Automated detection of missteps during community ambulation in patients with Parkinson's disease: A new approach for quantifying fall risk in the community setting

Tal Iluz, Eran Gazit, Talia Herman, Eliot Sprecher, Marina Brozgol, Nir Giladi, Anat Mirelman, Jeffrey M. Hausdorff

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Background: Falls are a leading cause of morbidity and mortality among older adults and patients with neurological disease like Parkinson's disease (PD). Self-report of missteps, also referred to as near falls, has been related to fall risk in patients with PD. We developed an objective tool for detecting missteps under real-world, daily life conditions to enhance the evaluation of fall risk and applied this new method to 3 day continuous recordings. Methods. 40 patients with PD (mean age ± SD: 62.2 ± 10.0 yrs, disease duration: 5.3 ± 3.5 yrs) wore a small device that contained accelerometers and gyroscopes on the lower back while participating in a protocol designed to provoke missteps in the laboratory. Afterwards, the subjects wore the sensor for 3 days as they carried out their routine activities of daily living. An algorithm designed to automatically identify missteps was developed based on the laboratory data and was validated on the 3 days recordings. Results: In the laboratory, we recorded 29 missteps and more than 60 hours of data. When applied to this dataset, the algorithm achieved a 93.1% hit ratio and 98.6% specificity. When we applied this algorithm to the 3 days recordings, patients who reported two falls or more in the 6 months prior to the study (i.e., fallers) were significantly more likely to have a detected misstep during the 3 day recordings (p = 0.010) compared to the non-fallers. Conclusions: These findings suggest that this novel approach can be applied to detect missteps during daily life among patients with PD and will likely help in the longitudinal assessment of disease progression and fall risk.

Original languageEnglish
Article number48
JournalJournal of NeuroEngineering and Rehabilitation
Volume11
Issue number1
DOIs
StatePublished - 3 Apr 2014
Externally publishedYes

Keywords

  • Accelerometers
  • Body-worn sensors
  • Fall risk
  • Gait
  • Monitoring
  • Parkinson's disease

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