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.
One cue does not fit all: a systematic review with meta-analysis of the effectiveness of cueing on freezing of gait in parkinson’s disease
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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.