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chore: updated README.

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  # Real Time Gesture Recognition Dataset
 
 
 
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  The dataset is divided into two folders: `dynamic_gestures` and `static_gestures`. The first contains loosely-timed sessions of gesture recordings where the gesture is repeated several times between intervals of rest. The second contains a single recording of each gesture, where the gesture is held for a few seconds. The gestures are: `baseline`, `fist`, `up`, `down`, `peace`, and `lift`. New gesture recordings and more gestures will be uploaded soon.
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  Every subject has two types of recordings: the raw EMG data in the `raw` folder and the FFT data in the `fft` folder.
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  Each individual recording is a CSV file dedicated to a single gesture performed by a subject.
 
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  # Real Time Gesture Recognition Dataset
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+ This repository contains the dataset for the [Real-Time-Gesture-Control-System-for-Precision-Control](https://github.com/Incomprehensible/Real-Time-Gesture-Control-System-for-Precision-Control) project that focuses on the development of a real-time gesture control system using the [uMyo EMG sensors](https://hackaday.io/project/186038-umyo-wearable-emg-sensor-with-wetdry-electrodes).
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+ ## Description
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  The dataset is divided into two folders: `dynamic_gestures` and `static_gestures`. The first contains loosely-timed sessions of gesture recordings where the gesture is repeated several times between intervals of rest. The second contains a single recording of each gesture, where the gesture is held for a few seconds. The gestures are: `baseline`, `fist`, `up`, `down`, `peace`, and `lift`. New gesture recordings and more gestures will be uploaded soon.
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  Every subject has two types of recordings: the raw EMG data in the `raw` folder and the FFT data in the `fft` folder.
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  Each individual recording is a CSV file dedicated to a single gesture performed by a subject.