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. 2017 Aug;264(8):1642-1654.
doi: 10.1007/s00415-017-8424-0. Epub 2017 Mar 1.

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

Ana Lígia Silva de Lima  1   2   3 , Luc J W Evers  4 , Tim Hahn  4 , Lauren Bataille  5 , Jamie L Hamilton  5 , Max A Little  6   7 , Yasuyuki Okuma  8 , Bastiaan R Bloem  9   4 , Marjan J Faber  4   10
Affiliations

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

Ana Lígia Silva de Lima et al. J Neurol. 2017 Aug.

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.

Keywords: Ambulatory monitoring; Parkinson’s disease; Validation studies; Wearable sensors.

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Conflict of interest statement

Ana Lígia Silva de Lima is supported by Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES (Grant Number 0428-140). Luc J. W. Evers is supported by a Research Grant provided by UCB and Philips Research. Tim Hahn is supported by a Research Grant provided by Stichting Parkinson Fonds. Lauren Bataille and Jamie L. Hamilton are supported by the Michael J. Fox Foundation. Max A. Little received Research funding support from the Michael J. Fox Foundation and UCB. Yasuyuki Okuma has no conflict of interest. Bastiaan R. Bloem received Grant support from the Michael J. Fox Foundation and Stichting Parkinson Fonds. Marjan J. Faber received Grant support from the Michael J. Fox Foundation, Stichting Parkinson Fonds and Philips Research.

Figures

Fig. 1
Fig. 1
Selection process for eligible articles
Fig. 2
Fig. 2
Distribution of device body location for FOG measurement
Fig. 3
Fig. 3
Instrument performance (sensitivity) in FOG detection
Fig. 4
Fig. 4
Instrument performance (specificity) in FOG detection. *Not reported
See this image and copyright information in PMC

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