Sensor-Based Tracking and Big Data Processing of Patient Activities in Ambient Assisted Living

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.