Clustering-Based Emotion Recognition Micro-Service Cloud Framework for Mobile Computing

In a situation where life becomes more stressful and challenging, people feel compelled to be more concerned about their mental situation. Different emotional statuses are external reactions to different mental states. Therefore, researchers always identify people's mental situation by monitoring their real-time emotions. At the same time, due to the availability of built-in sensors in a smartphone, applications that can identify real-time emotions of mobile users are constantly emerging. However, compared to most emotion recognition algorithms, computing resources and battery life in mobile phones are always limited. This makes accuracy and latency of these applications are unsatisfactory. In this paper, we propose a micro-service platform for mobile emotion recognition application developers (MSPMERAD) which can supply high performance. First, a classifier fusion emotion recognition algorithm is proposed by using a dynamic adaptive fusion strategy. Second, this new algorithm is encapsulated into a micro-service. With other affiliated micro-services such as data uploading, preprocessing, etc., developers can ignore the implementation of the emotion recognition algorithm and just focus on how to collect sensor data and interact with users. The accuracy and latency of one application based on the MSPMERAD are compared with another application that is implemented using a locale emotion recognition algorithm. Experiments based on the daily behavior data of 50 student volunteers show that the application based on our platform has higher recognition accuracy with a more reasonable time.