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chore: add more info to README

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Skudalen 2021-07-08 17:47:41 +02:00
parent 9c030fa2dc
commit 060507bc78
3 changed files with 33 additions and 18 deletions
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4
Handle_emg_data.py
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@ -486,7 +486,7 @@ class CSV_handler:
samplerate = get_samplerate(data_frame)
return data_frame, samplerate
# NOT IMPLEMENTED
'''
def get_keyboard_data(self, filename:str, pres_or_release:str='pressed'):
filepath = self.working_dir + str(filename)
@ -496,7 +496,7 @@ class CSV_handler:
else
'''
class DL_data_handler:
class NN_handler:
JSON_PATH_REG = "reg_data.json"
JSON_PATH_MFCC = "mfcc_data.json"

6
Present_data.py
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@ -220,9 +220,9 @@ def main():
csv_handler = CSV_handler()
csv_handler.load_data('soft')
dl_data_handler = DL_data_handler(csv_handler)
dl_data_handler.store_mfcc_samples()
dl_data_handler.save_json_mfcc()
nn_handler = NN_handler(csv_handler)
nn_handler.store_mfcc_samples()
nn_handler.save_json_mfcc()
'''
dict = dl_data_handler.get_mfcc_samples_dict()

39
README.md
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@ -1,17 +1,32 @@
# 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 DL_handler(Handle_emg_data.py) - combined EMG DataFrame, combined MFCCs DataFrame
* Neural Network analysis in Neural_Network_Analysis.py - LSTM NN, etc.
#### 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
* Indi libs: Python_speech_features, Pywt
#### 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
#### 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. Use __init__. - 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 | |
| Present_data.py | |
| Neural_Network_Analysis.py | |