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Boschi, F.* ; Sapienza, S.* ; Ibrahim, A.A.* ; Sonner, M.* ; Winkler, J.* ; Eskofier, B.M. ; Gaßner, H.* ; Klucken, J.*

Sensor-derived parameters from standardized walking tasks can support the identification of patients with Parkinson’s disease at risk of gait deterioration.

Bioengineering 13:130 (2026)
Publ. Version/Full Text DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Background: 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 (0.81 m/s) and stride length (0.80 m) than Stables (0.68 m/s; 0.70 m) and Deteriorators (0.59 m/s; 0.68 m), along with lower stance time variability (3.10% vs. 4.49% and 3.75%; all p<0.05). The combined sensor-based and clinical model showed the best performance (AUC 0.82). Conclusions: Integrating sensor-derived gait parameters with clinical score can support the identification of patients at risk of gait deterioration in the near future.
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Publication type Article: Journal article
Document type Scientific Article
Keywords Gait ; Stride ; Wearable Computer ; Gait Analysis ; Wearable Technology ; Disease
ISSN (print) / ISBN 2306-5354
Journal Bioengineering
Quellenangaben Volume: 13, Issue: 2, Pages: , Article Number: 130 Supplement: ,
Publisher MDPI
Publishing Place Basel
Reviewing status Peer reviewed
Institute(s) Institute of AI for Health (AIH)
Grants Luxembourg National Research Fund
Fraunhofer Internal Programs