scripts/README.md

Dataset Preparation

This guide provides commands to process raw EMG data into HDF5 format using sliding windows.

Usage

• Dependencies: Install requirements specific to these scripts via pip install -r scripts/requirements.txt. Framework requirements for TinyMyo are in the BioFoundation repository.
• Use --download_data if raw data is missing.
• Replace $DATA_PATH with your local storage path.
• seq_len: Window size (samples).
• stride: Step size (samples).
• Pretraining scripts use 2 kHz sampling. Downstream scripts use 200 Hz or 2 kHz.

---

Pretraining Datasets

(0.5s windows, 50% overlap @ 2 kHz)

Dataset	Size (GB)	Seq Len	Stride	Command
EMG2Pose	431	1000 (0.5s)	500	python scripts/emg2pose.py --data_dir $DATA_PATH/emg2pose_data/ --save_dir $DATA_PATH/emg2pose_data/h5/ --seq_len 1000 --stride 500
NinaPro DB6	~20	1000 (0.5s)	500	python scripts/db6.py --data_dir $DATA_PATH/ninapro/DB6/ --save_dir $DATA_PATH/ninapro/DB6/h5/ --seq_len 1000 --stride 500
NinaPro DB7	~10	1000 (0.5s)	500	python scripts/db7.py --data_dir $DATA_PATH/ninapro/DB7/ --save_dir $DATA_PATH/ninapro/DB7/h5/ --seq_len 1000 --stride 500

---

Downstream Datasets

Dataset	Metric	Seq Len	Stride	Command
NinaPro DB5	Gesture	200 (1s)	50	python scripts/db5.py --data_dir $DATA_PATH/ninapro/DB5/ --save_dir $DATA_PATH/ninapro/DB5/h5_1sec/ --seq_len 200 --stride 50 --data-augment
NinaPro DB5	Gesture	1000 (5s)	250	python scripts/db5.py --data_dir $DATA_PATH/ninapro/DB5/ --save_dir $DATA_PATH/ninapro/DB5/h5_5sec/ --seq_len 1000 --stride 250 --data-augment
EMG-EPN612	Gesture	200 (1s)	N/A	python scripts/epn.py --data_dir $DATA_PATH/EPN612/ --source_training $DATA_PATH/EPN612/trainingJSON/ --source_testing $DATA_PATH/EPN612/testingJSON/ --dest_dir $DATA_PATH/EPN612/h5_1sec/ --seq_len 200
EMG-EPN612	Gesture	1000 (5s)	N/A	python scripts/epn.py --data_dir $DATA_PATH/EPN612/ --source_training $DATA_PATH/EPN612/trainingJSON/ --source_testing $DATA_PATH/EPN612/testingJSON/ --dest_dir $DATA_PATH/EPN612/h5_5sec/ --seq_len 1000
UCI EMG	Gesture	200 (1s)	50	python scripts/uci.py --data_dir $DATA_PATH/UCI_EMG/EMG_data_for_gestures-master/ --save_dir $DATA_PATH/UCI_EMG/EMG_data_for_gestures-master/h5_1sec/ --seq_len 200 --stride 50
UCI EMG	Gesture	1000 (5s)	250	python scripts/uci.py --data_dir $DATA_PATH/UCI_EMG/EMG_data_for_gestures-master/ --save_dir $DATA_PATH/UCI_EMG/EMG_data_for_gestures-master/h5_5sec/ --seq_len 1000 --stride 250
NinaPro DB8	Regression	200 (0.1s)	200	python scripts/db8.py --data_dir $DATA_PATH/ninapro/DB8/ --save_dir $DATA_PATH/ninapro/DB8/h5_100/ --seq_len 200 --stride 200
NinaPro DB8	Regression	1000 (0.5s)	1000	python scripts/db8.py --data_dir $DATA_PATH/ninapro/DB8/ --save_dir $DATA_PATH/ninapro/DB8/h5_500/ --seq_len 1000 --stride 1000

Note: For DB5, we used the --data-augment flag to augment the training data by a factor of 3 (see --augment-factor in scripts/db5.py).