In-Bed Person Monitoring Using Thermal Infrared Sensors

TL;DR

This study develops a privacy-preserving in-bed monitoring system using infrared sensors and machine learning, achieving high accuracy in detecting bed presence under controlled conditions, with challenges under environmental variations.

Contribution

Introduces a non-invasive infrared sensor-based system for bed presence detection and evaluates machine learning algorithms' effectiveness in diverse conditions.

Findings

SVM and k-NN achieved 99% accuracy in controlled datasets.

Accuracy drops under environmental variations like duvet use or pets.

Infrared sensors offer a privacy-preserving alternative for in-bed monitoring.

Abstract

The world is expecting an aging population and shortage of healthcare professionals. This poses the problem of providing a safe and dignified life for the elderly. Technological solutions involving cameras can contribute to safety, comfort and efficient emergency responses, but they are invasive of privacy. We use 'Griddy', a prototype with a Panasonic Grid-EYE, a low-resolution infrared thermopile array sensor, which offers more privacy. Mounted over a bed, it can determine if the user is on the bed or not without human interaction. For this purpose, two datasets were captured, one (480 images) under constant conditions, and a second one (200 images) under different variations such as use of a duvet, sleeping with a pet, or increased room temperature. We test three machine learning algorithms: Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Neural Network (NN). With 10-fold cross validation, the highest accuracy in the main dataset is for both SVM and k-NN (99%). The results with variable data show a lower reliability under certain circumstances, highlighting the need of extra work to meet the challenge of variations in the environment.