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 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.