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Review
. 2024 Jun 18;24(12):3959.
doi: 10.3390/s24123959.

Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis

Hagar Elbatanouny  1 , Natasa Kleanthous  2 , Hayssam Dahrouj  1 , Sundus Alusi  3 , Eqab Almajali  1 , Soliman Mahmoud  1   4 , Abir Hussain  1
Affiliations
Review

Insights into Parkinson's Disease-Related Freezing of Gait Detection and Prediction Approaches: A Meta Analysis

Hagar Elbatanouny et al. Sensors (Basel). .

Abstract

Parkinson's Disease (PD) is a complex neurodegenerative disorder characterized by a spectrum of motor and non-motor symptoms, prominently featuring the freezing of gait (FOG), which significantly impairs patients' quality of life. Despite extensive research, the precise mechanisms underlying FOG remain elusive, posing challenges for effective management and treatment. This paper presents a comprehensive meta-analysis of FOG prediction and detection methodologies, with a focus on the integration of wearable sensor technology and machine learning (ML) approaches. Through an exhaustive review of the literature, this study identifies key trends, datasets, preprocessing techniques, feature extraction methods, evaluation metrics, and comparative analyses between ML and non-ML approaches. The analysis also explores the utilization of cueing devices. The limited adoption of explainable AI (XAI) approaches in FOG prediction research represents a significant gap. Improving user acceptance and comprehension requires an understanding of the logic underlying algorithm predictions. Current FOG detection and prediction research has a number of limitations, which are identified in the discussion. These include issues with cueing devices, dataset constraints, ethical and privacy concerns, financial and accessibility restrictions, and the requirement for multidisciplinary collaboration. Future research avenues center on refining explainability, expanding and diversifying datasets, adhering to user requirements, and increasing detection and prediction accuracy. The findings contribute to advancing the understanding of FOG and offer valuable guidance for the development of more effective detection and prediction methodologies, ultimately benefiting individuals affected by PD.

Keywords: FOG prediction; Parkinson’s disease; cueing devices; explainable AI; machine learning; wearable sensors.

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Conflict of interest statement

Author Natasa Kleanthous was employed by O&P Electronics & Robotics Ltd.; Author Sundus Alusi was employed by the Walton Centre NHS Foundation Trust. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
The structure of the PPN network. (a) The exact position of PPN [13]. (b) The connections of PPN neurons [12].
Figure 2
Figure 2
The utilization of the datasets in the literature. (Ferster et al., 2015): [35]; (Mazilu et al., 2015): [42]; (Zhang et al., 2022): [44]; (Huang et al., 2024): [54]; (Xia et al., 2024): [55]; (Khosla et al., 2024): [56]; (Sun et al., 2024): [57]; (Dimoudis et al., 2023): [60]; (Borzi et al., 2023): [65]; (Halder et al., 2021): [76]; (Esfahani et al., 2021): [80]; (Bikias et al., 2021): [81]; (Basaklar et al., 2021): [82]; (Suppa et al., 2017): [83]; (Ghosh et al., 2021): [84]; (Li et al., 2020): [85]; (Demrozi et al., 2020): [87]; (Kleanthous et al., 2020): [90]; (San-Segundo et al., 2019): [93]; (Naghavi et al., 2019): [94]; (Guo et al., 2019): [96]; (Arami et al., 2019): [98]; (Orphanidou et al., 2018): [100]; (Pham et al., 2017): [106]; (Palmerini et al., 2017): [107]; (Rezvanian et al., 2016): [114]; (Mazilu et al., 2012): [123].
Figure 3
Figure 3
Distribution of the papers over the years.
Figure 4
Figure 4
FOG detection/prediction system overview.
See this image and copyright information in PMC

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Grants and funding

This research received no external funding.