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Self-Supervised Learning Approaches for Wearable-Based Fatigue Recognition

Based on the Homekit2020 repository. See the original README.md and the original LICENSE.md.

For the paper Systematic Evaluation of Self-Supervised Learning Approaches for Wearable-Based Fatigue Recognition (CHIL 2024) by Tamás Visy, Rita Kuznetsova, Christian Holz, Shkurta Gashi.

Setup

Follow the original README.md for the initial setup.

Env

A ".env" file should be added to repository root:

# Environment variables go here, can be read by `python-dotenv` package:
#
#   `src/script.py`
#   ----------------------------------------------------------------
#    import dotenv
#
#    project_dir = os.path.join(os.path.dirname(__file__), os.pardir)
#    dotenv_path = os.path.join(project_dir, '.env')
#    dotenv.load_dotenv(dotenv_path)
#   ----------------------------------------------------------------
#
# DO NOT ADD THIS FILE TO VERSION CONTROL!
PROJECT_NAME=SSLA-WBFR

# Weights and Biases Parameters
WANDB_USERNAME=???
WANDB_PROJECT=???

Patches

The Petastorm package might have to be updated manually to fix a bug related to this:

In petastorm/utils.py, around line 77

if field.numpy_dtype == np.int32:
    decoded_row[field_name] = np.int64(row[field_name])
else:
    decoded_row[field_name] = field.numpy_dtype(row[field_name])

Data

To access the data using the commands utilized below, the templates like configs/data_temporal._yaml should be filled out in a file with the correct (".yaml") file type.

Some step values (mostly in the minute-level data) are very strong outliers, setting these to missing agrees more with the hour-level data. Set --data.fix_step_outliers true to do so, which uses DefaultFixerTransformRow to filter out values (below 0 or above 400 mean steps per minute) by setting values to 0 and missingness to 1.

Train

Sample command

If you follow everything in the README, the following "raw" command should work. However, to simplify setting up runs it is recommended to use configs as described later.

python src/models/train.py fit --config configs/tasks/HomekitPredictFluPos.yaml --config configs/models/CNNToTransformerClassifier.yaml --early_stopping_patience 2 --model.val_bootstraps 0 --data.fix_step_outliers true --data.train_path $PROJECT_ROOT$/data/processed/split_2020_02_10/train_7_day/ --data.val_path $PROJECT_ROOT$/data/processed/split_2020_02_10/eval_7_day/ --data.test_path $PROJECT_ROOT$/data/processed/split_2020_02_10/test_7_day/

where $PROJECT_ROOT$ has to be the absolute path to project's root folder (starting with a drive, e.g., "C:/Users/...", with right facing slashes so Petastorm / Parquet / etc. works).

While this is not mentioned in the original README.md, you should also pass a data.test_path, otherwise testing could raise errors and the train could crash.

Set --model.val_bootstraps=0 to avoid memory leak.

Added --early_stopping_patience, see "Appendix D. Datasheet" of the [Homekit2020] paper.

Diverging from the original command is the addition of --data.fix_step_outliers true, which is described in the previous section.

Self-supervised training sample command

MyCNNToTransformerClassifier can be pre-trained on "unlabeled" data by using an appropriate task. The model's task_type should be set to an appropriate value - this can't be avoided due to the structure of the legacy code and PyTorch Lightning.

python src/models/train.py fit --config configs/tasks/SSL-Autoencode.yaml --config configs/models/MyCNNtoTransformerClassifier.yaml --early_stopping_patience 2 --model.val_bootstraps 0 --model.task_type autoencoder --data.fix_step_outliers true --data.train_path $PROJECT_ROOT$/data/processed/split_2020_02_10/train_7_day/ --data.val_path $PROJECT_ROOT$/data/processed/split_2020_02_10/eval_7_day/ --data.test_path $PROJECT_ROOT$/data/processed/split_2020_02_10/test_7_day/

An example task would be SSL-Autoencode, where the model's task is to find a dense but representative embedding from which the input can be reconstructed well. Since this task has a higher memory footprint compared to other SSL methods, it is recommended to decrease batch size to half with --model.batch_size 400.

Train using configs

Most models can be trained using configs using the template

python src/models/train.py fit -c configs/models/MyCNNtoTransformerClassifier.yaml -c configs/tasks/$TASK_NAME$.yaml -c configs/data_temporal_7_day.yaml -c configs/common.yaml [--model.pretrained_ckpt_path $PATH$]

where task name and checkpoint path have to be set to correct values. Checkpoint path can be excluded for non-pretrained training runs (so all runs except for ones evaluating a model pretrained with an SSL method).

Sample commands

Fatigue
python src/models/train.py fit -c configs/models/MyCNNtoTransformerClassifier.yaml -c configs/tasks/Fatigue.yaml -c configs/data_temporal_7_day.yaml -c configs/common.yaml
SSL-DailyFeatures
python src/models/train.py fit -c configs/models/MyCNNtoTransformerClassifier.yaml -c configs/tasks/SSL-DailyFeatures.yaml -c configs/data_temporal_7_day.yaml -c configs/common.yaml
SSL-MultiHead
python src/models/train.py fit -c configs/models/MyCNNtoTransformerClassifier.yaml -c configs/tasks/SSL-MultiHead.yaml -c configs/data_temporal_7_day.yaml -c configs/common.yaml