000168136 001__ 168136
000168136 005__ 20260302145031.0
000168136 0247_ $$2doi$$a10.3390/life16020193
000168136 0248_ $$2sideral$$a147654
000168136 037__ $$aART-2026-147654
000168136 041__ $$aeng
000168136 100__ $$aBenyoucef, Yacine
000168136 245__ $$aSelective motor entropy modulation and targeted augmentation for the identification of parkinsonian gait patterns using multimodal gait analysis
000168136 260__ $$c2026
000168136 5060_ $$aAccess copy available to the general public$$fUnrestricted
000168136 5203_ $$aBackground/Objectives: Parkinsonian gait is characterized by impaired motor adaptability, altered temporal organization, and reduced movement variability. While data augmentation is commonly used to mitigate class imbalance in gait-based machine learning models, conventional strategies often ignore physiological differences between healthy and pathological movements, potentially distorting meaningful motor dynamics. This study explores whether preserving healthy motor variability while selectively augmenting pathological gait signals can improve the robustness and physiological coherence of gait pattern classification models.
Methods: Eight patients with Parkinsonian gait patterns and forty-eight healthy participants performed walking tasks on the Motigravity platform under hypogravity conditions. Full-body kinematic data were acquired using wearable inertial sensors. A selective augmentation strategy based on smooth time-warping was applied exclusively to pathological gait segments (×5, σ = 0.2), while healthy gait signals were left unaltered to preserve natural motor variability. Model performance was evaluated using a hybrid convolutional neural network–long short-term memory (CNN–LSTM) architecture across multiple augmentation configurations.
Results: Selective augmentation of pathological gait signals achieved the highest classification performance (94.1% accuracy, AUC = 0.97), with balanced sensitivity (93.8%) and specificity (94.3%). Performance decreased when augmentation exceeded an optimal range of variability, suggesting that beneficial augmentation is constrained by physiologically plausible temporal dynamics.
Conclusions: These findings demonstrate that physiology-informed, selective data augmentation can improve gait pattern classification under constrained data conditions. Rather than supporting disease-specific diagnosis, this proof-of-concept study highlights the importance of respecting intrinsic differences in motor variability when designing augmentation strategies for clinical gait analysis. Future studies incorporating disease-control cohorts and subject-independent validation are required to assess specificity and clinical generalizability.
000168136 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000168136 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000168136 700__ $$aHarmouch, Jouhayna
000168136 700__ $$aAsadi, Borhan
000168136 700__ $$aMelliti, Islem
000168136 700__ $$aMastro, Antonio del
000168136 700__ $$0(orcid)0000-0002-9201-0120$$aHerrero, Pablo$$uUniversidad de Zaragoza
000168136 700__ $$0(orcid)0009-0003-8000-7084$$aCarcasona-Otal, Alberto
000168136 700__ $$0(orcid)0000-0002-6506-6081$$aLapuente-Hernández, Diego$$uUniversidad de Zaragoza
000168136 7102_ $$11006$$2413$$aUniversidad de Zaragoza$$bDpto. Fisiatría y Enfermería$$cÁrea Fisioterapia
000168136 773__ $$g16(2), 193 (2026), 13$$pLife (Basel)$$tLife$$x2075-1729
000168136 8564_ $$s628001$$uhttps://zaguan.unizar.es/record/168136/files/texto_completo.pdf$$yVersión publicada
000168136 8564_ $$s2521995$$uhttps://zaguan.unizar.es/record/168136/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000168136 909CO $$ooai:zaguan.unizar.es:168136$$particulos$$pdriver
000168136 951__ $$a2026-03-02-14:50:04
000168136 980__ $$aARTICLE