Input Device Command Compensation Using Machine Learning
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Solution Overview
Problem
Users with impaired motor skills face challenges in operating input devices due to involuntary commands, leading to frustration and reduced accessibility.
Innovation Solution
A computer-implemented method that detects involuntary user commands using machine learning and applies compensation actions, such as force-feedback or soft-feedback, to modify or inhibit these commands, improving device operation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional input devices are used without compensation mechanisms, then the device structure remains simple and responsive, but users with impaired motor skills experience frustration and reduced accessibility due to involuntary commands
Solution Approach 1:
A compensation system acts as an intermediary layer between the user and the electronic device. This system includes a processor that receives input signals from the input device, determines whether they are involuntary commands using machine learning models, and applies compensation actions to generate corrected output signals. This intermediary processing layer enables accessibility improvements without fundamentally redesigning the physical input device structure.
Solution Approach 2:
The patent replaces traditional mechanical or direct signal transmission systems with an intelligent software-based compensation system. Instead of modifying the physical input device mechanics, the solution uses machine learning models and signal processing algorithms to detect and correct involuntary commands, substituting mechanical simplicity with computational intelligence to achieve better accessibility.
2Reliability
If compensation actions are applied to all user commands, then involuntary commands are reduced, but legitimate user commands may be incorrectly modified causing new usability issues
Solution Approach 1:
The compensation system implements feedback mechanisms where the processor continuously monitors input signals, compares them against learned patterns of involuntary commands, and adjusts compensation actions accordingly. The system uses feedback from multiple input sources and historical data to refine its determination of involuntary versus intentional commands, improving reliability while maintaining user command accuracy through iterative learning and adjustment.
Solution Approach 2:
The system dynamically changes parameters such as threshold values, sensitivity levels, and compensation strength based on the detected characteristics of input signals. By adjusting these parameters in real-time based on the situation and user behavior patterns, the system can distinguish between involuntary commands requiring compensation and legitimate user commands that should be executed as-is, thereby maintaining both reliability and ease of operation.
Data Source
AI summary
There is described a computer-implemented method for involuntary user command compensation on an input device, the method comprising; receiving a user command from at least one input source of an input device; determining whether the user command is an involuntary user command; if it is determined that the user command is an involuntary user command, applying a compensation action to the user command; and issuing a compensated user command output based on the compensation action. There is also described a computer program product and a system.