Sensor-Based Tracking and Big Data Processing of Patient Activities in Ambient Assisted Living
Dino Nienhold, Rolf Dornberger, Safak Korkut · 2016
In ambient assisted living, the home care treatment of elderly people is faced with the unreliability of correctly identifying all activities over the entire day. As the self-assessment is quite weak, the sensor-based tracking is researched intensively. In this research, we propose a minimal set of low cost and non-intrusive sensors in order to identify a set of prescribed patient activities. Prototypes of sensor systems are built and tested in real-life cases. The retrieved big data about patient activities are stored and processed comparing different methods for data analytics. Finally, sensor systems, although using low-cost sensors but providing high accuracy and reliability, are proposed and discussed to be used in home care treatment.