Machine Learning-Based Qualitative Analysis of Human Gait Through Video Features

Nicoletta Balletti, Nicoletta Balletti, Roberto Zinni, Marco Russodivito, Gennaro Laudato, Simone Scalabrino, Simone Scalabrino, Rocco Oliveto, Rocco Oliveto

2024

Abstract

Strokes constitute a major cause of both mortality and disability, carrying significant economic implications for healthcare systems. Evaluating the quality of gait in post-stroke patients during rehabilitation is essential for providing effective care. The Dynamic Gait Index (DGI) is a valuable metric for evaluating gait quality. However, the assessment of such an index typically requires invasive tests or specialized sensors. In this paper, we introduce a machine learning-based approach for estimating DGI exclusively from video recordings. Our research encompasses a comprehensive set of experiments, including data preprocessing, feature selection, and the application of various machine learning algorithms. To ensure the robustness of our findings, we employ the Leave 1 Subject Out (L1SO) cross-validation method. Our results underscore the challenge of accurately estimating DGI using solely video data. We achieved an R-squared (R2 ) value of only 0.19 and a mean absolute error (MAE) of 2.2. Notably, we observed that our approach yielded notably poorer results for a specific subset of three patients. Upon excluding this subset, the R2 increased to 0.30, and the MAE improved to 1.9. This observation suggests that incorporating patient-specific features into the model may hold the key to enhancing its overall accuracy.

Paper Citation

in Harvard Style

Balletti N., Zinni R., Russodivito M., Laudato G., Scalabrino S. and Oliveto R. (2024). Machine Learning-Based Qualitative Analysis of Human Gait Through Video Features. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF; ISBN 978-989-758-688-0, SciTePress, pages 450-457. DOI: 10.5220/0012375900003657

in Bibtex Style

@conference{healthinf24,
author={Nicoletta Balletti and Roberto Zinni and Marco Russodivito and Gennaro Laudato and Simone Scalabrino and Rocco Oliveto},
title={Machine Learning-Based Qualitative Analysis of Human Gait Through Video Features},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF},
year={2024},
pages={450-457},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012375900003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 2: HEALTHINF
TI - Machine Learning-Based Qualitative Analysis of Human Gait Through Video Features
SN - 978-989-758-688-0
AU - Balletti N.
AU - Zinni R.
AU - Russodivito M.
AU - Laudato G.
AU - Scalabrino S.
AU - Oliveto R.
PY - 2024
SP - 450
EP - 457
DO - 10.5220/0012375900003657
PB - SciTePress