EMG Gesture Classification Using Multi-Channel Neural Networks
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Solution Overview
Problem
Current wearable devices using electromyogram (EMG) signals for gesture control are limited in convenience due to the restrictive methods of interaction, as users are challenged in effectively controlling devices regardless of the device's location on the body.
Innovation Solution
A gesture classification apparatus and method utilizing an EMG signal sensor with multiple channels, a processor, and an artificial neural network to extract features from EMG data, preprocess the signals, and recognize gestures through a multi-layered neural network architecture, enabling more accurate and flexible control of wearable devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional EMG-based gesture control methods are used in wearable devices, then the device can detect gestures, but the ease of operation is limited due to restrictive interaction methods and location dependencies
Solution Approach 1:
The patent divides the EMG signal processing into multiple segments: preprocessing module for noise removal and normalization, feature extraction module for generating multiple features from segmented EMG data, and classification module for gesture recognition. This segmentation allows each module to optimize its function independently, improving overall system adaptability and ease of operation.
Solution Approach 2:
The patent transitions from single-channel EMG signals to multi-channel EMG data, adding spatial dimensionality. The feature extraction module generates multiple features from the EMG data across different channels, enabling the system to detect gestures regardless of wearable device location on the body, thereby enhancing adaptability and ease of operation.
2Measurement precision
If multiple EMG channels are used to improve gesture recognition accuracy, then measurement precision improves, but device complexity increases
Solution Approach 1:
The patent extracts only the necessary features from the multi-channel EMG data through the feature extraction module. Instead of processing all raw EMG data, the system extracts salient features that are sufficient for accurate gesture recognition, reducing computational complexity while maintaining high measurement precision.
Solution Approach 2:
The system uses multiple EMG channels (excessive action) to capture comprehensive gesture information, ensuring high measurement precision. The feature extraction module then processes this data to identify the most relevant features, balancing the complexity of multi-channel data processing with the need for accurate gesture recognition.
3Productivity
If real-time EMG signal processing is implemented, then productivity improves, but use of energy increases
Solution Approach 1:
The preprocessing module performs noise removal and normalization operations in advance on the raw EMG signals. This preliminary action prepares the data for efficient feature extraction and classification, enabling real-time gesture recognition while reducing the computational burden and energy consumption during real-time processing.
Solution Approach 2:
The feature extraction module extracts only the essential features from the EMG data, eliminating redundant information. This selective extraction reduces the amount of data that needs to be processed in real-time, improving productivity while minimizing energy consumption during gesture recognition.
Data Source
AI summary
A gesture classification apparatus and method is disclosed. The apparatus may include a feature extractor configured to extract a plurality of features using a electromyogram (EMG) data group obtained from an EMG signal sensor including a plurality of channels, an artificial neural network including an input layer to which the EMG data group corresponding to the plurality of features is input and an output layer configured to output a preset gesture corresponding to the plurality of features, and a gesture recognizer configured to recognize a gesture performed by a user and corresponding to the extracted features.