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.
Real-time detection of freezing of gait in parkinson’s disease using multi- head convolutional neural networks and a single inertial sensor,
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
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.