Context-Dependent Gesture Recognition for Task Automation
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Solution Overview
Problem
Conventional electronic devices lack effective methods to automate tasks in challenging environments or situations, such as construction sites or while operating a vehicle, where traditional input methods like dialing numbers are impractical.
Innovation Solution
The implementation of context-dependent gestures, where predefined movements relative to an electronic device, sensed by components like accelerometers, trigger specific actions based on associated context information, stored as triplets (context, gesture, action), allowing for task automation without accidental commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional input methods like dialing numbers are used, then device operation is simple and reliable, but task automation in challenging environments is impractical
Solution Approach 1:
The patent replaces conventional mechanical input methods (dialing, button pressing) with gesture-based input detected by sensors like accelerometers and gyroscopes. Users perform gestures such as shaking, tapping, or swirling the device, and the sensor system translates these physical movements into command signals, enabling hands-free operation in challenging environments like construction sites or vehicles.
Solution Approach 2:
The device performs automation tasks autonomously based on detected gestures and associated context information. The system automatically executes predefined actions such as making phone calls, sending messages, or controlling media playback without requiring manual input for each task step, thereby extending automation capability while maintaining operational simplicity.
2Measurement precision
If gesture recognition is implemented without context information, then gesture input is simple, but accidental commands increase and precision decreases
Solution Approach 1:
The patent introduces context information as an intermediary element that mediates between the detected gesture and the executed action. Context data (such as location, time, device orientation, or application state) is combined with gesture detection to determine whether a gesture should trigger a predefined action, thereby filtering out accidental commands while maintaining gesture input simplicity.
Solution Approach 2:
The system dynamically adjusts gesture recognition parameters based on context information. For example, the threshold for detecting a valid shake gesture may vary depending on the device's location or current state. This contextual parameter adjustment allows the system to maintain high recognition precision across different scenarios without requiring complex processing for each individual case.
3Adaptability or versatility
If multiple gestures are recognized for different actions, then task automation capability increases, but device complexity and processing requirements increase
Solution Approach 1:
The patent segments gesture recognition into distinct categories or types of gestures (e.g., shaking, tapping, swirling, sliding). Each gesture type is associated with specific action rules and context parameters, allowing the system to handle multiple actions through a modular framework. This segmentation reduces processing complexity by treating each gesture type independently rather than analyzing every possible movement pattern.
Solution Approach 2:
The system implements a universal gesture recognition framework that can accommodate multiple gesture types and actions through a single integrated architecture. The same sensor suite and processing logic handle diverse gestures (shake to cancel, tap to select, swirl to rotate), enabling versatile task automation without proportionally increasing 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
Enables efficient task automation, like making phone calls or starting/stopping timers, by differentiating gestures based on context, thus enhancing user accessibility and reducing accidental commands in diverse environments.
Implementation Method 1
sensed by components like accelerometers
Data Source
AI summary
At least some exemplary embodiments of the invention enable the use of context-dependent gestures, for example, in order to assist in the automation of one or more tasks. In one exemplary embodiment, an apparatus senses a predefined gesture and, in conjunction with context information (e.g., location information), performs a predefined action in response to the gesture. As non-limiting examples, the gesture may involve movement of the apparatus (e.g., shaking, tapping) or movement relative to the apparatus (e.g., using a touch screen). In one exemplary embodiment of the invention, a method includes: obtaining context information for an apparatus, wherein the context information includes a predefined context; and in response to sensing a predefined movement associated with the predefined context, performing, by the apparatus, a predefined action, wherein the predefined movement includes a movement of or in relation to the apparatus.