Sleep Position Classification using Transfer Learning for Bed-based Pressure Sensors
Journal:
arXiv
Published Date:
May 12, 2025
Abstract
Bed-based pressure-sensitive mats (PSMs) offer a non-intrusive way of
monitoring patients during sleep. We focus on four-way sleep position
classification using data collected from a PSM placed under a mattress in a
sleep clinic. Sleep positions can affect sleep quality and the prevalence of
sleep disorders, such as apnea. Measurements were performed on patients with
suspected sleep disorders referred for assessments at a sleep clinic. Training
deep learning models can be challenging in clinical settings due to the need
for large amounts of labeled data. To overcome the shortage of labeled training
data, we utilize transfer learning to adapt pre-trained deep learning models to
accurately estimate sleep positions from a low-resolution PSM dataset collected
in a polysomnography sleep lab. Our approach leverages Vision Transformer
models pre-trained on ImageNet using masked autoencoding (ViTMAE) and a
pre-trained model for human pose estimation (ViTPose). These approaches
outperform previous work from PSM-based sleep pose classification using deep
learning (TCN) as well as traditional machine learning models (SVM, XGBoost,
Random Forest) that use engineered features. We evaluate the performance of
sleep position classification from 112 nights of patient recordings and
validate it on a higher resolution 13-patient dataset. Despite the challenges
of differentiating between sleep positions from low-resolution PSM data, our
approach shows promise for real-world deployment in clinical settings
