├── .gitignore ├── README.md ├── cfgs ├── db1.yaml └── db2.yaml ├── dataloaders ├── db1.py ├── db2.py └── ninapro.py └── processing ├── extractFile_db1.ipynb ├── extractFile_db2.ipynb ├── process_db1.ipynb ├── process_db2.ipynb ├── readFile_db1.ipynb └── readFile_db2.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | __pycache__ 2 | *.pyc 3 | 4 | # outputs: logs, trained models 5 | outputs/ 6 | # dataset 7 | data/ 8 | *.tar 9 | # output files 10 | *.out 11 | 12 | # vscode 13 | .vscode 14 | 15 | # PBS 16 | PBS 17 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Ninapro-dataset-processing 2 | This repository provides the processing flow of Ninapro datasets and the pytorch dataloader of the processed Ninapro data. 3 | 4 | ## Ninapro Dataset Processing 5 | The experiment are taken on the [Ninapro dataset](http://ninaweb.hevs.ch/). The first sub-dataset DB1 and second sub-dataset DB2 are ultilized. 6 | 1. Firstly download the [Ninapro DB1](http://ninaweb.hevs.ch/data1) and [Ninapro DB2](http://ninaweb.hevs.ch/data2) datasets. And then extract the data files from the zip files. We provide two jupyter notebooks [extractFile_db1](https://github.com/increase24/Ninapro-dataset-processing/blob/master/processing/extractFile_db1.ipynb) / [extractFile_db2](https://github.com/increase24/Ninapro-dataset-processing/blob/master/processing/extractFile_db2.ipynb) under the directory **processing** for extracting DB1 / DB2 respectively. 7 | After extraction, your directory tree should look like this: 8 | 9 | ``` 10 | ${ROOT}/data/ninapro 11 | ├── db1 12 | | |—— s1 13 | | |—— s2 14 | | | ... 15 | | └── s27 16 | | |—— S27_A1_E1.mat 17 | | |—— S27_A1_E2.mat 18 | | └── S27_A1_E3.mat 19 | └── db2 20 | |—— DB2_s1 21 | |—— DB2_s2 22 | | ... 23 | └── DB2_s40 24 | |—— S40_E1_A1.mat 25 | |—— S40_E2_A1.mat 26 | └── S40_E3_A1.mat 27 | ``` 28 | 29 | 2. Secondly run the jupyter notebook script [process_db1](https://github.com/increase24/Ninapro-dataset-processing/blob/master/processing/process_db1.ipynb) / [process_db2](https://github.com/increase24/Ninapro-dataset-processing/blob/master/processing/process_db2.ipynb), which convert the mat files to txt files. 30 | After convertion, your directory tree should look like this: 31 | ``` 32 | ${ROOT}/data/ninapro 33 | ├── db1_processed 34 | | |—— s1 35 | | |—— s2 36 | | | ... 37 | | └── s27 38 | | |—— emg.txt 39 | | |—— rerepetition.txt 40 | | └── restimulus.txt 41 | └── db2_processed 42 | |—— DB2_s1 43 | |—— DB2_s2 44 | | ... 45 | └── DB2_s40 46 | |—— emg.txt 47 | |—— rerepetition.txt 48 | └── restimulus.txt 49 | ``` 50 | 51 | ## Ninapro Dataloader 52 | We provide dataloaders of Ninapro DB1 and Ninapro DB2 for pytorch network training. 53 | ### Unit testing of Ninapro DB1/DB2 dataloader 54 | ``` 55 | # Ninapro DB1 dataloader 56 | python ./dataloaders/db1.py 57 | # Ninapro DB2 dataloader 58 | python ./dataloaders/db2.py 59 | ``` 60 | ### Embeded into pytorch training code 61 | utilizing dataloader of Ninapro DB1: 62 | ``` 63 | from dataloaders.db1 import get_dataloader_db1 64 | train_loader, val_loader = get_dataloader_db1(DataConfig, path_subject) 65 | ``` 66 | utilizing dataloader of Ninapro DB1: 67 | ``` 68 | from dataloaders.db2 import get_dataloader_db2 69 | train_loader, val_loader = get_dataloader_db2(DataConfig, path_subject) 70 | ``` 71 | the configuration of **DataConfig** and **path_subject** can be seen from the unit testing of [db1.py](https://github.com/increase24/Ninapro-dataset-processing/blob/master/dataloaders/db1.py) and [db2.py](https://github.com/increase24/Ninapro-dataset-processing/blob/master/dataloaders/db2.py) 72 | 73 | ## Contact 74 | If you have any questions, feel free to contact me through jia.zeng@sjtu.edu.cn or Github issues. -------------------------------------------------------------------------------- /cfgs/db1.yaml: -------------------------------------------------------------------------------- 1 | ############################################################# 2 | # 1. Model Define Configs 3 | ############################################################# 4 | 5 | ############################################################# 6 | # 2. Optimizer & Train Configs 7 | ############################################################# 8 | 9 | ############################################################# 10 | # 3. DataSet Config 11 | ############################################################# 12 | DatasetConfig: 13 | dataset: 'db1' 14 | batch_size: 128 15 | num_workers: 6 16 | seq_lens: [20] # sample rate: 100Hz -> 1 dot: 10ms 17 | step: 1 18 | root_path: 'data/ninapro/db1_processed/' 19 | 20 | ############################################################# 21 | # 4. Output Config 22 | ############################################################# 23 | 24 | -------------------------------------------------------------------------------- /cfgs/db2.yaml: -------------------------------------------------------------------------------- 1 | ############################################################# 2 | # 1. Model Define Configs 3 | ############################################################# 4 | 5 | ############################################################# 6 | # 2. Optimizer & Train Configs 7 | ############################################################# 8 | 9 | ############################################################# 10 | # 3. DataSet Config 11 | ############################################################# 12 | DatasetConfig: 13 | dataset: 'db2' 14 | batch_size: 32 15 | num_workers: 6 16 | seq_lens: [200] # sample rate: 2000Hz -> 1 dot: 0.5ms 17 | step: 50 18 | root_path: 'data/ninapro/db2_processed/' 19 | 20 | ############################################################# 21 | # 4. Output Config 22 | ############################################################# 23 | 24 | -------------------------------------------------------------------------------- /dataloaders/db1.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.sys.path.append('.') 3 | import glob 4 | import numpy as np 5 | from scipy import signal 6 | import matplotlib.pyplot as plt 7 | from yacs.config import CfgNode as CN 8 | from torch.utils.data import DataLoader 9 | from dataloaders.ninapro import Ninapro 10 | 11 | def get_dataloader_db1(cfg, path_s): 12 | seq_lens = cfg.seq_lens 13 | step = cfg.step 14 | emgs = np.loadtxt(os.path.join(path_s, 'emg.txt')) 15 | labels = np.loadtxt(os.path.join(path_s, 'restimulus.txt')) 16 | repetitions = np.loadtxt(os.path.join(path_s, 'rerepetition.txt')) 17 | 18 | # perform 1-order 1Hz low-pass filter 19 | order = 1 20 | fs = 100 # sample rate: 100Hz 21 | cutoff = 1 # cutoff frequency 22 | nyq = 0.5 * fs 23 | normal_cutoff = cutoff / nyq 24 | b, a = signal.butter(order, normal_cutoff, 'lowpass') 25 | for _col in range(emgs.shape[1]): 26 | emgs[:,_col] = signal.filtfilt(b, a, emgs[:,_col]) 27 | 28 | # u-law normalization 29 | u = 256 30 | emgs = np.sign(emgs) * np.log(1+u*abs(emgs))/np.log(1+u) 31 | 32 | # segmentation of training and testing samples 33 | length_dots = len(labels) 34 | data_train = [] 35 | labels_train = [] 36 | data_val = [] 37 | labels_val = [] 38 | for seq_len in seq_lens: 39 | for idx in range(0, length_dots - length_dots%seq_len, step): 40 | if labels[idx]>0 and labels[idx+seq_len-1]>0 and labels[idx]==labels[idx+seq_len-1]: 41 | repetition = repetitions[idx] 42 | if repetition in [2,5,7]: # val dataset 43 | data_val.append(emgs[idx:idx+seq_len,:]) 44 | labels_val.append(labels[idx]) 45 | else: # train dataset 46 | data_train.append(emgs[idx:idx+seq_len,:]) 47 | labels_train.append(labels[idx]) 48 | trainset = Ninapro(data_train, labels_train) 49 | valset = Ninapro(data_val, labels_val) 50 | train_loader = DataLoader(trainset, batch_size=cfg.batch_size, num_workers=cfg.num_workers, pin_memory=True, shuffle=True) 51 | val_loader = DataLoader(valset, batch_size=cfg.batch_size, num_workers=cfg.num_workers, pin_memory=True, shuffle=False) 52 | return train_loader, val_loader 53 | 54 | 55 | if __name__ == "__main__": 56 | with open('./cfgs/db1.yaml') as cfg_file: 57 | cfg = CN.load_cfg(cfg_file) 58 | print('Successfully loading the config file...') 59 | dataCfg = cfg['DatasetConfig'] 60 | paths_s = glob.glob(os.path.join(dataCfg.root_path, 's1')) 61 | train_loader, val_loader = get_dataloader_db1(dataCfg, paths_s[0]) 62 | print('Successfully get dataloader of Ninapro dataset...') 63 | emg, label = iter(train_loader).next() 64 | print(emg.shape, label.shape) 65 | -------------------------------------------------------------------------------- /dataloaders/db2.py: -------------------------------------------------------------------------------- 1 | import os 2 | os.sys.path.append('.') 3 | import glob 4 | import numpy as np 5 | import pandas as pd 6 | from scipy import signal 7 | import matplotlib.pyplot as plt 8 | from yacs.config import CfgNode as CN 9 | from torch.utils.data import DataLoader 10 | from dataloaders.ninapro import Ninapro 11 | 12 | def get_dataloader_db2(cfg, path_s): 13 | seq_lens = cfg.seq_lens 14 | step = cfg.step 15 | emgs = pd.read_csv(os.path.join(path_s, 'emg.txt'), sep=' ', header=None) 16 | emgs = emgs.values 17 | labels = pd.read_csv(os.path.join(path_s, 'restimulus.txt'), header=None) 18 | labels = labels.values[:,0] 19 | repetitions = pd.read_csv(os.path.join(path_s, 'rerepetition.txt'), header=None) 20 | repetitions = repetitions.values[:,0] 21 | 22 | # # u-law normalization 23 | # u = 256 24 | # emgs = np.sign(emgs) * np.log(1+u*abs(emgs))/np.log(1+u) 25 | 26 | # min-max normalization 27 | _norm = max(abs(emgs.max()), abs(emgs.min())) 28 | emgs = emgs/_norm 29 | 30 | # segmentation of training and testing samples 31 | length_dots = len(labels) 32 | data_train = [] 33 | labels_train = [] 34 | data_val = [] 35 | labels_val = [] 36 | for seq_len in seq_lens: 37 | for idx in range(0, length_dots - length_dots%seq_len, step): 38 | if labels[idx]>0 and labels[idx+seq_len-1]>0 and labels[idx]==labels[idx+seq_len-1]: 39 | repetition = repetitions[idx] 40 | if repetition in [2,5]: # val dataset 41 | data_val.append(emgs[idx:idx+seq_len,:]) 42 | labels_val.append(labels[idx]) 43 | else: # train dataset #[1,3,4,6] 44 | data_train.append(emgs[idx:idx+seq_len,:]) 45 | labels_train.append(labels[idx]) 46 | trainset = Ninapro(data_train, labels_train) 47 | valset = Ninapro(data_val, labels_val) 48 | train_loader = DataLoader(trainset, batch_size=cfg.batch_size, num_workers=cfg.num_workers, shuffle=True) 49 | val_loader = DataLoader(valset, batch_size=cfg.batch_size, num_workers=cfg.num_workers, shuffle=False) 50 | return train_loader, val_loader 51 | 52 | 53 | if __name__ == "__main__": 54 | with open('./cfgs/db2.yaml') as cfg_file: 55 | cfg = CN.load_cfg(cfg_file) 56 | print('Successfully loading the config file...') 57 | dataCfg = cfg['DatasetConfig'] 58 | paths_s = glob.glob(os.path.join(dataCfg.root_path, 'DB2_s1')) 59 | train_loader, val_loader = get_dataloader_db2(dataCfg, paths_s[0]) 60 | print('Successfully get dataloader of Ninapro dataset...') 61 | emg, label = iter(train_loader).next() 62 | print(emg.shape, label.shape) 63 | -------------------------------------------------------------------------------- /dataloaders/ninapro.py: -------------------------------------------------------------------------------- 1 | import os 2 | import numpy as np 3 | from torch.utils.data import Dataset 4 | import torch 5 | 6 | class Ninapro(Dataset): 7 | def __init__(self, emgs, labels) -> None: 8 | super().__init__() 9 | self.emgs = emgs 10 | self.labels = labels 11 | 12 | def __len__(self): 13 | return len(self.labels) 14 | 15 | def __getitem__(self, index): 16 | sample = self.emgs[index] 17 | sample = torch.tensor(sample).float() 18 | sample = sample.permute(1,0).unsqueeze(0) 19 | label = self.labels[index]-1 20 | label = torch.tensor(label).long() 21 | return sample, label -------------------------------------------------------------------------------- /processing/extractFile_db1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "source": [ 7 | "import os\n", 8 | "import numpy as np\n", 9 | "import glob" 10 | ], 11 | "outputs": [], 12 | "metadata": {} 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "source": [ 18 | "zipFiles = sorted(glob.glob('../data/ninapro/db1/*.zip'))" 19 | ], 20 | "outputs": [], 21 | "metadata": {} 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "source": [ 27 | "for zipFile in zipFiles:\n", 28 | " dir_subject = zipFile.split('.zip')[0]\n", 29 | " if not os.path.exists(dir_subject):\n", 30 | " os.makedirs(dir_subject)\n", 31 | " os.system('unzip -n {} -d {}/'.format(zipFile, dir_subject))" 32 | ], 33 | "outputs": [], 34 | "metadata": {} 35 | } 36 | ], 37 | "metadata": { 38 | "orig_nbformat": 4, 39 | "language_info": { 40 | "name": "python", 41 | "version": "3.7.10", 42 | "mimetype": "text/x-python", 43 | "codemirror_mode": { 44 | "name": "ipython", 45 | "version": 3 46 | }, 47 | "pygments_lexer": "ipython3", 48 | "nbconvert_exporter": "python", 49 | "file_extension": ".py" 50 | }, 51 | "kernelspec": { 52 | "name": "python3", 53 | "display_name": "Python 3.7.10 64-bit ('torchlts': conda)" 54 | }, 55 | "interpreter": { 56 | "hash": "3e4932aeea9803c1af62998b3f994b61f47b3904cfcb9818f6cbb04aa1244e7f" 57 | } 58 | }, 59 | "nbformat": 4, 60 | "nbformat_minor": 2 61 | } -------------------------------------------------------------------------------- /processing/extractFile_db2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "source": [ 7 | "import os\n", 8 | "import numpy as np\n", 9 | "import glob" 10 | ], 11 | "outputs": [], 12 | "metadata": {} 13 | }, 14 | { 15 | "cell_type": "code", 16 | "execution_count": 2, 17 | "source": [ 18 | "zipFiles = sorted(glob.glob('../data/ninapro/db2/*.zip'))" 19 | ], 20 | "outputs": [], 21 | "metadata": {} 22 | }, 23 | { 24 | "cell_type": "code", 25 | "execution_count": 3, 26 | "source": [ 27 | "for zipFile in zipFiles:\n", 28 | " dir_subject = os.path.dirname(zipFile)\n", 29 | " print(dir_subject)\n", 30 | " os.system('unzip -n {} -d {}/'.format(zipFile, dir_subject))" 31 | ], 32 | "outputs": [], 33 | "metadata": {} 34 | } 35 | ], 36 | "metadata": { 37 | "orig_nbformat": 4, 38 | "language_info": { 39 | "name": "python", 40 | "version": "3.7.10", 41 | "mimetype": "text/x-python", 42 | "codemirror_mode": { 43 | "name": "ipython", 44 | "version": 3 45 | }, 46 | "pygments_lexer": "ipython3", 47 | "nbconvert_exporter": "python", 48 | "file_extension": ".py" 49 | }, 50 | "kernelspec": { 51 | "name": "python3", 52 | "display_name": "Python 3.7.10 64-bit ('torchlts': conda)" 53 | }, 54 | "interpreter": { 55 | "hash": "3e4932aeea9803c1af62998b3f994b61f47b3904cfcb9818f6cbb04aa1244e7f" 56 | } 57 | }, 58 | "nbformat": 4, 59 | "nbformat_minor": 2 60 | } -------------------------------------------------------------------------------- /processing/process_db1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "source": [ 7 | "import os\n", 8 | "import numpy as np\n", 9 | "import glob\n", 10 | "import scipy.io as scio" 11 | ], 12 | "outputs": [], 13 | "metadata": {} 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "source": [ 19 | "def process_dataset_for_oneSubject(_subject):\n", 20 | " data_paths = sorted(glob.glob(f'../data/ninapro/db1/{_subject}/*.mat'))\n", 21 | " EMGData1 = scio.loadmat(data_paths[0])\n", 22 | " EMGData2 = scio.loadmat(data_paths[1])\n", 23 | " EMGData3 = scio.loadmat(data_paths[2])\n", 24 | " emg1 = EMGData1['emg']\n", 25 | " restimulus1 = EMGData1['restimulus']\n", 26 | " rerepetition1 = EMGData1['rerepetition']\n", 27 | " emg2 = EMGData2['emg']\n", 28 | " restimulus2 = EMGData2['restimulus']\n", 29 | " restimulus2 = restimulus2 + restimulus1.max() * (restimulus2>0).astype('int')\n", 30 | " rerepetition2 = EMGData2['rerepetition']\n", 31 | " emg3 = EMGData3['emg']\n", 32 | " restimulus3 = EMGData3['restimulus']\n", 33 | " restimulus3 = restimulus3 + restimulus2.max() * (restimulus3>0).astype('int')\n", 34 | " rerepetition3 = EMGData3['rerepetition']\n", 35 | " emg = np.vstack([emg1,emg2,emg3])\n", 36 | " restimulus = np.vstack([restimulus1,restimulus2,restimulus3]).astype('int')\n", 37 | " rerepetition = np.vstack([rerepetition1,rerepetition2,rerepetition3]).astype('int')\n", 38 | " if not os.path.exists('../data/ninapro/db1_processed/{}'.format(_subject)):\n", 39 | " os.makedirs('../data/ninapro/db1_processed/{}'.format(_subject))\n", 40 | " np.savetxt('../data/ninapro/db1_processed/{}/emg.txt'.format(_subject), emg)\n", 41 | " np.savetxt('../data/ninapro/db1_processed/{}/restimulus.txt'.format(_subject), restimulus, fmt=\"%d\")\n", 42 | " np.savetxt('../data/ninapro/db1_processed/{}/rerepetition.txt'.format(_subject), rerepetition, fmt=\"%d\")" 43 | ], 44 | "outputs": [], 45 | "metadata": {} 46 | }, 47 | { 48 | "cell_type": "code", 49 | "execution_count": 3, 50 | "source": [ 51 | "_subjects = sorted(os.listdir('../data/ninapro/db1/'))\n", 52 | "_subjects = [_subject for _subject in _subjects if not _subject.endswith('zip')]\n", 53 | "for _subject in _subjects:\n", 54 | " process_dataset_for_oneSubject(_subject)" 55 | ], 56 | "outputs": [], 57 | "metadata": {} 58 | } 59 | ], 60 | "metadata": { 61 | "orig_nbformat": 4, 62 | "language_info": { 63 | "name": "python", 64 | "version": "3.7.10", 65 | "mimetype": "text/x-python", 66 | "codemirror_mode": { 67 | "name": "ipython", 68 | "version": 3 69 | }, 70 | "pygments_lexer": "ipython3", 71 | "nbconvert_exporter": "python", 72 | "file_extension": ".py" 73 | }, 74 | "kernelspec": { 75 | "name": "python3", 76 | "display_name": "Python 3.7.10 64-bit ('torchlts': conda)" 77 | }, 78 | "interpreter": { 79 | "hash": "3e4932aeea9803c1af62998b3f994b61f47b3904cfcb9818f6cbb04aa1244e7f" 80 | } 81 | }, 82 | "nbformat": 4, 83 | "nbformat_minor": 2 84 | } -------------------------------------------------------------------------------- /processing/process_db2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "source": [ 7 | "import os\n", 8 | "import numpy as np\n", 9 | "import glob\n", 10 | "import scipy.io as scio" 11 | ], 12 | "outputs": [], 13 | "metadata": {} 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": null, 18 | "source": [ 19 | "def process_dataset_for_oneSubject(_subject):\n", 20 | " data_paths = sorted(glob.glob(f'../data/ninapro/db2/{_subject}/*.mat'))\n", 21 | " EMGData1 = scio.loadmat(data_paths[0])\n", 22 | " EMGData2 = scio.loadmat(data_paths[1])\n", 23 | " EMGData3 = scio.loadmat(data_paths[2])\n", 24 | " #emg1 = EMGData1['emg']\n", 25 | " restimulus1 = EMGData1['restimulus']\n", 26 | " #rerepetition1 = EMGData1['rerepetition']\n", 27 | " #emg2 = EMGData2['emg']\n", 28 | " restimulus2 = EMGData2['restimulus']\n", 29 | " #rerepetition2 = EMGData2['rerepetition']\n", 30 | " #emg3 = EMGData3['emg']\n", 31 | " restimulus3 = EMGData3['restimulus']\n", 32 | " #rerepetition3 = EMGData3['rerepetition']\n", 33 | " #emg = np.vstack([emg1,emg2,emg3])\n", 34 | " restimulus = np.vstack([restimulus1,restimulus2,restimulus3]).astype('int')\n", 35 | " #rerepetition = np.vstack([rerepetition1,rerepetition2,rerepetition3]).astype('int')\n", 36 | " # if not os.path.exists('../data/ninapro/db2_processed/{}'.format(_subject)):\n", 37 | " # os.makedirs('../data/ninapro/db2_processed/{}'.format(_subject))\n", 38 | " #np.savetxt('../data/ninapro/db2_processed/{}/emg.txt'.format(_subject), emg)\n", 39 | " np.savetxt('../data/ninapro/db2_processed/{}/restimulus.txt'.format(_subject), restimulus, fmt=\"%d\")\n", 40 | " #np.savetxt('../data/ninapro/db2_processed/{}/rerepetition.txt'.format(_subject), rerepetition, fmt=\"%d\")" 41 | ], 42 | "outputs": [], 43 | "metadata": {} 44 | }, 45 | { 46 | "cell_type": "code", 47 | "execution_count": null, 48 | "source": [ 49 | "_subjects = sorted(os.listdir('../data/ninapro/db2/'))\n", 50 | "_subjects = [_subject for _subject in _subjects if _subject.startswith('DB2_s')]\n", 51 | "for _subject in _subjects:\n", 52 | " process_dataset_for_oneSubject(_subject)" 53 | ], 54 | "outputs": [], 55 | "metadata": {} 56 | } 57 | ], 58 | "metadata": { 59 | "orig_nbformat": 4, 60 | "language_info": { 61 | "name": "python", 62 | "version": "3.7.10", 63 | "mimetype": "text/x-python", 64 | "codemirror_mode": { 65 | "name": "ipython", 66 | "version": 3 67 | }, 68 | "pygments_lexer": "ipython3", 69 | "nbconvert_exporter": "python", 70 | "file_extension": ".py" 71 | }, 72 | "kernelspec": { 73 | "name": "python3", 74 | "display_name": "Python 3.7.10 64-bit ('torchlts': conda)" 75 | }, 76 | "interpreter": { 77 | "hash": "3e4932aeea9803c1af62998b3f994b61f47b3904cfcb9818f6cbb04aa1244e7f" 78 | } 79 | }, 80 | "nbformat": 4, 81 | "nbformat_minor": 2 82 | } -------------------------------------------------------------------------------- /processing/readFile_db1.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "source": [ 7 | "import os\n", 8 | "import numpy as np\n", 9 | "import glob\n", 10 | "import scipy.io as scio" 11 | ], 12 | "outputs": [], 13 | "metadata": {} 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "source": [ 19 | "data_path1 = '../data/ninapro/db1/s1/S1_A1_E1.mat'\n", 20 | "data_path2 = '../data/ninapro/db1/s1/S1_A1_E2.mat'\n", 21 | "data_path3 = '../data/ninapro/db1/s1/S1_A1_E3.mat'\n", 22 | "EMGData1 = scio.loadmat(data_path1)\n", 23 | "EMGData2 = scio.loadmat(data_path2)\n", 24 | "EMGData3 = scio.loadmat(data_path3)\n", 25 | "print(EMGData1.keys())" 26 | ], 27 | "outputs": [ 28 | { 29 | "output_type": "stream", 30 | "name": "stdout", 31 | "text": [ 32 | "dict_keys(['__header__', '__version__', '__globals__', 'emg', 'stimulus', 'glove', 'subject', 'exercise', 'repetition', 'restimulus', 'rerepetition'])\n" 33 | ] 34 | } 35 | ], 36 | "metadata": {} 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 3, 41 | "source": [ 42 | "emg1 = EMGData1['emg']\n", 43 | "restimulus1 = EMGData1['restimulus']\n", 44 | "rerepetition1 = EMGData1['rerepetition']\n", 45 | "emg2 = EMGData2['emg']\n", 46 | "restimulus2 = EMGData2['restimulus']\n", 47 | "restimulus2 = restimulus2 + restimulus1.max() * (restimulus2>0).astype('int')\n", 48 | "rerepetition2 = EMGData2['rerepetition']\n", 49 | "emg3 = EMGData3['emg']\n", 50 | "restimulus3 = EMGData3['restimulus']\n", 51 | "restimulus3 = restimulus3 + restimulus2.max() * (restimulus3>0).astype('int')\n", 52 | "rerepetition3 = EMGData3['rerepetition']\n", 53 | "print(emg1.shape, emg2.shape, emg3.shape)\n", 54 | "print(restimulus1.shape, restimulus2.shape, restimulus3.shape)\n", 55 | "print(rerepetition1.shape, rerepetition2.shape, rerepetition3.shape)\n", 56 | "emg = np.vstack([emg1,emg2,emg3])\n", 57 | "restimulus = np.vstack([restimulus1,restimulus2,restimulus3])\n", 58 | "rerepetition = np.vstack([rerepetition1,rerepetition2,rerepetition3])\n", 59 | "print(emg.shape)\n", 60 | "print(restimulus.shape)\n", 61 | "print(rerepetition.shape)" 62 | ], 63 | "outputs": [ 64 | { 65 | "output_type": "stream", 66 | "name": "stdout", 67 | "text": [ 68 | "(101014, 10) (142976, 10) (227493, 10)\n", 69 | "(101014, 1) (142976, 1) (227493, 1)\n", 70 | "(101014, 1) (142976, 1) (227493, 1)\n", 71 | "(471483, 10)\n", 72 | "(471483, 1)\n", 73 | "(471483, 1)\n" 74 | ] 75 | } 76 | ], 77 | "metadata": {} 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 4, 82 | "source": [ 83 | "print(EMGData1['restimulus'].min(), EMGData1['restimulus'].max(), \n", 84 | "EMGData2['restimulus'].min(),EMGData2['restimulus'].max(), \n", 85 | "EMGData3['restimulus'].min(),EMGData3['restimulus'].max())\n" 86 | ], 87 | "outputs": [ 88 | { 89 | "output_type": "stream", 90 | "name": "stdout", 91 | "text": [ 92 | "0 12 0 17 0 23\n" 93 | ] 94 | } 95 | ], 96 | "metadata": {} 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 5, 101 | "source": [ 102 | "import matplotlib.pyplot as plt\n", 103 | "from scipy.signal import resample\n", 104 | "plt.figure(figsize=(12,8))\n", 105 | "plt.subplot(211)\n", 106 | "sample_dots = len(restimulus)\n", 107 | "indices = [i for i in range(0, sample_dots, 1)]\n", 108 | "plt.plot(indices, restimulus)\n", 109 | "plt.legend(['restimulus'])\n", 110 | "plt.subplot(212)\n", 111 | "sample_dots = len(rerepetition)\n", 112 | "indices = [i for i in range(0, sample_dots, 1)]\n", 113 | "plt.plot(indices, rerepetition)\n", 114 | "plt.legend(['rerepetition'])\n", 115 | "plt.show()\n", 116 | "\n", 117 | "\n", 118 | "sub_length = 30000\n", 119 | "plt.figure(figsize=(12,8))\n", 120 | "plt.subplot(211)\n", 121 | "sample_dots = len(restimulus)\n", 122 | "indices = [i for i in range(0, sample_dots, 1)]\n", 123 | "plt.plot(indices[0:sub_length], restimulus[0:sub_length])\n", 124 | "plt.legend(['restimulus'])\n", 125 | "plt.subplot(212)\n", 126 | "sample_dots = len(rerepetition)\n", 127 | "indices = [i for i in range(0, sample_dots, 1)]\n", 128 | "plt.plot(indices[0:sub_length], rerepetition[0:sub_length])\n", 129 | "plt.legend(['rerepetition'])\n", 130 | "plt.show()" 131 | ], 132 | "outputs": [ 133 | { 134 | "output_type": "display_data", 135 | "data": { 136 | "image/png": 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", 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", 149 | "text/plain": [ 150 | "
" 151 | ] 152 | }, 153 | "metadata": { 154 | "needs_background": "light" 155 | } 156 | } 157 | ], 158 | "metadata": {} 159 | } 160 | ], 161 | "metadata": { 162 | "orig_nbformat": 4, 163 | "language_info": { 164 | "name": "python", 165 | "version": "3.7.10", 166 | "mimetype": "text/x-python", 167 | "codemirror_mode": { 168 | "name": "ipython", 169 | "version": 3 170 | }, 171 | "pygments_lexer": "ipython3", 172 | "nbconvert_exporter": "python", 173 | "file_extension": ".py" 174 | }, 175 | "kernelspec": { 176 | "name": "python3", 177 | "display_name": "Python 3.7.10 64-bit ('torchlts': conda)" 178 | }, 179 | "interpreter": { 180 | "hash": "3e4932aeea9803c1af62998b3f994b61f47b3904cfcb9818f6cbb04aa1244e7f" 181 | } 182 | }, 183 | "nbformat": 4, 184 | "nbformat_minor": 2 185 | } -------------------------------------------------------------------------------- /processing/readFile_db2.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "source": [ 7 | "import os\n", 8 | "import numpy as np\n", 9 | "import glob\n", 10 | "import scipy.io as scio" 11 | ], 12 | "outputs": [], 13 | "metadata": {} 14 | }, 15 | { 16 | "cell_type": "code", 17 | "execution_count": 2, 18 | "source": [ 19 | "data_path1 = '../data/ninapro/db2/DB2_s1/S1_E1_A1.mat'\n", 20 | "data_path2 = '../data/ninapro/db2/DB2_s1/S1_E2_A1.mat'\n", 21 | "data_path3 = '../data/ninapro/db2/DB2_s1/S1_E3_A1.mat'\n", 22 | "EMGData1 = scio.loadmat(data_path1)\n", 23 | "EMGData2 = scio.loadmat(data_path2)\n", 24 | "EMGData3 = scio.loadmat(data_path3)\n", 25 | "print(EMGData1.keys())" 26 | ], 27 | "outputs": [ 28 | { 29 | "output_type": "stream", 30 | "name": "stdout", 31 | "text": [ 32 | "dict_keys(['__header__', '__version__', '__globals__', 'emg', 'acc', 'stimulus', 'glove', 'inclin', 'subject', 'exercise', 'repetition', 'restimulus', 'rerepetition'])\n" 33 | ] 34 | } 35 | ], 36 | "metadata": {} 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 3, 41 | "source": [ 42 | "emg1 = EMGData1['emg']\n", 43 | "restimulus1 = EMGData1['restimulus']\n", 44 | "rerepetition1 = EMGData1['rerepetition']\n", 45 | "emg2 = EMGData2['emg']\n", 46 | "restimulus2 = EMGData2['restimulus']\n", 47 | "#restimulus2 = restimulus2 + restimulus1.max() * (restimulus2>0).astype('int')\n", 48 | "rerepetition2 = EMGData2['rerepetition']\n", 49 | "emg3 = EMGData3['emg']\n", 50 | "restimulus3 = EMGData3['restimulus']\n", 51 | "#restimulus3 = restimulus3 + restimulus2.max() * (restimulus3>0).astype('int')\n", 52 | "rerepetition3 = EMGData3['rerepetition']\n", 53 | "print(emg1.shape, emg2.shape, emg3.shape)\n", 54 | "print(restimulus1.shape, restimulus2.shape, restimulus3.shape)\n", 55 | "print(rerepetition1.shape, rerepetition2.shape, rerepetition3.shape)\n", 56 | "emg = np.vstack([emg1,emg2,emg3])\n", 57 | "restimulus = np.vstack([restimulus1,restimulus2,restimulus3])\n", 58 | "rerepetition = np.vstack([rerepetition1,rerepetition2,rerepetition3])\n", 59 | "print(emg.shape)\n", 60 | "print(restimulus.shape)\n", 61 | "print(rerepetition.shape)" 62 | ], 63 | "outputs": [ 64 | { 65 | "output_type": "stream", 66 | "name": "stdout", 67 | "text": [ 68 | "(1808331, 12) (2553289, 12) (877073, 12)\n", 69 | "(1808331, 1) (2553289, 1) (877072, 1)\n", 70 | "(1808331, 1) (2553289, 1) (877072, 1)\n", 71 | "(5238693, 12)\n", 72 | "(5238692, 1)\n", 73 | "(5238692, 1)\n" 74 | ] 75 | } 76 | ], 77 | "metadata": {} 78 | }, 79 | { 80 | "cell_type": "code", 81 | "execution_count": 4, 82 | "source": [ 83 | "print(EMGData1['restimulus'].min(), EMGData1['restimulus'].max(), \n", 84 | "EMGData2['restimulus'].min(),EMGData2['restimulus'].max(), \n", 85 | "EMGData3['restimulus'].min(),EMGData3['restimulus'].max())\n" 86 | ], 87 | "outputs": [ 88 | { 89 | "output_type": "stream", 90 | "name": "stdout", 91 | "text": [ 92 | "0 17 0 40 0 49\n" 93 | ] 94 | } 95 | ], 96 | "metadata": {} 97 | }, 98 | { 99 | "cell_type": "code", 100 | "execution_count": 5, 101 | "source": [ 102 | "import matplotlib.pyplot as plt\n", 103 | "from scipy.signal import resample\n", 104 | "plt.figure(figsize=(12,8))\n", 105 | "plt.subplot(211)\n", 106 | "sample_dots = len(restimulus)\n", 107 | "indices = [i for i in range(0, sample_dots, 1)]\n", 108 | "plt.plot(indices, restimulus)\n", 109 | "plt.legend(['restimulus'])\n", 110 | "plt.subplot(212)\n", 111 | "sample_dots = len(rerepetition)\n", 112 | "indices = [i for i in range(0, sample_dots, 1)]\n", 113 | "plt.plot(indices, rerepetition)\n", 114 | "plt.legend(['rerepetition'])\n", 115 | "plt.show()\n", 116 | "\n", 117 | "\n", 118 | "sub_length = 600000\n", 119 | "plt.figure(figsize=(12,8))\n", 120 | "plt.subplot(211)\n", 121 | "sample_dots = len(restimulus)\n", 122 | "indices = [i for i in range(0, sample_dots, 1)]\n", 123 | "plt.plot(indices[0:sub_length], restimulus[0:sub_length])\n", 124 | "plt.legend(['restimulus'])\n", 125 | "plt.subplot(212)\n", 126 | "sample_dots = len(rerepetition)\n", 127 | "indices = [i for i in range(0, sample_dots, 1)]\n", 128 | "plt.plot(indices[0:sub_length], rerepetition[0:sub_length])\n", 129 | "plt.legend(['rerepetition'])\n", 130 | "plt.show()" 131 | ], 132 | "outputs": [ 133 | { 134 | "output_type": "display_data", 135 | "data": { 136 | "image/png": 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", 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", 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