Abstract
A growing number of advanced smart systems and solutions are being designed for the elderly, helping them to live longer at home. These systems need to provide unobtrusive monitoring and safety for their users and information for the healthcare professionals and family members. Multi-modal sensor data enables the possibility for in-depth behavioral analysis. To gather multi-modal data, we propose an IoT-based smart ambient behavior observation system (SABOS). SABOS provides unobtrusive monitoring of daily living activities by utilizing various sensors integrated into the residential house. To reduce the amount of data, we present a data reduction algorithm. The data reduction algorithm effectively reduces over 90% of the submitted data with full recovery in the cloud. Data is sent to ThingSpeak for MATLAB visualization and analysis to generate graphical illustrations of daily living activities. In an emergency, an "if this then that" (IFTTT) service combined with ThingSpeak triggers an applet to send a defined message to a healthcare professional or a family member.