A Framework for Real Time Emotion Recognition Based on Human ANS Using Pervasive Device

The concept of connected things by involving emotional aspects has been raised as a new research issue which is known as "emotional IoT". The deeper interaction between object and human shows an importance to develop a system with either cognitive or affective capabilities such as emotion. While the existing works on real time emotion recognition mostly rely on facial data, there are a few works dealing with real time emotion recognition based on physiological data using pervasive devices. In this work, we propose a framework to recognize emotion based on human physiological signals using the pervasive wearable device. This framework opposed most of the works which employed sensors which are expensive and complex in arrangement. The challenge on using pervasive devices is the low accuracy due to the low sampling rate. The approach is implemented in an end-to-end soft real time emotion recognition system using smartphone and smartwatch devices. The performance of our system was evaluated under a common environment and proved the system applicability throughout everyday life.