EMG Silent Speech Detection With Feedback Threshold Adjustment
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Solution Overview
Problem
Existing communication systems require overt physical movements for speech input, which can invade privacy and are inefficient due to inaccuracy and interference from involuntary gestures, making it burdensome to compose messages in public or noisy environments.
Innovation Solution
An EMG communication system that processes EMG signals to remove noise and adjust detection thresholds based on user feedback, allowing silent speech interaction with devices without muscle movement, using wearable collars and machine learning to synthesize speech or text from inner thoughts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If voice message interfaces are used for communication, then users can send verbal messages, but user privacy is invaded and the system is vulnerable to interference from involuntary gestures
Solution Approach 1:
The patent replaces traditional voice-based mechanical speech recognition with an EMG-based detection system that measures electrical signals from muscle contractions. This substitution allows detection of intended speech at the neural/muscle level before actual vocalization occurs, eliminating interference from involuntary gestures and improving reliability in noisy environments.
Solution Approach 2:
The patent introduces EMG sensors as an intermediary between neural intent and external communication. By detecting electrical signals from speech-producing muscles, the system creates an intermediate detection layer that filters out involuntary gestures and provides more accurate speech intent recognition before actual speech production.
2Productivity
If traditional voice input is used, then communication can occur, but resource usage is high and accuracy is reduced due to environmental noise
Solution Approach 1:
The patent replaces acoustic-based voice recognition with electrical signal detection through EMG sensors. This substitution captures speech intent at the muscle contraction level, providing higher measurement precision that is independent of environmental noise and improving communication efficiency.
3Measurement precision
If EMG signal detection is implemented without feedback, then silent speech can be detected, but detection accuracy is reduced due to noise and threshold issues
Solution Approach 1:
The patent implements a feedback mechanism where the system presents detected silent speech to the user for confirmation or correction. This feedback loop allows the system to learn from user responses, adjust detection thresholds dynamically, and improve both measurement precision and reliability of STA detection over time.
Solution Approach 2:
The patent makes the detection threshold dynamic rather than static. By adjusting thresholds based on feedback and varying signal conditions, the system maintains high detection accuracy across different environments and user states, improving both precision and reliability.
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 private communication by reducing resource usage and improving accuracy, allowing users to interact with messaging and AR/VR devices through silent speech, enhancing device efficiency and user experience.
Implementation Method 1
Some electronics-enabled devices include various input interfaces to allow a user to communicate with other users. Such input interfaces include voice message interfaces that enable users to send verbal messages to others.
Implementation Method 2
An EMG communication system that processes EMG signals to remove noise and adjust detection thresholds based on user feedback
Implementation Method 3
using wearable collars and machine learning to synthesize speech or text from inner thoughts
Data Source
AI summary
Systems and methods are provided for performing EMG signal operations. The system accesses one or more blocks of EMG data that were generated based on a plurality of EMG channels of an EMG communication device based on one or more subthreshold activity (STA) of one or more muscles associated with speech production. The system processes the one or more blocks of the EMG data and computes a metric for each block of the one or more blocks of the EMG data that have been processed. The system determines that the metric representing at least one EMG channel of the plurality of EMG channels transgresses the threshold for detection of the STA and generates audible or visual feedback to indicate that the metric representing the at least one EMG channel of the plurality of EMG channels transgresses the threshold for detection of the STA.