Year: 2009
Type: conference-paper
Abstract: : As is known for centuries, humans exhibit an electrical profile. This profile is altered through various phys-iological processes, which can be measured through biosignals; e.g., electromyography (EMG) and electro-dermal activity (EDA). These biosignals can reveal our emotions and, as such, can serve as an advanced man-machine interface (MMI) for empathic consumer products. However, such an MMI requires the correct classification of biosignals to emotion classes. This paper explores the use of EDA and three facial EMG signals to determine neutral, positive, negative, and mixed emotions, using recordings of 24 people. A range of techniques is tested, which resulted in a generic framework for automated emotion classification with up to 61.31 % correct classification of the four emotion classes, without the need of personal profiles. Among vari-ous other directives for future research, the results emphasize the need for both personalized biosignal-profiles and the recording of multiple biosignals in parallel. That men are machines (whatever else they may be) has long been suspected; but not till our generation have men fairly felt in concrete just what wonderful psycho-neuro-physical mechanisms they are. William James (1842 – 1910) 1
Language: English
Topic: Emotion and Mood Recognition
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2129758587