000161717 001__ 161717
000161717 005__ 20251017144642.0
000161717 0247_ $$2doi$$a10.3390/jcm14124207
000161717 0248_ $$2sideral$$a144331
000161717 037__ $$aART-2025-144331
000161717 041__ $$aeng
000161717 100__ $$aBenyoucef, Yacine
000161717 245__ $$aWearable Sensors and Artificial Intelligence for the Diagnosis of Parkinson’s Disease
000161717 260__ $$c2025
000161717 5060_ $$aAccess copy available to the general public$$fUnrestricted
000161717 5203_ $$aBackground/Objectives: This study explores the integration of wearable sensors and artificial intelligence (AI) for Human Activity Recognition (HAR) in the diagnosis and rehabilitation of Parkinson’s disease (PD). The objective was to develop a proof-of-concept model based on internal reproducibility, without external generalization, that is capable of distinguishing pathological movements from healthy ones while ensuring clinical relevance and patient safety. Methods: Nine subjects, including eight patients with Parkinson’s disease and one healthy control, were included. Motion data were collected using the Motigravity platform, which integrates inertial sensors in a controlled environment. The signals were automatically segmented into fixed-length windows, with poor-quality segments excluded through preprocessing. A hybrid CNN-LSTM (Convolutional Neural Networks—Long Short-Term Memory) model was trained to classify motion patterns, leveraging convolutional layers for spatial feature extraction and LSTM layers for temporal dependencies. The Motigravity system provided a controlled hypogravity environment for data collection and rehabilitation exercises. Results: The proposed CNN-LSTM model achieved a validation accuracy of 100%, demonstrating classification potential. The Motigravity system contributed to improved data reliability and ensured patient safety. Despite increasing class imbalance in extended experiments, the model consistently maintained perfect accuracy, suggesting strong generalizability after external validation to overcome the limitations. Conclusions: Integrating AI and wearable sensors has significant potential to improve the HAR-based classification of movement impairments and guide rehabilitation strategies in PD. While challenges such as dataset size remain, expanding real-world validation and enhancing automated segmentation could further improve clinical impact. Future research should explore larger cohorts, extend the model to other neurodegenerative diseases, and evaluate its integration into clinical rehabilitation workflows.
000161717 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000161717 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000161717 700__ $$aMelliti, Islem
000161717 700__ $$aHarmouch, Jouhayna
000161717 700__ $$aAsadi, Borhan$$uUniversidad de Zaragoza
000161717 700__ $$aDel Mastro, Antonio
000161717 700__ $$0(orcid)0000-0002-6506-6081$$aLapuente-Hernández, Diego$$uUniversidad de Zaragoza
000161717 700__ $$0(orcid)0000-0002-9201-0120$$aHerrero, Pablo$$uUniversidad de Zaragoza
000161717 7102_ $$11006$$2413$$aUniversidad de Zaragoza$$bDpto. Fisiatría y Enfermería$$cÁrea Fisioterapia
000161717 773__ $$g14, 12 (2025), 4207 [15 pp.]$$pJ. clin.med.$$tJournal of Clinical Medicine$$x2077-0383
000161717 8564_ $$s2293527$$uhttps://zaguan.unizar.es/record/161717/files/texto_completo.pdf$$yVersión publicada
000161717 8564_ $$s2570770$$uhttps://zaguan.unizar.es/record/161717/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000161717 909CO $$ooai:zaguan.unizar.es:161717$$particulos$$pdriver
000161717 951__ $$a2025-10-17-14:32:43
000161717 980__ $$aARTICLE