Staged Gesture Recognition Pipeline for Power Efficiency
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Solution Overview
Problem
Current gesture recognition systems in mobile devices face challenges such as high computational demands, power consumption issues, and false positives due to the use of resource-intensive algorithms like Hidden Markov Models (HMM), which limit the accuracy and richness of gesture vocabularies, especially in resource-constrained devices with low latency requirements.
Innovation Solution
A power-efficient staged gesture recognition pipeline that leverages multimodal interaction detection, context-based optimized recognition, and continuous learning, using a low-power sensing unit for initial data processing and selectively invoking more intensive algorithms only when necessary, along with context-aware model loading and filler models to reduce false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If HMM algorithms are used for gesture recognition, then measurement precision is improved, but use of energy increases and device complexity increases
Solution Approach 1:
The patent segments gesture recognition into multiple stages: a first stage using low-power algorithms (template matching, simple pattern recognition) and a second stage using more computationally intensive HMM algorithms. The system transitions between stages based on confidence levels, allowing accurate gesture recognition while minimizing power consumption by using HMM only when necessary.
Solution Approach 2:
The patent introduces an intermediary confidence level assessment between the initial low-power gesture detection and the final HMM-based recognition. This intermediary stage determines whether the simple algorithms are sufficient or if the system needs to invoke the more power-consuming HMM algorithms, thus mediating between power efficiency and recognition accuracy.
2Measurement precision
If HMM algorithms are used for gesture recognition, then measurement precision is improved, but productivity decreases due to computational demands
Solution Approach 1:
The patent divides the gesture recognition process into two segments: a fast first stage using computationally simple algorithms that can process gestures quickly, and a slower second stage using HMM algorithms reserved for complex or ambiguous gestures. This segmentation maintains high processing speed for common gestures while achieving high accuracy when needed.
Solution Approach 2:
The patent applies partial action by using simple recognition algorithms for the majority of gestures that don't require high accuracy, and only invoking the more computationally intensive HMM algorithms partially - specifically when the first stage algorithms are uncertain or when gesture complexity demands it. This approach maintains productivity while achieving necessary precision.
3Adaptability or versatility
If continuous gesture recognition is implemented, then adaptability is improved, but use of energy increases causing false positives
Solution Approach 1:
The patent implements a dynamic gesture recognition system that adapts its algorithm selection based on real-time conditions. The system dynamically switches between low-power and high-accuracy algorithms depending on gesture complexity, confidence levels, and contextual information, allowing rich gesture vocabulary support while managing power consumption dynamically rather than continuously.
Solution Approach 2:
The patent changes operational parameters (algorithm selection, processing intensity) based on gesture characteristics and context. By adjusting the recognition threshold and algorithm complexity according to the specific gesture being detected, the system achieves high adaptability for diverse gestures while avoiding continuous high-power operation that would cause false positives.
4Ease of operation
If simple gestures are used to trigger recognition, then ease of operation is improved, but adaptability decreases
Solution Approach 1:
The patent segments gestures into simple trigger gestures that are easy to perform and more complex follow-up gestures that provide rich vocabulary. The simple gestures serve as accessible entry points for all users, while the system then engages more sophisticated recognition algorithms to interpret complex gesture sequences, thus maintaining both ease of operation and adaptability.
Data Source
AI summary
Embodiments of the invention describe a system to efficiently execute gesture recognition algorithms. Embodiments of the invention describe a power efficient staged gesture recognition pipeline including multimodal interaction detection, context based optimized recognition, and context based optimized training and continuous learning. Embodiments of the invention further describe a system to accommodate many types of algorithms depending on the type of gesture that is needed in any particular situation. Examples of recognition algorithms include but are not limited to, HMM for complex dynamic gestures (e.g. write a number in the air), Decision Trees (DT) for static poses, peak detection for coarse shake/whack gestures or inertial methods (INS) for pitch/roll detection.