Wearable Micro-Motion Gesture Control via Sensor Substitution
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Solution Overview
Problem
Traditional mobile computing devices require cumbersome physical user interfaces for gesture detection, which are power and processor intensive, limiting their usability in user-worn applications.
Innovation Solution
A wrist-wearable computing device that utilizes micro-motion detection based on sensors such as accelerometers, gyroscopes, and magnetometers to recognize gestures by comparing detected micro-motions to predefined input gesture models, eliminating the need for abstract features and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If physical user interfacing devices (buttons, displays) are used for gesture detection, then gesture input capability is achieved, but power consumption and processor intensity increase significantly
Solution Approach 1:
The patent replaces physical mechanical interfaces (buttons, displays) with sensor-based detection systems. Accelerometers, gyroscopes, and magnetometers detect micro-motions and gestures through physical field interactions rather than mechanical contact, eliminating the need for power-intensive physical components while maintaining gesture input capability
Solution Approach 2:
The patent uses sensor data to create digital representations of physical gestures. Instead of requiring physical buttons or displays, the system captures micro-motion patterns through sensors and processes these as gesture inputs, substituting physical interfaces with their digital equivalents that consume significantly less power
2Ease of operation
If physical user interfacing devices are used for gesture detection, then gesture input capability is achieved, but device complexity increases
Solution Approach 1:
The patent extracts the essential function of gesture detection from complex physical interfaces and isolates it to sensor-based micro-motion detection. By removing buttons, displays, and other physical components, the system achieves gesture input capability with significantly reduced interface complexity, relying only on compact sensor arrays and processing algorithms
3Measurement precision
If abstract features are used in gesture models, then gesture recognition capability is improved, but model size and computational load increase
Solution Approach 1:
The patent changes the parameters used in gesture models from abstract high-dimensional features to concrete micro-motion parameters directly measured by sensors. By using acceleration, angular velocity, and magnetic field data as model parameters, the system maintains gesture recognition accuracy while significantly reducing model size and computational requirements
Solution Approach 2:
The patent segments gesture recognition into distinct micro-motion states (e.g., wrist rotation, finger movement, hand position) that can be independently detected and processed. This segmentation allows the system to recognize gestures using smaller, specialized models for each motion type rather than one large comprehensive model, reducing overall computational load
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 and intuitive gesture-based control of user-worn devices without the need for power-intensive physical interfaces, enhancing usability and reducing model size and computational load.
Implementation Method 1
sensors such as accelerometers, gyroscopes, and magnetometers
Implementation Method 2
sensors such as accelerometers, gyroscopes, and magnetometers
Implementation Method 3
sensors such as accelerometers, gyroscopes, and magnetometers
Data Source
AI summary
Technologies for detecting micro-motion based input gestures include a wrist-wearable computing device that includes sensors from which values for micro-motion states can be determined. Each micro-motion state is indicative of a motion-related characteristic of the wrist-wearable computing device that is used to determine whether a sequence of detected gesture steps matches an input gesture model associated with an input gesture. The input gesture model defines a required sequence of required gesture steps from which an input gesture may be determined.