Prediction of freezing of gait in Parkinson's from physiological wearables: An exploratory study

Sinziana Mazilu, Alberto Calatroni, Eran Gazit, Anat Mirelman, Jeffrey M. Hausdorff, Gerhard Tröster

Research output: Contribution to journalArticlepeer-review

73 Scopus citations


Freezing of gait (FoG) is a common gait impairment among patients with advanced Parkinson's disease. FoG is associated with falls and negatively impacts the patient's quality of life. Wearable systems that detect FoG in real time have been developed to help patients resume walking bymeans of rhythmic cueing. Current methods focus on detection, which require FoG events to happen first, while their prediction opens the road to preemptive cueing, which might help subjects to avoid freeze altogether. We analyzed electrocardiography (ECG) and skin-conductance (SC) data from 11 subjects who experience FoG in daily life, and found statistically significant changes in ECG and SC data just before the FoG episodes, compared to normal walking. Based on these findings, we developed an anomaly-based algorithm for predicting gait freeze from relevant SC features. We were able to predict 71.3% from 184 FoG with an average of 4.2 s before a freeze episode happened. Our findings enable the possibility of wearable systems, which predict with few seconds before an upcoming FoG from SC, and start external cues to help the user avoid the gait freeze.

Original languageEnglish
Article number7180300
Pages (from-to)1843-1854
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number6
StatePublished - 1 Nov 2015
Externally publishedYes


  • Body-fixed sensors
  • Electrocardiography (ECG)
  • Freezing of gait (FoG)
  • Parkinson's disease (PD)
  • Prediction
  • Skin conductance (SC)
  • Wearables

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management


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