In-Bed Person Monitoring Using Thermal Infrared Sensors
- URL: http://arxiv.org/abs/2107.07986v1
- Date: Fri, 16 Jul 2021 15:59:07 GMT
- Title: In-Bed Person Monitoring Using Thermal Infrared Sensors
- Authors: Elias Josse, Amanda Nerborg, Kevin Hernandez-Diaz, Fernando
Alonso-Fernandez
- Abstract summary: We use 'Griddy', a prototype with a Panasonic Grid-EYE, a low-resolution infrared thermopile array sensor, which offers more privacy.
For this purpose, two datasets were captured, one (480 images) under constant conditions, and a second one (200 images) under different variations.
We test three machine learning algorithms: Support Vector Machines (SVM), k-Nearest Neighbors (k-NN) and Neural Network (NN)
- Score: 53.561797148529664
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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