Analysis of Keystroke EMG data for identification

EMG data handling and Neural Network analysis

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:

• Preprocessing with Signal_prep.py - FFT, MFCC, Wavelet db4
• Storage for Neural Network analysis with NN_handler(Handle_emg_data.py) - combined EMG DataFrame, combined MFCCs DataFrame
• Neural Network analysis in Neural_Network_Analysis.py - LSTM NN, etc.

Technologies used

• Common libs: Numpy, Pandas, Pathlib, Sklearn, Scipy, Matplotlib, Tensorflow, Keras
• Community libs: Python_speech_features, Pywt

Challanges in the module

• The CSV handlig is for the moment hard-coded to fit the current project due to a very specific file structure and respective naming convention.
• Preprocessing is still limited in Signal_prep.py
• Neural_Network_Analysis.py lacks a more general way to access multiple types of networks

Credits for insporational code

• Kapre: Keunwoochoi
• Audio-Classification: seth814
• DeepLearningForAudioWithPyhton: musikalkemist

Table of Contents

File and classes	Description and help functions
Handle_emg_data.py: * Data_container * CSV_handler * NN_handler	Handles, manipulates, and stores data for analysis. * Data_container is a class that describes the data for each subject in the experiment. * CSV_handler takes data from CSV files and places it in Data_container for each subject. Use load_data() to load csv data into data containers and add the containers to the CSV_handler's 'data_container_dict', indexed by subject number. Use get_data() to retrieve specific data. * NN_handler prepares data for further analysis in Neural Networks. This class has storage for this data and/or can save it to a json file.
Signal_prep.py	Does mapping to data and contains various functions. Among others, this contains wavelet, MFCC, cepstrum and normalization.
Present_data.py	Contains plot and case functions. Case functions combines many elements from the code and presents some results described.
Neural_Network_Analysis.py	Contains functions to load, build and execute analysis with Neural Networks. Main functions are load_data_from_json(), build_model(), and main()

How to use it

1. Clone the repo
2. Place the data files in the working directory
3. (For now) Add the session filenames in the desired load_data() function
4. Assuming NN analysis:
   1. Create a CSV_handler object
   2. Load data with load_data(CSV_handler, <datatype>)
   3. Create NN_handler object with CSV_handler as input
   4. Load MFCC data into the NN_handler with store_mfcc_samples()
   5. Run save_json_mfcc() to save samples in json
   6. Run Neural_Network_Analysis.py with desired config