EMG Signal Detection With Adaptive Ambient Noise Filtering
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Solution Overview
Problem
Conventional electromyography (EMG) signal detection technologies face challenges in accurately removing noise signals, such as power line interference and motion artifacts, which complicate the diagnostic process due to their reliance on pre-recorded noise databases, increasing implementation complexity.
Innovation Solution
An adaptive filtering scheme is employed to detect and remove noise signals from EMG signals using a noise signal obtaining device that acquires noise signals at a user's periphery, either through a wire as an antenna, an additional electrode, or a combination of both, and processes these signals using an adaptive filter to enhance noise removal accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods use pre-recorded noise databases to remove noise signals, then noise removal can be achieved, but implementation complexity increases
Solution Approach 1:
The patent applies preliminary action by obtaining a reference noise signal in advance from the environment around the user before EMG signal processing. This reference noise signal is stored and later used by the adaptive filter to remove noise from EMG signals, eliminating the need for complex pre-recorded noise databases while maintaining effective noise removal.
Solution Approach 2:
The patent uses copying by creating a reference copy of the ambient noise signal from the user's environment. This reference noise signal is then fed into the adaptive filter alongside the EMG signal, allowing the filter to identify and remove noise components without requiring complex noise databases or increasing implementation complexity.
2Reliability
If traditional noise removal methods are used, then some noise can be filtered, but noise signals cause errors in diagnosing user conditions
Solution Approach 1:
The patent implements feedback through an adaptive filter that continuously processes the EMG signal by comparing it with a reference noise signal. The filter dynamically adjusts to remove noise components from the EMG signal in real-time, significantly improving signal accuracy and enabling more reliable diagnostic assessments without causing errors from residual noise.
3Device complexity
If noise signals are not removed, then implementation remains simple, but power line interference and motion artifacts complicate the diagnostic process
Solution Approach 1:
The patent introduces an intermediary element - a microphone or noise sensor that captures ambient noise signals from the environment. This reference noise signal acts as a mediator that is fed into the adaptive filter, enabling automatic noise removal from EMG signals and simplifying the diagnostic process by eliminating manual noise filtering requirements.
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 allows for real-time noise signal removal from EMG signals with high accuracy, improving diagnostic precision by effectively filtering out ambient noise peaks and harmonics, as demonstrated in both low and high noise conditions.
Implementation Method 1
the noise signal obtaining device may obtain the ambient noise signal with an unknown reference frequency, which is capacitively coupled to the wire, through an arbitrary capacitance of an unknown value
Data Source
AI summary
An embodiment electromyography signal detection device includes a noise signal obtaining device configured to obtain a noise signal of an unknown reference frequency at a periphery of a user, an electromyography signal acquisition device configured to measure an electromyography signal from the user, and a controller configured to remove a noise signal included in the electromyography signal of the user based on the obtained noise signal of the unknown reference frequency.