46 lines
2.9 KiB
Markdown
46 lines
2.9 KiB
Markdown
# Analysis of Keystroke EMG data for identification
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#### EMG data handling and Neural Network analysis
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Scripts to handle CSV files composed by 2 * 8 EMG sensors(left & right) devided into sessions per subject. The raw data is organised in a CSV_handler object with Handle_emg_data.py. Processing of data can take the further form of:
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* Preprocessing with Signal_prep.py - FFT, MFCC, Wavelet db4
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* Storage for Neural Network analysis with NN_handler(Handle_emg_data.py) - combined EMG DataFrame, combined MFCCs DataFrame
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* Neural Network analysis in Neural_Network_Analysis.py - LSTM NN, etc.
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#### Technologies used
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* Common libs: Numpy, Pandas, Pathlib, Sklearn, Scipy, Matplotlib, Tensorflow, Keras
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* Community libs: Python_speech_features, Pywt
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#### Challanges in the module
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* The CSV handlig requires a specific file structure. Se "How to use it"
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* Preprocessing is still limited in Signal_prep.py
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#### Credits for insporational code
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* Kapre: Keunwoochoi
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* Audio-Classification: seth814
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* DeepLearningForAudioWithPyhton: musikalkemist
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## Table of Contents
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| File and classes | Description and help functions |
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| Handle_emg_data.py:<br><br> * Data_container<br> * CSV_handler<br> * NN_handler | Handles, manipulates, and stores data for analysis. <br><br> * Data_container is a class that describes the data for each subject in the experiment.<br> * CSV_handler takes data from CSV files and places it in Data_container for each subject.<br> Use load_data() to load csv data into data containers and add the containers to the <br> CSV_handler's 'data_container_dict', indexed by subject number. Use get_data() to retrieve <br> specific data. <br> * NN_handler prepares data for further analysis in Neural Networks. This class has storage <br> for this data and/or can save it to a json file. |
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| Signal_prep.py | Does mapping to data and contains various functions. Among others, this contains wavelet, <br>MFCC, cepstrum and normalization. |
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| Present_data.py | Contains plot and case functions. Case functions combines many elements from the code and <br>presents some results described. |
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| Neural_Network_Analysis.py | Contains functions to load, build and execute analysis with Neural Networks. Main functions are <br>load_data_from_json(), build_model(), and main() |
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## How to use it
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1. Clone the repo
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2. Place the data files in the working directory
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3. Place the data files within the `data`-folder
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(format: `/data/<datatype>/<subject-folder+ID>/<session-folder>/<left/right-CSV-files>`)
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4. Assuming NN analysis:
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1. Create a `CSV_handler` object
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2. Load data with `load_data(CSV_handler, <datatype>)`
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3. Create `NN_handler` object with `CSV_handler` as input
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4. Load MFCC data into the `NN_handler` with `store_mfcc_samples()`
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5. Run `save_json_mfcc()` to save samples in json
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6. Run `Neural_Network_Analysis.py` with desired config
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