Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson’s Disease at Risk of Gait Deterioration

Boschi F, Sapienza S, Ibrahim A, Sonner M, Winkler J, Eskofier B, Gaßner H, Klucken J (2026)

Publication Type: Journal article

Publication year: 2026

Journal

Book Volume: 13

Article Number: 130

Journal Issue: 2

DOI: 10.3390/bioengineering13020130

Abstract

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 ((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.

Authors with CRIS profile

Involved external institutions

University of Luxembourg Luxembourg (LU) Fraunhofer-Institut für Integrierte Schaltungen (IIS) Germany (DE)

How to cite

APA:

Boschi, F., Sapienza, S., Ibrahim, A., Sonner, M., Winkler, J., Eskofier, B.,... Klucken, J. (2026). Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson’s Disease at Risk of Gait Deterioration. Bioengineering, 13(2). https://doi.org/10.3390/bioengineering13020130

MLA:

Boschi, Francesca, et al. "Sensor-Derived Parameters from Standardized Walking Tasks Can Support the Identification of Patients with Parkinson’s Disease at Risk of Gait Deterioration." Bioengineering 13.2 (2026).

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