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

**Authors:** Francesca Boschi, Stefano Sapienza, Alzhraa A. Ibrahim, Magdalena Sonner, Juergen Winkler, Bjoern Eskofier, Heiko Gaßner, Jochen Klucken

PMC · DOI: 10.3390/bioengineering13020130 · Bioengineering · 2026-01-23

## TL;DR

Wearable sensors during walking tasks can help identify Parkinson’s patients likely to experience worsening gait issues.

## Contribution

Sensor-based gait parameters combined with clinical data improve prediction of gait deterioration in Parkinson’s disease.

## Key findings

- Improvers showed higher gait velocity and stride length compared to Stables and Deteriorators.
- Sensor-derived features combined with clinical variables achieved an AUC of 0.82 in predicting deterioration risk.

## 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 (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.

## Linked entities

- **Diseases:** Parkinson’s disease (MONDO:0005180)

## Full-text entities

- **Diseases:** Gait Deterioration (MESH:D020233), motor deficits (MESH:D009461), metabolic diseases (MESH:D008659), Parkinson's (MESH:D010300), injury to (MESH:D014947), Parkinsonian syndromes (MESH:D020734), LEDD (MESH:D020773), diabetes (MESH:D003920), Parkinson- (MESH:D010302), Gait impairments (MESH:D020234), dopaminergic (MESH:D009422), cognitive decline (MESH:D003072), rigidity (MESH:D009127), Instability and Gait Difficulty (MESH:D043171), bradykinesia (MESH:D018476), postural instability (MESH:D054972)
- **Chemicals:** dopaminergic (MESH:D004298), Levodopa (MESH:D007980)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

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## Figures

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## References

45 references — full list in the complete paper: https://tomesphere.com/paper/PMC12937682/full.md

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Source: https://tomesphere.com/paper/PMC12937682