In-Bed Person Monitoring Using Thermal Infrared Sensors
This addresses the problem of elderly care safety and privacy for healthcare systems, though it is incremental as it builds on existing sensor and ML methods.
The paper tackled in-bed person monitoring using a low-resolution thermal infrared sensor to preserve privacy, achieving 99% accuracy with SVM and k-NN under constant conditions but showing lower reliability with environmental variations like duvets or pets.
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.