Validation of a sensor-based gait analysis system with a gold-standard motion capture system in patients with parkinson’s disease

Verena Jakob, Arne Küderle, Felix Kluge, Jochen Klucken, Bjoern M. Eskofier, Jürgen Winkler, Martin Winterholler, Heiko Gassner*

*Corresponding author for this work

Research output: Contribution to journalArticleResearchpeer-review

10 Citations (Scopus)


Digital technologies provide the opportunity to analyze gait patterns in patients with Par-kinson’s Disease using wearable sensors in clinical settings and a home environment. Confirming the technical validity of inertial sensors with a 3D motion capture system is a necessary step for the clinical application of sensor-based gait analysis. Therefore, the objective of this study was to com-pare gait parameters measured by a mobile sensor-based gait analysis system and a motion capture system as the gold standard. Gait parameters of 37 patients were compared between both systems after performing a standardized 5 × 10 m walking test by reliability analysis using intra-class correlation and Bland–Altman plots. Additionally, gait parameters of an age-matched healthy control group (n = 14) were compared to the Parkinson cohort. Gait parameters representing bradykinesia and short steps showed excellent reliability (ICC > 0.96). Shuffling gait parameters reached ICC > 0.82. In a stridewise synchronization, no differences were observed for gait speed, stride length, stride time, relative stance and swing time (p > 0.05). In contrast, heel strike, toe off and toe clearance significantly differed between both systems (p < 0.01). Both gait analysis systems distinguish Parkinson patients from controls. Our results indicate that wearable sensors generate valid gait parameters compared to the motion capture system and can consequently be used for clinically relevant gait recordings in flexible environments.

Original languageEnglish
Article number7680
Issue number22
Publication statusPublished - 1 Nov 2021


  • Inertial sensors
  • Machine learning algorithm
  • Parkinson’s disease
  • Spatiotemporal gait parameters
  • Three-dimensional gait analysis
  • Wearables


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