Dynamic Gesture Parameter Tuning for False Positive Reduction
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Solution Overview
Problem
Existing gesture recognition systems face challenges in accurately interpreting user inputs due to the difficulty in determining suitable parameter ranges for different users, leading to issues with false positives and false negatives, as larger ranges increase the risk of misidentification while smaller ranges may result in missed inputs.
Innovation Solution
The system dynamically tunes gesture parameters based on user-specific data collected during ordinary device use or interactive games, adjusting ranges to improve recognition accuracy by learning from user interactions and feedback, such as false positives and negatives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If larger parameter ranges are used for gesture recognition, then the system becomes more tolerant of user variations and reduces false negatives, but the risk of false positives increases
Solution Approach 1:
The patent implements dynamic adjustment of gesture parameter ranges based on individual user characteristics. The system starts with default parameter ranges and automatically tunes them by analyzing user feedback from false positives and false negatives, transforming static recognition thresholds into adaptive, user-specific parameters that optimize between false positive and false negative rates
Solution Approach 2:
The system modifies gesture recognition parameters (such as motion thresholds, velocity ranges, and acceleration limits) based on collected user data. By changing these parameters dynamically according to individual user behavior patterns, the system resolves the contradiction between maintaining broad tolerance for user variations and preventing misidentification of unintended gestures
2Object-affected harmful factors
If smaller parameter ranges are used for gesture recognition, then the risk of false positives is reduced, but false negatives increase
Solution Approach 1:
The system dynamically adapts parameter ranges rather than using fixed small ranges. By continuously learning from user interactions and adjusting thresholds accordingly, the system maintains tight control to minimize false positives while ensuring legitimate gestures are recognized, thus avoiding the false negative problem associated with overly restrictive parameters
Solution Approach 2:
The gesture recognition system performs self-tuning by automatically analyzing its own performance metrics (false positives and false negatives) and adjusting its parameters accordingly. This self-service mechanism allows the system to optimize its parameter ranges autonomously, reducing false positives without sacrificing recognition accuracy
3Device complexity
If fixed gesture parameters are used, then the system is simpler to implement, but it cannot adapt to individual user behaviors
Solution Approach 1:
The system implements a two-phase approach: first using default fixed parameters for initial operation, then transitioning to adaptive parameter tuning after collecting sufficient user data. This preliminary use of simple fixed parameters delays the complexity of adaptive tuning until necessary, while still achieving user-specific adaptation over time
Solution Approach 2:
The system incorporates feedback loops that monitor gesture recognition outcomes and use this information to automatically adjust parameters. By implementing feedback mechanisms that learn from user interactions, the system gains adaptability to individual behaviors while maintaining relatively simple implementation through automated adjustment rather than manual configuration
Data Source
AI summary
Embodiments are disclosed herein that relate to tuning gesture recognition characteristics for a device configured to receive gesture-based user inputs. For example, one disclosed embodiment provides a head-mounted display device including a plurality of sensors, a display configured to present a user interface, a logic machine, and a storage machine that holds instructions executable by the logic machine to detect a gesture based upon information received from a first sensor of the plurality of sensors, perform an action in response to detecting the gesture, and determine whether the gesture matches an intended gesture input. The instructions are further executable to update a gesture parameter that defines the intended gesture input if it is determined that the gesture detected does not match the intended gesture input.