Gradio app, performing multiclass-classification on emg sig!
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The Multimodal Emg Signal Classifier is a Gradio application designed for performing multiclass classification on electromyography (EMG) signals. It is primarily used for pose estimation and can predict hand actions based on sensor inputs. This tool leverages advanced machine learning models to analyze EMG data and classify it into predefined categories, making it useful for applications such as gesture recognition, prosthetic control, and rehabilitation.
• Multiclass Classification: Capable of distinguishing between multiple hand actions or gestures.
• Real-Time Prediction: Provides fast and accurate results for real-time applications.
• User-Friendly Interface: Built on Gradio, offering an intuitive interface for uploading and analyzing EMG data.
• High Accuracy: Utilizes state-of-the-art algorithms to ensure reliable predictions.
• Cross-Platform Compatibility: Can be accessed and used on various devices and platforms.
What type of data does the classifier accept?
The classifier typically accepts EMG signal data in formats such as CSV or JSON, depending on the implementation.
How accurate is the Multimodal Emg Signal Classifier?
The accuracy depends on the dataset and model used, but it is designed to provide high accuracy for real-time applications.
Can the classifier be used for prosthetic control?
Yes, the classifier is suitable for applications like prosthetic control, as it can predict hand actions with high accuracy.