Head-wearable Audio Gesture Recognition with Per-User Parameters
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Solution Overview
Problem
Existing head-wearable audio devices have limited capabilities for recognizing and managing gesture inputs, particularly in scenarios where users wear gloves or have disabilities, and there is a need for more accurate and user-specific gesture recognition.
Innovation Solution
The implementation of a head-wearable audio device equipped with motion sensors and capacitive touch sensors that utilize per-user per-gesture-type recognition parameter values to accurately identify gestures, allowing for the execution of corresponding actions and providing gesture confirmation indicators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If per-user per-gesture-type recognition parameter values are implemented, then gesture recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments gesture recognition into distinct types (e.g., tap, swipe, pinch) and creates separate parameter sets for each type. This allows the device to manage complexity by processing parameters in discrete, organized categories rather than as a monolithic system, improving accuracy while maintaining manageable complexity through structured data organization.
Solution Approach 2:
The system dynamically adjusts recognition parameters based on user-specific characteristics and gesture type. By changing parameter values according to user profile and gesture category, the system achieves high accuracy without requiring all possible parameter combinations to be stored simultaneously, thus managing device complexity through adaptive parameter selection.
2Adaptability or versatility
If multiple sensor types are used for gesture detection, then gesture recognition capability is improved, but device complexity increases
Solution Approach 1:
The system implements a universal gesture recognition framework that handles multiple sensor types (capacitive touch sensors, motion sensors, gyroscopes) through a common processing architecture. This multi-functional approach allows the device to maintain versatility in gesture detection while reducing complexity by using a single unified system rather than separate dedicated systems for each sensor type.
Solution Approach 2:
The patent merges data from multiple sensor types into a unified gesture recognition process. By combining capacitive touch parameters, motion vectors, and orientation data through integrated processing, the system achieves enhanced gesture detection capability while managing complexity through consolidation rather than separate processing channels.
3Ease of operation
If gesture confirmation indicators are provided, then user experience is improved, but processing time increases
Solution Approach 1:
The system provides gesture confirmation indicators through periodic feedback mechanisms that occur at optimal moments in the gesture interaction flow. By timing confirmations to coincide with natural user expectations rather than providing continuous feedback, the system improves user experience while minimizing processing time through selective, rhythmically-spaced feedback delivery.
Solution Approach 2:
The gesture confirmation system operates autonomously based on pre-established recognition parameters and user profiles. The system self-determines when and how to provide confirmation without requiring additional user input or complex real-time decision-making, thereby improving user experience while keeping processing time minimal through automated, pre-configured feedback logic.
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 solution enhances the accuracy and usability of gesture recognition in head-wearable audio devices, enabling users to perform gestures more effectively, regardless of their ability to use capacitive touch sensors, and providing customizable settings for improved user experience.
Implementation Method 1
a motion sensor that is configured to detect a motion event and output one or more motion parameter values associated with the detected motion event
Implementation Method 2
a capacitive touch sensor that is configured to detect a capacitive touch event and output one or more capacitive touch parameter values that are associated with the detected capacitive touch event
Data Source
AI summary
A head-wearable audio device has a motion sensor that outputs motion parameter values based on a detected motion gesture. A gesture type is recognized by comparing the motion parameter values and per-user per-gesture-type recognition parameter values. An action is selected based on the selected gesture type and is executed by the head-wearable audio device or an application in a remote computing device. The head-wearable audio device may also include a capacitive touch sensor that detects capacitive touch events and outputs capacitive touch parameter values. A gesture type may be recognized by comparing both of detected motion parameter values and detected capacitive touch parameter values with per-user per-gesture-type recognition parameter values. The per-user per-gesture-type recognition parameter values are improved over time using machine learning and/or automated logic based on historical detected motion or capacitive touch parameters and corresponding selected gestures, or user profile data.