Freezing of gait and fall detection in Parkinson’s disease using wearable sensors:a systematic review

Silva de Lima, Ana Lígia, Evers, Luc J.W., Hahn, Tim, Bataille, Lauren, Hamilton, Jamie L., Little, Max A., Okuma, Yasuyuki, Bloem, Bastiaan R. and Faber, Marjan J. (2017). Freezing of gait and fall detection in Parkinson’s disease using wearable sensors:a systematic review. Journal of Neurology, in pre ,

Abstract

Despite the large number of studies that have investigated the use of wearable sensors to detect gait disturbances such as Freezing of gait (FOG) and falls, there is little consensus regarding appropriate methodologies for how to optimally apply such devices. Here, an overview of the use of wearable systems to assess FOG and falls in Parkinson’s disease (PD) and validation performance is presented. A systematic search in the PubMed and Web of Science databases was performed using a group of concept key words. The final search was performed in January 2017, and articles were selected based upon a set of eligibility criteria. In total, 27 articles were selected. Of those, 23 related to FOG and 4 to falls. FOG studies were performed in either laboratory or home settings, with sample sizes ranging from 1 PD up to 48 PD presenting Hoehn and Yahr stage from 2 to 4. The shin was the most common sensor location and accelerometer was the most frequently used sensor type. Validity measures ranged from 73–100% for sensitivity and 67–100% for specificity. Falls and fall risk studies were all home-based, including samples sizes of 1 PD up to 107 PD, mostly using one sensor containing accelerometers, worn at various body locations. Despite the promising validation initiatives reported in these studies, they were all performed in relatively small sample sizes, and there was a significant variability in outcomes measured and results reported. Given these limitations, the validation of sensor-derived assessments of PD features would benefit from more focused research efforts, increased collaboration among researchers, aligning data collection protocols, and sharing data sets.

Publication DOI: https://doi.org/10.1007/s00415-017-8424-0
Divisions: Engineering & Applied Sciences > Mathematics
Engineering & Applied Sciences > Systems analytics research institute (SARI)
Additional Information: © The Author(s) 2017. This article is published with open access at springerlink.com. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Uncontrolled Keywords: ambulatory monitoring,Parkinson’s disease,validation studies,wearable sensors,Neurology,Clinical Neurology
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Related URLs: http://www.scop ... tnerID=8YFLogxK (Scopus URL)
Published Online Date: 2017-03-01
Authors: Silva de Lima, Ana Lígia
Evers, Luc J.W.
Hahn, Tim
Bataille, Lauren
Hamilton, Jamie L.
Little, Max A. ( 0000-0002-1507-3822)
Okuma, Yasuyuki
Bloem, Bastiaan R.
Faber, Marjan J.

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