Parkinson’s Disease Detection Through Inertial Signals and Posture Insights

Manuel Gil-Martín, Sergio Esteban-Romero, Fernando Fernández-Martínez, Rubén San-Segundo

2024

Abstract

In the development of deep learning systems aimed at detecting Parkinson's Disease (PD) using inertial sensors, some aspects could be essential to refine tremor detection methodologies in realistic scenarios. This work analyses the effect of the subjects’ posture during tremor recordings and the required amount of data to assess a proper PD detection in a Leave-One-Subject-Out Cross-Validation (LOSO CV) scenario. We propose a deep learning architecture that learns a PD biomarker from accelerometer signals to classify subjects between healthy and PD patients. This study uses the PD-BioStampRC21 dataset, containing accelerometer recordings from healthy and PD participants equipped with five inertial sensors. An increment of performance was obtained when using sitting windows compared to using lying windows for Fast Fourier Transform (FFT) input signal domain. Moreover, using 5 minutes per subject could be sufficient to properly evaluate the PD status of a patient without losing performance, reaching a windowlevel accuracy of 77.71 ± 1.07 % and a user-level accuracy of 87.10 ± 11.80 %. Furthermore, a knowledge transfer could be performed when training the system with sitting instances and testing with lying examples, indicating that the sitting activity contains valuable information that allows an effective generalization to lying instances.

Paper Citation

in Harvard Style

Gil-Martín M., Esteban-Romero S., Fernández-Martínez F. and San-Segundo R. (2024). Parkinson’s Disease Detection Through Inertial Signals and Posture Insights. In Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART; ISBN 978-989-758-680-4, SciTePress, pages 1144-1151. DOI: 10.5220/0012451100003636

in Bibtex Style

@conference{icaart24,
author={Manuel Gil-Martín and Sergio Esteban-Romero and Fernando Fernández-Martínez and Rubén San-Segundo},
title={Parkinson’s Disease Detection Through Inertial Signals and Posture Insights},
booktitle={Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART},
year={2024},
pages={1144-1151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012451100003636},
isbn={978-989-758-680-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART
TI - Parkinson’s Disease Detection Through Inertial Signals and Posture Insights
SN - 978-989-758-680-4
AU - Gil-Martín M.
AU - Esteban-Romero S.
AU - Fernández-Martínez F.
AU - San-Segundo R.
PY - 2024
SP - 1144
EP - 1151
DO - 10.5220/0012451100003636
PB - SciTePress