JEPA-based self-supervised pre-training on inertial sensor data improves recognition of rare transitional human activities compared to supervised learning, with gains mostly on transition classes.
The FORTH-TRACE dataset for human activity recognition of simple activities and postural transitions using a Body Area Network
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Joint-Embedding Predictive Architecture for Sensor-based Activity Recognition
JEPA-based self-supervised pre-training on inertial sensor data improves recognition of rare transitional human activities compared to supervised learning, with gains mostly on transition classes.