EMG Wrist Gesture Detection for Low-Motion Text Editing
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Solution Overview
Problem
Existing wearable devices require full-range and space-consuming user movements for gesture detection, which are socially unacceptable and energy-intensive, limiting their adoption and use in artificial-reality environments.
Innovation Solution
The use of neuromuscular-signal-based detection for in-air hand gestures, such as thumb-to-finger gestures and hand movements, allows for minimal user movement and efficient command performance, reducing the need for large spaces and energy expenditure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full-range user movements are used for gesture detection, then gesture recognition accuracy is improved, but user energy expenditure and space requirements increase
Solution Approach 1:
The system detects neuromuscular signals (EMG) from the user's wrist before the actual hand gesture occurs, allowing the system to anticipate and prepare for the gesture in advance. This preliminary detection through muscle signal monitoring enables accurate gesture recognition without requiring full-range movements, thereby reducing energy expenditure while maintaining recognition accuracy.
2Measurement precision
If full-range user movements are used for gesture detection, then gesture recognition accuracy is improved, but social acceptability deteriorates
Solution Approach 1:
By detecting EMG signals from wrist muscles before the gesture is fully executed, the system can interpret intent early in the movement sequence. This allows for recognition of subtle, minimal movements that are socially acceptable while maintaining accurate gesture identification, eliminating the need for large, conspicuous full-range movements.
3Use of energy by moving object
If minimal user movements are used for gesture detection, then energy expenditure is reduced, but gesture detection capability deteriorates
Solution Approach 1:
The patent introduces neuromuscular signal detection (EMG sensors on the wrist) as an intermediary mechanism between the user's intent and the gesture execution. By monitoring muscle electrical signals, the system can detect gesture intent with high precision before the physical movement occurs, enabling accurate detection even when the subsequent physical gesture involves minimal movement.
4Measurement precision
If full-range user movements are used for text modification, then input accuracy is improved, but productivity deteriorates
Solution Approach 1:
The system detects multiple gestures in sequence, where the first gesture selects text and the second gesture modifies it. By using EMG signal detection to recognize these gestures with high accuracy through minimal movements, the system enables efficient text editing operations without requiring time-consuming full-range movements, thereby improving both accuracy and productivity.
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 enables comfortable, socially acceptable, and efficient text production and modification in AR and VR environments, enhancing user adoption and interaction with these technologies.
Implementation Method 1
detecting, using data from one or more neuromuscular-signal sensors in communication with the wearable device, an in-air hand gesture performed by the user
Data Source
AI summary
The various implementations described herein include methods and systems for producing and modifying text using neuromuscular-signal-sensing devices. In one aspect, a method includes causing the display of a plurality of text terms input by a user. Using data from one or more neuromuscular-signal sensors in communication with the wearable device, an in-air hand gesture performed by the user is detected while the text terms are displayed. In response to the in-air hand gesture, a text-modification mode is enabled that allows for modifying the text terms input by the user. A target term is identified and, while the text-modification mode is enabled, data about a voice input provided by the user for modifying the target term is received. The method further includes causing a modification to the target term in accordance with the voice input from the user.