Adaptive Multi-Sensor Gesture Recognition Thresholds
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Solution Overview
Problem
In computing environments using natural user interfaces, the variability in sensor inputs due to changes in available devices and environmental conditions affects the reliability and quality of gesture recognition, necessitating adaptive methods to compensate for these changes.
Innovation Solution
An adaptive multi-sensor gesture recognition system that adjusts confidence thresholds based on compensating events, such as excessive motion or changes in sensor availability, and synchronizes sensor inputs to generate a multi-sensor confidence value for accurate gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple sensor inputs are used for gesture recognition, then the accuracy and reliability of gesture recognition is improved, but the system complexity and difficulty of handling variable sensor availability increases
Solution Approach 1:
The system dynamically adjusts confidence thresholds based on the number and type of available sensors. When fewer sensors are available, the system adapts by modifying the thresholds to maintain reliable gesture recognition without requiring a fixed sensor configuration, thus resolving the contradiction between reliability and device complexity
Solution Approach 2:
The system changes operational parameters (confidence thresholds) based on sensor availability conditions. By adjusting these parameters dynamically, the system maintains high gesture recognition reliability while accommodating variable sensor configurations without increasing inherent system complexity
2Measurement precision
If confidence thresholds are adjusted dynamically based on sensor conditions, then the accuracy of gesture recognition is maintained under varying conditions, but the computational overhead and processing time increases
Solution Approach 1:
The system pre-establishes multiple confidence threshold configurations corresponding to different sensor availability scenarios. During operation, it selectively applies the appropriate pre-computed thresholds based on current sensor conditions, maintaining high accuracy while avoiding the computational overhead of real-time threshold optimization
Solution Approach 2:
The system applies confidence threshold adjustments only when sensor availability changes or compensating events occur, rather than continuously. This partial action approach maintains measurement precision while significantly reducing unnecessary computational overhead and processing time
3Stability of the object's composition
If the system compensates for sensor deficiencies by adjusting confidence thresholds, then the consistency of gesture recognition performance is improved, but the system's adaptability requirements and configuration complexity increases
Solution Approach 1:
The system implements feedback mechanisms that monitor sensor availability and performance, automatically adjusting confidence thresholds to maintain consistent gesture recognition. This closed-loop approach ensures performance stability while minimizing the need for manual system adaptation and configuration
Solution Approach 2:
The system performs self-adjustment of confidence thresholds based on monitored sensor conditions, eliminating the need for external configuration or manual adaptation. This self-service capability maintains gesture recognition consistency while reducing system adaptability requirements
Data Source
AI summary
Methods for recognizing gestures using adaptive multi-sensor gesture recognition are described. In some embodiments, a gesture recognition system receives a plurality of sensor inputs from a plurality of sensor devices and a plurality of confidence thresholds associated with the plurality of sensor inputs. A confidence threshold specifies a minimum confidence value for which it is deemed that a particular gesture has occurred. Upon detection of a compensating event, such as excessive motion involving one of the plurality of sensor devices, the gesture recognition system may modify the plurality of confidence thresholds based on the compensating event. Subsequently, the gesture recognition system generates a multi-sensor confidence value based on whether at least a subset of the plurality of confidence thresholds has been satisfied. The gesture recognition system may also modify the plurality of confidence thresholds based on the plugging and unplugging of sensor inputs from the gesture recognition system.