LIFT-PD combines self-supervised pre-training with differential hopping windowing and opportunistic activation to detect FoG using 40% less labeled data and 67% less inference time than continuous supervised models.
Towards real-time prediction of freezing of gait in patients with parkinson’s disease: a novel deep one-class classifier
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Self-Supervised Learning and Opportunistic Inference for Continuous Monitoring of Freezing of Gait in Parkinson's Disease
LIFT-PD combines self-supervised pre-training with differential hopping windowing and opportunistic activation to detect FoG using 40% less labeled data and 67% less inference time than continuous supervised models.