Using Unsupervised Anomaly Detection to Analyze Physiological Signals for Emotion Recognition

An increase in the collection of physiological signals, whether done implicitly in wearable or IoT device or explicitly in experimental and laboratory environments, creates the need for development of smart systems and tools capable of data analysis with limited expert knowledge. Anomaly detection, specifically unsupervised anomaly detection can be used to design a tool or even as a tool to help remedy this issue. This paper will focus on how unsupervised anomaly detection can be utilized for the development of such systems. A systematic, robust, and customizable approach will be presented and preliminary results will be shown that open the door for future research and algorithm development.