Multi-Sensor Gesture Detection with Complementary Voting
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Solution Overview
Problem
Existing gesture recognition systems in mobile devices rely on single sensors, which are limited in accuracy and power efficiency, especially in varying lighting and proximity conditions.
Innovation Solution
The use of multiple sensors, such as cameras and ultrasound sensors, that dynamically activate and deactivate based on lighting and proximity conditions, with a complementary voting scheme to determine gestures and self-adjust their performance based on data exchange between sensor paths.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are used for gesture recognition, then measurement precision is improved, but use of energy increases
Solution Approach 1:
The system dynamically switches between single-sensor and multi-sensor modes based on environmental conditions (lighting level, gesture range). The camera is selectively deactivated when lighting is poor or gesture is far away, while ultrasound sensor continues operation. This dynamic adaptation maintains gesture recognition accuracy when needed while reducing power consumption in suboptimal conditions.
Solution Approach 2:
The system changes operational parameters by adjusting which sensors are active based on detected conditions. When lighting level is above threshold and gesture range is within threshold, both camera and ultrasound sensor are activated for high accuracy. When conditions deteriorate, the camera is deactivated, switching to ultrasound-only mode to conserve energy.
2Reliability
If multiple sensors are used for gesture recognition, then reliability is improved, but device complexity increases
Solution Approach 1:
The gesture recognition system is segmented into independent sensor paths (camera path and ultrasound path), each capable of autonomous gesture detection. The camera processing path and ultrasound processing path operate independently and can be selectively activated, reducing the complexity burden of having multiple sensors while maintaining reliability through complementary voting.
Solution Approach 2:
The system implements feedback mechanisms where each sensor path provides confidence levels for detected gestures. The complementary voting scheme uses this feedback to determine final gesture output, selecting gestures with higher confidence or using ultrasound as backup when camera confidence is low. This feedback-driven approach improves reliability while managing system complexity through structured decision-making.
3Use of energy by moving object
If sensors are selectively deactivated based on conditions, then use of energy is reduced, but measurement precision may worsen
Solution Approach 1:
The system performs preliminary assessment of environmental conditions (lighting level, gesture range) before activating or deactivating sensors. By evaluating conditions in advance, the system ensures that the camera is only deactivated when conditions are truly suboptimal, preventing premature loss of measurement precision while achieving energy savings when appropriate.
Solution Approach 2:
The ultrasound sensor serves as an intermediary backup when camera operation is compromised by poor lighting or distance. When the camera is deactivated due to suboptimal conditions, the ultrasound sensor continues to provide gesture detection capability, ensuring that measurement precision is maintained through alternative sensing rather than completely lost.
Data Source
AI summary
A method includes receiving a first output from a first sensor of an electronic device and receiving a second output from a second sensor of the electronic device. The first sensor has a first sensor type and the second sensor has a second sensor type that is different from the first sensor type. The method also includes detecting a gesture based on the first output and the second output according to a complementary voting scheme that is at least partially based on gesture complexity.