Deep learning algorithms for detecting freezing of gait in Parkinson’s disease: A cross-dataset study

Freezing of gait is a complex and disabling symptom of Parkinson’s disease, which has a significant impact on the patients’ quality of life and increases the risk of falls and related injuries. This study aims to evaluate the generalization capability of deep learning algorithms in freezing of gait...

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Vydáno v:Expert systems with applications Ročník 255; s. 124522
Hlavní autoři: Sigcha, Luis, Borzì, Luigi, Olmo, Gabriella
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Ltd 01.12.2024
Témata:
Accelerometer
Cross-dataset test
Deep learning
Machine learning
Motor symptoms
Wearable sensor
Deep learning
Accelerometer
Cross-dataset test
Wearable sensor
Machine learning
Motor symptoms
ISSN:0957-4174, 1873-6793
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Shrnutí:Freezing of gait is a complex and disabling symptom of Parkinson’s disease, which has a significant impact on the patients’ quality of life and increases the risk of falls and related injuries. This study aims to evaluate the generalization capability of deep learning algorithms in freezing of gait detection. To address this task, various machine learning and deep learning algorithms were implemented, fine-tuned, and evaluated using diverse data splitting and validation strategies. The experiments performed yielded mixed results. Although the implementations demonstrated competitive performance in single-dataset settings (area under the curve ranging from 0.77 to 0.94), all approaches showed limited robustness in cross-dataset tests and suboptimal generalization across different datasets (area under the curve ranging from 0.65 to 0.84). These results highlight the importance of standardized data collection procedures to ensure uniformity. The specification of sensor settings and predefined sensor locations can foster homogeneity in datasets, even when dealing with diverse subjects and environments. Such standardization efforts are crucial for advancing generalized methodologies in the detection of freezing of gait, applicable to both research and clinical applications.
ISSN:0957-4174
1873-6793
DOI:10.1016/j.eswa.2024.124522