Freezing of gait and fall detection in Parkinson's disease using wearable sensors: a systematic review
Publication year
2017Source
Journal of Neurology, 264, 8, (2017), pp. 1642-1654ISSN
Publication type
Article / Letter to editor

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Organization
Neurology
IQ Healthcare
Journal title
Journal of Neurology
Volume
vol. 264
Issue
iss. 8
Page start
p. 1642
Page end
p. 1654
Subject
Radboudumc 18: Healthcare improvement science RIHS: Radboud Institute for Health Sciences; Radboudumc 3: Disorders of movement DCMN: Donders Center for Medical NeuroscienceAbstract
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.
This item appears in the following Collection(s)
- Academic publications [203608]
- Electronic publications [101974]
- Faculty of Medical Sciences [80231]
- Open Access publications [70685]
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