Variable Input Masked Autoencoders for Wrist-Worn Accelerometry
VarMAE is a variable input masked autoencoder designed to learn a single representation for wrist-worn accelerometry regardless of sampling rate or window duration. It achieves this by creating two views of sensor data and using a cross-resolution reconstruction objective, allowing one foundation model to effectively handle diverse sensing configurations.