A human activity recognition-aware framework using multi-modal sensor data fusion

Recent years, with smartphones and other pervasive devices the paradigm of situation-recognition extended to IoT devices in home. Since IoT devices produce data that can help predict accidents or disasters in private or public environments. In IoT devices provided situation, machine learning technologies can help make an insight to what's really meaningful according to service provider's purpose. So, in order to understand several kinds of user's situation within an environment with IoT devices, a novel human activity recognition scheme is required to manage lots of data for guaranteeing accurate situation in real-time. As the accuracy of analyzing result and the response time of informing a situational corresponding to users are important factors on providing services, we present a human activity recognition-aware framework using multi-modal sensors connected with IoT devices and consumer devices in home.