Abstract
Human facial expressions are a complex capacity, carrying important psychological and neurological information. Facial expressions typically involve the co-activation of several muscles; they vary between individuals, between voluntary versus spontaneous expressions, and depend strongly on personal interpretation. Accordingly, while high-resolution recording of muscle activation in a non-laboratory setting offers exciting opportunities, it remains a major challenge. This paper describes a wearable and non-invasive method for objective mapping of facial muscle activation and demonstrates its application in a natural setting. We focus on muscle activation associated with “enjoyment”, “social” and “masked” smiles; three categories with distinct social meanings. We use an innovative, dry, soft electrode array designed specifically for facial surface electromyography recording, a customized independent component analysis algorithm, and a short training procedure to achieve the desired mapping. First, identification of the orbicularis oculi and the levator labii superioris was demonstrated from voluntary expressions. Second, the zygomaticus major was identified from voluntary and spontaneous Duchenne and non-Duchenne smiles. Finally, using a wireless device in an unmodified work environment revealed expressions of diverse emotions in face-to-face interaction. Our high-resolution and crosstalk-free mapping, along with excellent user-convenience, opens new opportunities in gaming, virtual-reality, bio-feedback and objective psychological and neurological assessment.
Highlights
The emotional-state of the tested subject has proved difficult to achieve
Two electrode arrays were connected to two amplifier units, (Intan Technologies amplifier evaluation board, RHD2000) using a costume-made printed circuit board (PCB) connector
We describe a high resolution, non-invasive Surface electromyography (sEMG) method for objective mapping of facial muscle activation in both a lab and natural environment
Summary
The emotional-state of the tested subject has proved difficult to achieve. While visual analysis has gained huge acceptance in recent years[7,9], and has been enriched by automated facial analysis methods[15], it does not capture fundamental muscle activation. The main strength of facial sEMG is in its potential ability to provide precise physiological information by identifying specific muscle activation, while negating the need for a constant visual path to the subject’s face. SEMG usefulness depends on the ability to overcome crosstalk and to achieve high resolution and specificity[17,18], along with subject comfort. High-resolution facial sEMG relied on gelled electrodes and lengthy placement procedures, and was restricted to artificial laboratory settings. We demonstrate a powerful new facial sEMG system and show its unique performances in capturing three muscles involved in different smiles. An application of the technology in a completely natural work environment demonstrated the capacity to identify the activation of the orbicularis oculi, the zygomaticus major, and the levator labii superioris muscles in face-to-face interactions
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