A Gait Analysis Tool Based on Machine Learning to Support the Rehabilitation Strategy of Post-stroke Patients

Nicoletta Balletti, Nicoletta Balletti, Gennaro Laudato, Rocco Oliveto, Rocco Oliveto

2023

Abstract

Stroke is a serious medical condition that can result in permanent brain damage and other pathological issues. Conditions suffered by survivors ranged in severity from full recovery to significant movement disability. Even though some may recover quickly, many stroke survivors require long-term support to help them achieve as much independence as they can. Thanks to a proper rehabilitation, patients who have experienced a stroke can work to regain skills that are suddenly lost when a section of their brain is injured. Due to the breakdown of neuronal networks in the motor cortex, abnormal gait patterns are a typical disability after a stroke. Therefore, gait analysis can be a powerful tool to support stroke patients during rehabilitation. In this work we propose GIULYO, a Machine Learning based tool that offers support in the assessment of video gait trials in stroke patients by providing an automatic analysis on the muscle activity of the assisted subject. GIULYO is a device-agnostic tool because it accepts motion tracking data in terms of 3d trajectories regardless of the type of instrumentation. GIULYO has been validated on the ARRA Stroke dataset and the results showed an overall accuracy of 0.74 while on a subset a patients—with common clinical assessment of mobility impairments—the accuracy increased to 0.92, therefore demonstrating the feasibility of involving a ML-based approach for the rehabilitation support of post stroke patients.

Paper Citation

in Harvard Style

Balletti N., Laudato G. and Oliveto R. (2023). A Gait Analysis Tool Based on Machine Learning to Support the Rehabilitation Strategy of Post-stroke Patients. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 400-407. DOI: 10.5220/0011697800003414

in Bibtex Style

@conference{healthinf23,
author={Nicoletta Balletti and Gennaro Laudato and Rocco Oliveto},
title={A Gait Analysis Tool Based on Machine Learning to Support the Rehabilitation Strategy of Post-stroke Patients},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={400-407},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011697800003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - A Gait Analysis Tool Based on Machine Learning to Support the Rehabilitation Strategy of Post-stroke Patients
SN - 978-989-758-631-6
AU - Balletti N.
AU - Laudato G.
AU - Oliveto R.
PY - 2023
SP - 400
EP - 407
DO - 10.5220/0011697800003414
PB - SciTePress