Time-of-flight Hand Gesture Recognition with Bayesian Processing
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Solution Overview
Problem
Current hand gesture recognition systems for electronic devices require intensive processing and memory, making them inefficient for use in smaller devices and consumer electronics, and are not optimized for low power consumption.
Innovation Solution
An electronic device equipped with a laser source and detectors uses time-of-flight measurements and Bayesian probabilities to determine hand gestures, reducing processing power and memory requirements by calculating Mean Absolute Deviation (MAD) values and employing a confusion matrix for gesture recognition.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If time-of-flight and machine learning based algorithms are used for hand gesture recognition, then gesture recognition accuracy is improved, but processing power and memory requirements increase
Solution Approach 1:
The patent extracts only the essential features needed for gesture recognition (distance values from multiple detectors) and removes unnecessary computational complexity. Instead of using full machine learning algorithms, it extracts and processes only the critical distance measurements, achieving accurate gesture recognition with minimal processing requirements.
Solution Approach 2:
The patent changes the approach from complex algorithmic processing to simple parameter comparison. It measures distance parameters from multiple detectors and uses straightforward mathematical operations (mean calculation, standard deviation) to recognize gestures, replacing intensive machine learning computations with efficient parameter-based decision making.
2Adaptability or versatility
If complex algorithms are used for hand gesture recognition, then gesture discrimination capability is improved, but power consumption increases
Solution Approach 1:
The patent replaces complex computational mechanisms with simpler physical measurement principles. It uses time-of-flight measurements from multiple detectors and basic statistical calculations instead of intensive algorithmic processing, significantly reducing power consumption while maintaining gesture discrimination capability.
Solution Approach 2:
The patent uses partial action by measuring distance with multiple detectors and processing only the essential distance values. Instead of implementing complete machine learning pipelines, it performs selective measurements and simplified calculations, achieving sufficient gesture recognition with reduced computational effort and lower power consumption.
3Measurement precision
If more detectors are used for hand gesture recognition, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent segments the measurement task across multiple detectors positioned at different locations. Each detector independently measures distance to the hand, and the system combines these segmented measurements to achieve comprehensive spatial awareness and improved measurement precision without requiring a single complex detector.
Solution Approach 2:
The patent makes each detector universal by having all detectors perform the same basic function (measure distance using time-of-flight). This multi-functionality approach allows the system to achieve improved measurement precision through multiple identical simple detectors rather than one complex detector, reducing overall device complexity.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables efficient hand gesture recognition with lower processing power and memory usage, allowing for effective use in small devices and consumer electronics while maintaining a high success rate comparable to more complex systems.
Implementation Method 1
A controller is coupled to the at least one laser source and plurality of laser detectors and configured to determine a set of distance values to the user's hand for each respective laser detector based upon a time-of-flight of the laser radiation
Implementation Method 2
A plurality of laser detectors is configured to receive reflected laser radiation from the user's hand
Data Source
AI summary
An electronic device includes at least one laser source configured to direct laser radiation toward a user's hand. Laser detectors are configured to receive reflected laser radiation from the user's hand. A controller is coupled to the at least one laser source and laser detectors and configured to determine a set of distance values to the user's hand for each respective laser detector and based upon a time-of-flight of the laser radiation. The controller also determines a hand gesture from among a plurality of possible hand gestures based upon the sets of distance values using Bayesian probabilities.