Multimodal Physical Fitness Monitoring (PFM) Framework Based on TimeMAE-PFM in Wearable Scenarios

TL;DR

This paper introduces a multimodal physical fitness monitoring framework utilizing an improved TimeMAE model with attention mechanisms, enabling real-time, personalized health assessment from wearable sensor data, validated on NHATS dataset.

Contribution

It presents a novel multimodal PFM framework based on an enhanced TimeMAE that effectively compresses and analyzes wearable sensor time-series data for health monitoring.

Findings

Achieved 70.6% accuracy in physical health monitoring

Attained 82.20% AUC in classification tasks

Outperformed existing time-series models

Abstract

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.