ORCID Profile
0000-0002-6762-3347
Current Organisation
KU Leuven
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Publisher: Springer Science and Business Media LLC
Date: 10-09-2021
DOI: 10.1038/S41531-021-00224-4
Abstract: Freezing of gait (FOG) in Parkinson’s disease (PD) causes severe patient burden despite pharmacological management. Exercise and training are therefore advocated as important adjunct therapies. In this meta-analysis, we assess the existing evidence for such interventions to reduce FOG, and further examine which type of training helps the restoration of gait function in particular. The primary meta-analysis across 41 studies and 1838 patients revealed a favorable moderate effect size (ES = −0.37) of various training modalities for reducing subjective FOG-severity ( p 0.00001), though several interventions were not directly aimed at FOG and some included non-freezers. However, exercise and training also proved beneficial in a secondary analysis on freezers only (ES = −0.32, p = 0.007). We further revealed that dedicated training aimed at reducing FOG episodes (ES = −0.24) or ameliorating the underlying correlates of FOG (ES = −0.40) was moderately effective ( p 0.01), while generic exercises were not (ES = −0.14, p = 0.12). Relevantly, no retention effects were seen after cessation of training (ES = −0.08, p = 0.36). This review thereby supports the implementation of targeted training as a treatment for FOG with the need for long-term engagement.
Publisher: Cold Spring Harbor Laboratory
Date: 05-05-2023
DOI: 10.1101/2023.05.05.23289387
Abstract: Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson’s Disease (PD). Traditionally, FOG assessment relies on time-consuming visual inspection of camera footage. Therefore, previous studies have proposed portable and automated solutions to annotate FOG. However, automated FOG assessment is challenging due to gait variability caused by medication effects and varying FOG-provoking tasks. Moreover, whether automated approaches can differentiate FOG from typical everyday movements, such as volitional stops, remains to be determined. To address these questions, we evaluated an automated FOG assessment model with deep learning (DL) based on inertial measurement units (IMUs). We assessed its performance trained on all standardized FOG-provoking tasks and medication states, as well as on specific tasks and medication states. Furthermore, we examined the effect of adding stopping periods on FOG detection performance. Twelve PD patients with self-reported FOG (mean age 69.33 ± 6.28 years) completed a FOG-provoking protocol, including timed-up-and-go and 360-degree turning-in-place tasks in On/Off dopaminergic medication states with/without volitional stopping. IMUs were attached to the pelvis and both sides of the tibia and talus. A multi-stage temporal convolutional network was developed to detect FOG episodes. FOG severity was quantified by the percentage of time frozen (%TF) and the number of freezing episodes (#FOG). The agreement between the model-generated outcomes and the gold standard experts’ video annotation was assessed by the intra-class correlation coefficient (ICC). For FOG assessment in trials without stopping, the agreement of our model was strong (ICC(%TF) = 0.92 [0.68, 0.98] ICC(#FOG) = 0.95 [0.72, 0.99]). Models trained on a specific FOG-provoking task could not generalize to unseen tasks, while models trained on a specific medication state could generalize to unseen states. For assessment in trials with stopping, the model trained on stopping trials made fewer false positives than the model trained without stopping (ICC(%TF) = 0.95 [0.73, 0.99] ICC(#FOG) = 0.79 [0.46, 0.94]). A DL model trained on IMU signals allows valid FOG assessment in trials with/without stops containing different medication states and FOG-provoking tasks. These results are encouraging and enable future work investigating automated FOG assessment during everyday life.
Publisher: Cold Spring Harbor Laboratory
Date: 12-07-2023
DOI: 10.1101/2023.07.10.23292437
Abstract: Freezing of gait (FOG) is an episodic and highly disabling symptom of Parkinson’s disease (PD). Although described as a single phenomenon, FOG is not univocal and can express as different manifestations, such as trembling in place or complete akinesia. We aimed to analyze the utility of deep learning trained on inertial measurement unit data to classify FOG into both manifestations. We developed a temporal convolutional neural network, which we compared to three state-of-the-art FOG detection algorithms that were adapted to the FOG manifestation detection task. Next, we investigated its performance in distinguishing between the two manifestations and other forms of movement cessation (e.g., volitional stopping and sitting) based on gold-standard video annotations. Experiments were conducted on a dataset of twelve PD patients with FOG that completed a FOG-provoking protocol, including the timed-up-and-go and 360-degree turning-in-place tasks during ON and OFF anti-Parkinsonian medication. The results showed that our model enables accurate detection of FOG manifestations with an 11.43% higher F1 score than the second-best model. Assessment of FOG manifestation severity was moderately strong for trembling in place (Intra-class Correlation Coefficient (ICC)=0.64, [0.16,0.88]) and strong for complete akinesia (ICC=0.87, [0.63,0.96]). Remarkably, our results show that complete akinesia can be distinguished from volitional stopping. In conclusion, we established that FOG manifestations could be accurately detected and assessed with deep learning. Future work should establish whether these results hold firm for a more extensive and varied verification cohort.
No related grants have been discovered for Pieter Ginis.