CC BY 4.0Boschi, FrancescaFrancescaBoschiSapienza, StefanoStefanoSapienzaIbrahim, Alzhraa A.Alzhraa A.IbrahimSonner, MagdalenaMagdalenaSonnerWinkler, JürgenJürgenWinklerEskofier, Bjoern M.Bjoern M.EskofierGaßner, HeikoHeikoGaßnerKlucken, JochenJochenKlucken2026-03-112026-03-112026https://publica.fraunhofer.de/handle/publica/509194https://doi.org/10.24406/publica-786010.3390/bioengineering1302013010.24406/publica-78602-s2.0-105031520250Background: People with Parkinson’s disease suffer from gait impairments. Clinical scales provide a limited and rater-dependent assessment of gait. Wearable sensors allow an objective characterization by capturing rhythm, pace, and signature patterns. This study investigated if sensor-derived gait parameters have prognostic value for short-term progression of gait impairments. Methods: A total of 111 longitudinal visit pairs were analyzed, where participants underwent clinical evaluation and a 4 × 10 m walking test instrumented with wearable sensors. Changes in the UPDRSIII gait score between baseline and follow-up were used to classify participants as Improvers, Stables, or Deteriorators. Baseline group differences were assessed statistically. Machine-learning classifiers were trained to predict group membership using clinical variables alone, sensor-derived gait features alone, or a combination of both. Results: Significant between-group differences emerged. In participants with UPDRSIII gait score = 1, Improvers showed higher median gait velocity ((Formula presented.)) and stride length ((Formula presented.)) than Stables ((Formula presented.) ; (Formula presented.)) and Deteriorators ((Formula presented.) ; (Formula presented.)), along with lower stance time variability (3.10% vs. 4.49% and 3.75%; all (Formula presented.)). The combined sensor-based and clinical model showed the best performance (AUC (Formula presented.)). Conclusions: Integrating sensor-derived gait parameters with clinical score can support the identification of patients at risk of gait deterioration in the near future.entrueclinical utilitygait disordersinstrumented gait assessmentsmachine learningwearable sensorsjournal article