Clinical validation using MJFF Levodopa Response Study

[angerhang]

• [x] Process the MJFF with labels in 10 seconds epoch.
• Expected data format can be found here.
• Label cleaning: merging walking classes that might be the same from an ACC perspective and annotate the merging process
• Data quality check and create a PR to merge the data processing script into https://github.com/OxWearables/ssl-wearables/tree/main/data_parsing

Experiments Both the below experiments can be run using https://github.com/OxWearables/ssl-wearables/blob/main/downstream_task_evaluation.py

Example scripts to run for different modes. Ask Hang for model weights for different pre-trained models.

python downstream_task_evaluation.py data=pamap_10s evaluation=hand_crafted
python downstream_task_evaluation.py gpu=1 data=pamap_10s evaluation=flip_net_random_mlp model=resnet
python downstream_task_evaluation.py gpu=0 data=pamap_10s evaluation=flip_net_ft_freeze model=resnet
python downstream_task_evaluation.py gpu=2 data=pamap_10s evaluation=flip_net_ft model=resnet
python downstream_task_evaluation.py gpu=3 data=pamap_10s evaluation=flip_net_100k_ft model=resnet

• [x] Add MJFF results to Table 2: RF+fine-tuning results
• [x] Add MJFF results to Table 3: fine-tuning results
• [x] Add PCA MJFF results to Figure 2: feature visu. Ask Shing for code pointer

The model weights for Table 2 can be downloaded from Github readme links. For Table 3, self-supervised ones, you can get from Github readme links too. Ask Hang for the supervised weights.

Writing

• [x] Add MJFF descriptions to Table 4: dataset descriptions

[aidanacquah]

PR submitted for code to download data