How jogcaetano13/heel_rise_jupyter_notebook works
This is a heel-rise accelerometer analysis repository. Its documented workflow is to explore participant recordings, process signals and repetition metrics, and produce aggregated CSV results; a separate classifier investigates samples already logged as errors. The sampled Python code confirms raw-file conversion and signal-processing helpers, while the notebook internals were not sampled, so their documented roles are shown without inferring detailed calls between notebook stages.
Main entry points: generate_csv.ipynb CSV processing · classify_errors.py Error classifier · data Participant dataset · script.py Recording converter
Components 11
Data ingestion
- Recording converter
script.py - Participant dataset
data
Signal analysis
- CSV processing
generate_csv.ipynb - Signal utilities
helpers.py - Error classifier
classify_errors.py
Analysis outputs
- Processed results
results.csv - Error log
errors.csv - Classified errors
errors_classified.csv
Exploration
- Dataset overview
data_set_overview.ipynb
Other components
- Analyst
- Source recordings
Connections 14
- Source recordings → Recording converter: reads
- Recording converter → Participant dataset: writes
- Analyst → Dataset overview: explores
- Dataset overview → Participant dataset: examines
- Analyst → CSV processing: runs
- CSV processing → Participant dataset: processes
- CSV processing → Processed results: writes
- CSV processing → Error log: writes
- CSV processing → Signal utilities: uses
- Analyst → Error classifier: runs
- Error classifier → Error log: reads
- Error classifier → Participant dataset: reads
- Error classifier → Signal utilities: uses
- Error classifier → Classified errors: writes