Pattern Recognition for Automated Healthcare Assessment Using Non-invasive, Ambient Sensors

In this paper, a solution for an automated healthcare assessment process is proposed. Non-invasive, ambient sensors are retrieving data from patients being in their home care treatment setups. The type of sensors is limited to the tracking of inertia, motion, and alcohol gas. Low-cost sensor prototypes are developed. They constantly measure the movement and the air around the patients. The Big Data generated in this way is used to retrieve patterns of activities. Different pattern recognition algorithms are tested and compared. The highest accuracy and reliability in assessing the data are support vector machines and feedforward neural networks with a performance of 90 % probability in identifying the correct patients' activities over the test period. In this paper, the setup of the sensor prototypes, the data handling, and the data analytics are discussed.