beneficial filtering mechanism. Instead of trying to accommodate all environmental variations in the recognition process, the system uses velocity variation detection to identify and extract only the movement data that is intrinsic to the user's intent, thereby simplifying the recognition process while maintaining environmental adaptability.
Data Source
AI summary
A behavior recognition apparatus includes a hand movement observation unit which outputs hand movement data indicating a hand movement made by a user; a velocity variation detection unit which detects a time at which the hand movement matches a predetermined movement pattern; and a feature selection unit which selects the hand movement data outputted at the time detected by the velocity variation detection unit. A standard behavior pattern storage unit stores a movement pattern data sequence represented by a hand movement data sequence, in association with a user behavior; and a behavior recognition unit calculates a degree of similarity between the movement pattern data sequence stored in the standard behavior pattern storage unit and the hand movement data selected by the feature selection unit, and recognizes, as the user behavior, a behavior represented by the movement pattern data sequence when the degree of similarity is the highest.