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• W4226419212 abstract "Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson's disease. FoG impairs walking and is associated with increased fall risk. On-demand external cueing systems can detect FoG and provide stimuli to help individuals overcome freezing. Predicting FoG before onset enables preemptive cueing and may prevent FoG. However, detection and prediction remain challenging. If FoG data are not available for an individual, patient-independent models have been used to detect FoG, which have shown great sensitivity and poor specificity, or vice versa. In this study, we introduce a Deep Gait Anomaly Detector (DGAD) using a transfer learning-based approach to improve FoG detection accuracy. We also evaluate the effect of data augmentation and additional pre-FoG segments on prediction rate. Seven individuals with PD performed a series of daily walking tasks wearing inertial measurement units on their ankles. The DGAD algorithm demonstrated average sensitivity and specificity of 63.0% and 98.6% (3.2% improvement compared with the highest specificity in the literature). The target models identified 87.4% of FoG onsets, with 21.9% predicted. This study demonstrates our algorithm's potential for accurate identification of FoG and delivery of cues for patients for whom no FoG data is available for model training." @default.
• W4226419212 created "2022-05-05" @default.
• W4226419212 creator A5006390458 @default.
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• W4226419212 date "2022-04-01" @default.
• W4226419212 modified "2023-10-02" @default.
• W4226419212 title "Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson’s Disease: A Novel Deep One-Class Classifier" @default.
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• W4226419212 doi "https://doi.org/10.1109/jbhi.2021.3103071" @default.
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