Freezing of gait (FOG) is a debilitating symptom of Parkinson's disease (PD), characterized by an absence or reduction in forward movement of the legs despite the intention to walk. Detecting FOG during free-living conditions presents significant challenges, particularly when using only inertial measurement unit (IMU) data, as it must be distinguished from voluntary stopping events that also feature reduced forward movement. Influences from stress and anxiety, measurable through galvanic skin response (GSR) and electrocardiogram (ECG), may assist in distinguishing FOG from normal gait and stopping. However, no study has investigated the fusion of IMU, GSR, and ECG for FOG detection. Therefore, this study introduced two methods: a two-step approach that first identified reduced forward movement segments using a Transformer-based model with IMU data, followed by an XGBoost model classifying these segments as FOG or stopping using IMU, GSR, and ECG features; and an end-to-end approach employing a multi-stage temporal convolutional network to directly classify FOG and stopping segments from IMU, GSR, and ECG data. Results showed that the two-step approach with all data modalities achieved an average F1 score of 0.728 and F1@50 of 0.725, while the end-to-end approach scored 0.771 and 0.759, respectively. However, no significant difference was found compared to using only IMU data in both approaches (p-values: 0.466 to 0.887). In conclusion, adding physiological data does not provide a statistically significant benefit in distinguishing between FOG and stopping.

The authors have declared no competing interest.

This study was funded by the Development of the Freezing of Gait Interactive Tagging (FOG-IT) project from KU Leuven (under grant C3/20/109) and the Flemish Government (Flanders AI Research Program). Po-Kai Yang was supported by the Ministry of Education (KU Leuven-Taiwan) scholarship. Benjamin Filtjens was supported by KU Leuven Internal Funds Postdoctoral Mandate (under grant PDMT2/22/046), the Data Sciences Institute at the University of Toronto (under grant DSI-PDFY3R1P13), and the strategic basic research project RevalExo (under grant S001024N) funded by the Research Foundation Flanders. Maaike Goris was supported by the Research Foundation Flanders (under grant 1SHEK24N). The resources and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation - Flanders and the Flemish Government.

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Ethics committee Research UZ/KU Leuven approved this study (S65059).

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