This repository contains exploratory and focused analysis work around loading-rate estimation from wearable-sensor and force-plate data.

The work in this project contributed to the publication indexed at PubMed:

• https://pubmed.ncbi.nlm.nih.gov/41216764/

The article was published in the Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, volume 239, issues 11–12, pages 1147–1155

This repository should not be read as a clean, single-purpose paper package. A substantial amount of parallel experimental work was done here at the same time, including side analyses, method checks, frequency-domain variants, reviewer-response work, and intermediate notebook-based exploration.

• ktp_review_MAIN.ipynb in the project root is the main/final notebook that was kept at the top level.
• notebooks/ contains supporting notebooks grouped by topic:
  • core/
  • cwt/
  • grf/
  • stats/
• Paper_review/ contains additional review-response and paper-specific exploratory material.
• helpers.py contains shared utility functions used by multiple notebooks.

Create and activate a virtual environment, then install the Python dependencies:

python3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt
python -m ipykernel install --user --name rsh-loading-rate --display-name "rsh-loading-rate"

Start JupyterLab from the project root:

source .venv/bin/activate
jupyter lab

Running JupyterLab from the project root is important because several notebooks resolve project resources relative to this directory.

The repository includes notebooks, helper code, and multiple generated result folders, but not every historical dataset artifact used during exploration appears to be present in this checkout.

Some notebooks may still require local data files that are not currently committed here. In particular, this project reflects an active research workspace rather than a fully reduced archival reproduction bundle.

The Python environment in requirements.txt is intended to support the notebook workflow in this repository through JupyterLab. It does not attempt to manage MATLAB tooling or system-level scientific software outside Python.