Near-Eye Display Gesture Sensing Using Vibrations and Acoustics
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Solution Overview
Problem
Existing near-eye display systems face challenges in accurately and efficiently detecting swipe gestures due to variations in user behavior, environmental factors, and computational demands, leading to inconsistent performance and a learning curve for users.
Innovation Solution
Implementing sensors that transduce physical phenomena into electrical signals, combined with machine learning algorithms, particularly deep neural networks, to analyze inertial and acoustic data for precise swipe gesture detection, distinguishing between different types of gestures and reducing false positives.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional gesture detection methods are used in near-eye display systems, then the device structure remains simple, but gesture recognition accuracy deteriorates due to variations in user behavior and environmental factors
Solution Approach 1:
The patent combines multiple sensor types (inertial sensors, acoustic sensors, and touch sensors) into an integrated gesture detection system. By merging these different sensing modalities, the system achieves more accurate gesture recognition through data fusion, where each sensor type compensates for the weaknesses of others, particularly in distinguishing swipe gestures from other gestures under varying environmental conditions
Solution Approach 2:
The gesture detection process is segmented into multiple independent analysis stages: inertial data analysis, acoustic data analysis, and touch sensor data analysis. Each segment processes specific characteristics separately before integration, allowing the system to handle complex gesture patterns through modular processing while maintaining overall system manageability
2Reliability
If computational algorithms are enhanced to improve gesture detection accuracy, then gesture recognition reliability improves, but energy consumption increases
Solution Approach 1:
The system applies partial processing by selectively analyzing sensor data based on detected patterns. Instead of continuously processing all sensor inputs at maximum computational depth, the system performs initial filtering and only applies full computational algorithms when gesture patterns are detected, reducing overall energy consumption while maintaining detection reliability
Solution Approach 2:
The sensor system serves itself by using inertial and acoustic sensors to detect potential gesture events, which then trigger more intensive processing only when needed. This self-service mechanism allows the system to maintain high reliability by being ready to detect gestures while avoiding unnecessary energy expenditure during non-gesture periods
3Measurement precision
If multiple sensor types are integrated to distinguish different gestures, then gesture detection accuracy improves, but device complexity and manufacturing difficulty increase
Solution Approach 1:
The patent implements multi-functionality by designing sensor elements that can detect multiple types of inputs through a single integrated structure. The sensor system can detect touch, acoustic waves, and inertial movements using a unified detection framework, reducing the need for separate dedicated sensors for each gesture type and simplifying the manufacturing process
Solution Approach 2:
The system uses an intermediary processing layer that standardizes data from different sensor types into a common format. This intermediary processing stage harmonizes the diverse sensor outputs, making integration easier and reducing manufacturing complexity by providing a unified interface for handling multiple sensor modalities
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
Enhances gesture recognition accuracy, reliability, and energy efficiency while minimizing wear and tear, allowing for sleeker designs and consistent performance across various environments, including challenging conditions.
Implementation Method 1
analyzing a first set of sensor data, including one or both of inertial sensor data or acoustic sensor data
Implementation Method 2
analyzing a first set of sensor data, including one or both of inertial sensor data or acoustic sensor data
Data Source
AI summary
A near-eye display (NED) system analyzes a set of sensor data. The set of sensor data includes one or both of inertial sensor data, such as accelerometer data, or acoustic sensor data, such as microphone data, obtained from one or more sensors of the NED system. Based on the analysis of the set of sensor data and in response to a detection that the set of sensor data includes one or more of inertial characteristics or acoustic characteristics corresponding to a gesture, the NED system generates an indication that a gesture has occurred and one or more operations of the NED system are controlled in response to the indication.