  Distal facial Electromyography (EMG) can be used to detect smiles and frowns
with reasonable accuracy. It capitalizes on volume conduction to detect
relevant muscle activity, even when the electrodes are not placed directly on
the source muscle. The main advantage of this method is to prevent occlusion
and obstruction of the facial expression production, whilst allowing EMG
measurements. However, measuring EMG distally entails that the exact source of
the facial movement is unknown. We propose a novel method to estimate specific
Facial Action Units (AUs) from distal facial EMG and Computer Vision (CV). This
method is based on Independent Component Analysis (ICA), Non-Negative Matrix
Factorization (NNMF), and sorting of the resulting components to determine
which is the most likely to correspond to each CV-labeled action unit (AU).
Performance on the detection of AU06 (Orbicularis Oculi) and AU12 (Zygomaticus
Major) was estimated by calculating the agreement with Human Coders. The
results of our proposed algorithm showed an accuracy of 81% and a Cohen's Kappa
of 0.49 for AU6; and accuracy of 82% and a Cohen's Kappa of 0.53 for AU12. This
demonstrates the potential of distal EMG to detect individual facial movements.
Using this multimodal method, several AU synergies were identified. We
quantified the co-occurrence and timing of AU6 and AU12 in posed and
spontaneous smiles using the human-coded labels, and for comparison, using the
continuous CV-labels. The co-occurrence analysis was also performed on the
EMG-based labels to uncover the relationship between muscle synergies and the
kinematics of visible facial movement.
