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

Since falls are so deadly post-stroke, I would expect our doctors and therapists to evaluate this immediately and install it in their hospital in the next month. What are they waiting for?
http://www.jneuroengrehab.com/content/11/1/48

Tal Iluz¹, Eran Gazit¹, Talia Herman¹, Eliot Sprecher¹, Marina Brozgol¹, Nir Giladi¹²⁴, Anat Mirelman¹ and Jeffrey M Hausdorff^1235*

• * Corresponding author: Jeffrey M Hausdorff jhausdor@tlvmc.gov.il

Author Affiliations

¹ Laboratory for Gait &Neurodynamics, Movement Disorders Unit, Department of Neurology, Tel Aviv Sourasky Medical Center, 6 Weizman Street, Tel Aviv 64239, Israel
² Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
³ Department of Physical Therapy, Tel Aviv University, Tel Aviv, Israel
⁴ Department of Neurology, Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
⁵ Harvard Medical School, Boston, MA, USA

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Journal of NeuroEngineering and Rehabilitation 2014, 11:48  doi:10.1186/1743-0003-11-48

The electronic version of this article is the complete one and can be found online at: http://www.jneuroengrehab.com/content/11/1/48

Received:	11 September 2013
Accepted:	24 March 2014
Published:	3 April 2014

© 2014 Iluz et al.; licensee BioMed Central Ltd.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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.