Feature-Mask-Based Strategies for Subtype-Specific Freezing of Gait Detection using CNNs

Research overview

The text below is the source article’s original abstract under a verified reuse license. cc by

Objective Freezing of gait (FOG), a disabling symptom of Parkinson’s disease, varies in manifestations and motion contexts. Its heterogeneity motivates subtype categorization such as manifestation-specific subtypes (akinesia, trembling or shuffling) or motion-specific subtypes (gait-initiation, walking or turning). Despite numerous promising deep learning FOG detection studies, few consider FOG heterogeneity. It remains unclear whether different subtypes require distinct detection strategies, and whether tailoring subtype-specific models could enhance detection generalizability across subtypes. Methods: To address these questions, we categorize FOG data into manifestation- or motion-specific subtypes and derive their corresponding detection strategies as interpretable feature masks. We then propose a feature-mask-based CNN that explicitly embeds the identified strategies. Using waist-mounted 3D accelerometer data, a general CNN and subtype-specific CNNs are trained. Results: According to feature-mask analysis, motion-specific subtypes share a common detection strategy, whereas manifestation-specific subtypes require distinct strategies. Manifestation models exhibit enhanced generalizability across subtypes compared to the general model, boosting the overall average FOG detection sensitivity by 24.95%±9.80% and specificity by 18.29%±8.71%. Conversely, motion models reduce the overall FOG sensitivity by 1.89%±8.74% and specificity by 5.17%±10.76%. Conclusions: The detection strategy is mainly driven by manifestation composition of the data. The general model favors the dominant manifestation-specific subtype group(s), a bias corrected by tailored manifestation-specific strategies. No comparable benefit arises from motion models due to their similar manifestation compositions. Significance: This study interpretably reveals the detection strategies required by different FOG subtypes and demonstrates the effectiveness of subtype-specific tailoring in improving FOG detection generalizability.

Related topics