High-accuracy wearable detection of freezing of gait in Parkinson's disease based on pseudo-multimodal features

Objective: Freezing of gait (FoG) is a serious symptom of Parkinson's disease and prompt detection of FoG is crucial for fall prevention. Although multimodal data combining electroencephalography (EEG) benefit accurate FoG detection, the preparation, acquisition, and analysis of EEG signals are time-consuming and costly, which impedes the application of multimodal information in FoG detection. This work proposes a wearable FoG detection method that merges multimodal information from acceleration and EEG while avoiding the acquisition of real EEG data. Methods: A proxy measurement (PM) model based on long-short-term-memory (LSTM) network was proposed to measure EEG features from accelerations, and pseudo-multimodal features, i.e., pseudo-EEG and acceleration, could be extracted using a highly wearable inertial sensor for FoG detection. Results: Based on a self-collected FoG dataset, the performance of different feature combinations were compared in terms of subject-dependent and cross-subject settings. In both settings, pseudo-multimodal features achieved the most promising performance, with a geometric mean of 91.0 +/- 5.0% in subject-dependent setting and 91.0 +/- 3.5% in cross-subject setting. Conclusion: Our study suggests that wearable FoG detection can be enhanced through leveraging cross-modal information fusion. Significance: The new method provides a promising path for multimodal information fusion and the long-term monitoring of FoG in living environments.