Tremor Cancellation via Machine Learning in User Interface Devices
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Solution Overview
Problem
Individuals with tremor conditions face significant challenges using conventional human-computer interfaces, as existing tremor cancellation devices compromise user privacy and are visibly apparent, and existing solutions often require significant intervention in the computing environment.
Innovation Solution
A user interface device with a tremor learning module and tremor cancellation module that learns and corrects tremor patterns in real-time, allowing for seamless and privacy-preserving interaction by producing output indistinguishable from a tremor-free user, using machine learning techniques and polynomial regression to identify and cancel tremors within the device itself.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing tremor cancellation devices are used, then tremor reduction is achieved, but user privacy is compromised and the condition becomes visibly apparent to others
Solution Approach 1:
The patent introduces an intermediary computing device that acts as a mediator between the user interface device and the computer system. This intermediary transparently processes input data, applying tremor cancellation algorithms without the user needing to wear visible external devices or use specialized hardware that would reveal their condition to others.
Solution Approach 2:
The patent replaces physical/mechanical tremor cancellation devices (such as wearable stabilizing equipment) with a software-based solution running on a computing device. This substitution eliminates the need for visible external hardware while maintaining tremor reduction effectiveness through digital signal processing.
2Reliability
If existing tremor cancellation devices are used, then tremor reduction is achieved, but significant intervention in the computing environment is required
Solution Approach 1:
The patent creates a universal solution that works across multiple user interface devices (mouse, keyboard, trackpad, joystick) through a single computing device intermediary. The system provides multi-functionality by handling tremor cancellation for various input types without requiring device-specific modifications or complex environmental setup.
Solution Approach 2:
The computing device automatically performs tremor analysis and cancellation without requiring manual configuration or significant user intervention. The system self-adjusts by learning user input patterns and applying appropriate correction algorithms, reducing the complexity of setup and operation.
3Reliability
If adaptive filtering is applied to cancel tremor, then tremor reduction is achieved, but user input characteristics are modified
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors user input characteristics, analyzes tremor patterns, and adjusts cancellation parameters in real-time. This feedback loop ensures that tremor reduction is achieved while preserving the essential characteristics of intentional user input, as the system learns from ongoing interaction patterns.
Solution Approach 2:
The patent applies partial tremor cancellation rather than complete elimination of all input variations. By selectively filtering only the tremor frequency components while preserving other input characteristics, the system achieves effective tremor reduction without过度 modifying the user's intentional input signals.
Data Source
AI summary
A user interface device is adapted to provide tremor cancellation. The user interface device comprises a user interface for determining a position from a physical user input and a position output for providing a time-ordered output stream of position data, but also provides a tremor learning module and a tremor cancellation module. The tremor learning module can be trained to identify tremor patterns for a user by comparing time-ordered output streams of position data produced by the user with predetermined representations. The tremor cancellation module is adapted to apply the tremor patterns learned for the user to cancel tremors in a time-ordered stream of position data produced by the user to create an output stream of position data which is corrected for user tremor. A method of training and then using such a user interface device is also described.