Predictive Input Interface Using EMG and Proximity Sensing
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Solution Overview
Problem
Existing input devices introduce noticeable delays in providing input to computing devices, which can impact performance in critical scenarios such as automotive control, motorsports, and assisted robotic surgery, due to inherent delays in physical interactions and signal processing.
Innovation Solution
The use of muscle activity sensors, such as electromyography (EMG), in conjunction with proximity sensors, to predict and generate input signals before physical interaction with input elements, reducing delays by detecting muscle signals associated with intended actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional physical input devices are used, then the device structure is simple and reliable, but the input delay is noticeable and impacts performance
Solution Approach 1:
The system performs preliminary detection of muscle activity signals before the actual physical interaction with input elements. By detecting EMG signals that precede voluntary muscle contractions, the system can predict and prepare for upcoming inputs, generating output signals before the user physically presses buttons or moves joysticks, thereby reducing input delay
Solution Approach 2:
The patent replaces traditional mechanical input detection (physical button presses, joystick movements) with biological signal detection using EMG sensors. This substitution captures electrical signals from muscle contractions, enabling the system to detect intent before physical movement occurs, thus reducing the time loss associated with mechanical interaction delays
2Loss of time
If muscle activity sensors are added to predict input, then the input delay is reduced, but the device complexity increases
Solution Approach 1:
The system introduces muscle activity sensors as intermediary detection elements that capture biological signals between the user's neural intent and physical action. These sensors act as mediators that translate muscle electrical activity into predictable input patterns, allowing the system to anticipate user actions without requiring complex direct neural interfaces
Solution Approach 2:
The system implements feedback loops that continuously monitor muscle activity signals and adjust predictions accordingly. By analyzing patterns in EMG signals and comparing them against stored reference patterns, the system refines its ability to predict upcoming inputs, improving response time while managing complexity through adaptive learning rather than static complex architecture
3Measurement precision
If multiple sensors are used for prediction, then the prediction accuracy is improved, but the ease of operation deteriorates
Solution Approach 1:
The system performs self-calibration by automatically establishing baseline muscle activity patterns during initial use and continuously refining predictions without requiring manual user configuration. The multi-sensor system self-adjusts to individual user characteristics, eliminating the need for users to manually tune sensor sensitivity or configure prediction parameters, thus maintaining ease of operation despite enhanced accuracy
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 approach significantly reduces input delays by predicting intended interactions up to 70 ms before physical engagement, enhancing responsiveness and efficiency in input devices.
Implementation Method 1
receiving, via a muscle activity sensor associated with an input device, a first signal
Data Source
AI summary
Systems and methods are provided for receiving input via an input device. A first signal is received via a muscle activity sensor. It is determined that the first signal corresponds to a movement of a user digit that does not correspond to a first interaction with an input element of the input device. A digit movement direction is identified, and a prediction of a second interaction with an input element is generated. A first confidence level associated with the prediction is below a threshold, and a second signal is received via a proximity sensor. A velocity of the user digit is determined, and a prediction of the second interaction with a first input element is made. It is identified that a second confidence level associated with the prediction is above the threshold, and a third signal associated with the first input element of the input device is generated for output.