  Physical function monitoring (PFM) plays a crucial role in healthcare
especially for the elderly. Traditional assessment methods such as the Short
Physical Performance Battery (SPPB) have failed to capture the full dynamic
characteristics of physical function. Wearable sensors such as smart wristbands
offer a promising solution to this issue. However, challenges exist, such as
the computational complexity of machine learning methods and inadequate
information capture. This paper proposes a multi-modal PFM framework based on
an improved TimeMAE, which compresses time-series data into a low-dimensional
latent space and integrates a self-enhanced attention module. This framework
achieves effective monitoring of physical health, providing a solution for
real-time and personalized assessment. The method is validated using the NHATS
dataset, and the results demonstrate an accuracy of 70.6% and an AUC of 82.20%,
surpassing other state-of-the-art time-series classification models.
