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Evaluating Temporal Windows and Subject -Specific Fine -Tuning for Freezing of Gait Detection with Classical Machine Learning Models
Clebson Ismael Santos Silva (Universidade Federal do Pará), Ronaldo F. Zampolo, Antônio Pereira Jr (Universidade Federal do Pará)
Résumé : Freezing of Gait (FoG) is a critical motor symptom in Parkinson’s disease and a prime target for wearable-based detection. While global models trained across subjects offer scalability, they may fail to generalize to individuals with distinct gait signatures. This study investigates the effectiveness of classical machine learning models, explores the impact of temporal window size, and evaluates whether subject-specific fine-tuning improves detection performance. Using data from a dataset with 35 subjects performing a rotation-eliciting task, we show that ensemble models with 4–5 s windows achieve F1-scores up to 0.92. The results of the importance analysis of features highlight the role of signals from the SI-axis derived from a gyroscope. Fine-tuning with 10 windows yields significant classification gains in selected individuals but only marginal average improvement (∆F1 = +0.011), raising questions about its general viability. We argue for adaptive deployment strategies that combine selective personalization and task-aware design to balance accuracy, interpretability, and efficiency in wearable systems.